A method for evaluating productivity of a compact gas reservoir horizontal well

Abstract

The present disclosure provides a method for evaluating the productivity of a horizontal well in a tight gas reservoir, and relates to the technical field of tight gas reservoirs. The method comprises: obtaining logging sensitive parameters and rock mechanics parameters of the horizontal well in the tight gas reservoir; calculating a comprehensive index corresponding to different reservoirs in the horizontal well based on the logging sensitive parameters and a comprehensive index model; determining the geological sweet spot level and the engineering sweet spot level corresponding to the reservoir based on the comprehensive index, the logging sensitive parameters and the rock mechanics parameters; and evaluating the productivity of the horizontal well based on the geological sweet spot level and the engineering sweet spot level corresponding to the reservoir. In this way, the comprehensive index and the logging sensitive parameters determined by the logging sensitive parameters which are greatly affected by lithology can accurately identify the geological sweet spot based on different data dimensions, and the geological sweet spot and the engineering sweet spot are evaluated in coordination, and the reservoir capacity and the reconstruction capacity of the reservoir productivity are considered, thereby significantly improving the accuracy of the productivity prediction.

Description

Technical Field

[0001] This disclosure relates to the field of tight gas reservoir technology, and in particular to a method for evaluating the productivity of horizontal wells in tight gas reservoirs. Background Technology

[0002] Tight sandstone gas reservoirs, as important unconventional natural gas resources in my country, are characterized by poor reservoir properties, strong heterogeneity, and low natural production capacity. Currently, horizontal well technology can effectively improve the controlled reserves and initial production of a single well during development, but it generally suffers from problems such as a short stable production period and rapid production decline.

[0003] Among related technologies, traditional capacity evaluation methods still have shortcomings. For example, they rely too much on fracturing process optimization technology, and only improve short-term output by modifying scale. They are highly empirical and have poor reliability. Summary of the Invention

[0004] This disclosure provides a method for evaluating the productivity of horizontal wells in tight gas reservoirs. By synergistically evaluating geological sweet spots and engineering sweet spots, it is possible to improve the accuracy of productivity prediction and optimize the allocation of fracturing resources.

[0005] According to a first aspect of the present disclosure, a method for evaluating the productivity of a horizontal well in a tight gas reservoir is provided, comprising:

[0006] To obtain logging-sensitive parameters and rock mechanics parameters of horizontal wells in tight gas reservoirs;

[0007] Based on the logging sensitive parameters and comprehensive index model, the comprehensive index corresponding to different reservoirs in horizontal wells is calculated. The comprehensive index is used to characterize the degree of good or bad of the comprehensive geological conditions of the reservoir.

[0008] Based on the comprehensive index, the well logging sensitive parameters, and the rock mechanics parameters, the geological sweet spot level and engineering sweet spot level of the reservoir are determined;

[0009] Based on the geological sweet spot level and engineering sweet spot level corresponding to the reservoir, the productivity of the horizontal well is evaluated.

[0010] In some possible implementations, the logging sensitive parameters include sonic transit time curve, minimum sonic transit time curve, clay content, deep lateral logging resistivity, total hydrocarbon value in gas logging, and total hydrocarbon base value in gas logging.

[0011] The formula for calculating the composite index model is as follows:

[0012] ;

[0013] Where ZHZS represents the comprehensive index; DT represents the acoustic time difference curve; Vsh represents the minimum value of the sonic transit time curve; RLLD represents the deep lateral logging resistivity; QT represents the total hydrocarbon value from gas logging. This indicates the total hydrocarbon base value measured in gas.

[0014] In some possible implementations, the geological sweet spot level and engineering sweet spot level of the reservoir are determined based on the comprehensive index, the well logging sensitive parameters, and the rock mechanical parameters, including:

[0015] Based on the comprehensive index, the well logging sensitive parameters, and the pre-set geological classification evaluation criteria, the geological sweet spot level corresponding to the reservoir is determined;

[0016] Based on the rock mechanics parameters and the pre-set engineering grading evaluation criteria, the engineering sweet spot level corresponding to the reservoir is determined.

[0017] In some possible implementations, the geological sweet spot level corresponding to the reservoir is determined based on the comprehensive index, the well logging sensitive parameters, and a pre-set geological classification evaluation standard, including:

[0018] Based on the aforementioned logging sensitive parameters, the acoustic transit time, porosity, and permeability of the reservoir are determined;

[0019] Based on the established geological grading evaluation criteria, the first level corresponding to the comprehensive index, the sonic transit time, the porosity, and the permeability are determined, as well as the first weight corresponding to the comprehensive index, the sonic transit time, the porosity, and the permeability.

[0020] The geological sweet spot level of the reservoir is determined based on the first level corresponding to the comprehensive index, the acoustic transit time, the porosity, and the permeability, respectively, and the first weight corresponding to the comprehensive index, the acoustic transit time, the porosity, and the permeability, respectively.

[0021] In some possible implementations, the rock mechanical parameters include minimum principal stress, Young's modulus, Poisson's ratio, and compressive strength; based on the rock mechanical parameters and pre-set engineering grading evaluation criteria, the engineering sweet spot level corresponding to the reservoir is determined, including:

[0022] The brittleness index is determined based on the Young's modulus and the Poisson's ratio.

[0023] Based on the established engineering classification evaluation criteria, the second level corresponding to the minimum principal stress, Young's modulus, brittleness index and compressive strength are determined, as well as the second weight corresponding to the minimum principal stress, Young's modulus, brittleness index and compressive strength.

[0024] The engineering sweet spot level of the reservoir is determined based on the second level corresponding to the minimum principal stress, Young's modulus, brittleness index, and compressive strength, respectively, and the second weight corresponding to the minimum principal stress, Young's modulus, brittleness index, and compressive strength, respectively.

[0025] In some possible implementations, the weights of each parameter in the geological classification evaluation criteria and / or the engineering classification evaluation criteria are obtained in advance using the analytic hierarchy process.

[0026] In some possible implementations, the geological grading evaluation criteria include:

[0027] In the first geological level, the value of the comprehensive index is greater than 30, the value of the sonic transit time is greater than 225 μs / m, the value of the porosity is greater than 9%, and the value of the permeability is greater than 0.5 mD.

[0028] In the second geological level, the comprehensive index value is between 20 and 30, the sonic transit time value is between 218 and 225 μs / m, the porosity value is between 7.5 and 9%, and the permeability value is between 0.25 and 0.5 mD.

[0029] In the third geological level, the comprehensive index value is between 10 and 20, the acoustic transit time value is between 208 and 218 μs / m, the porosity value is between 6 and 7.5%, and the permeability value is between 0.1 and 0.25 mD.

[0030] In some possible implementations, the engineering grading evaluation criteria include:

[0031] In the first engineering level, the minimum principal stress is less than 37 MPa, the Young's modulus is less than 28 GPa, the brittleness index is greater than 55%, and the compressive strength is less than 70 MPa.

[0032] In the second engineering level, the minimum principal stress is between 37-39 MPa, the Young's modulus is between 28-30 GPa, the brittleness index is between 45-55%, and the compressive strength is between 70-110 MPa.

[0033] In the third engineering level, the minimum principal stress is greater than 39 MPa, the Young's modulus is greater than 30 GPa, the brittleness index is less than 45%, and the compressive strength is greater than 110 MPa.

[0034] In some possible implementations, before obtaining logging-sensitive parameters of a horizontal well in a tight gas reservoir, the method further includes:

[0035] Obtain logging curves for vertical wells and horizontal wells;

[0036] Determine the mapping relationship between the parameters in the vertical well logging curve and the horizontal well logging curve;

[0037] By using the vertical well logging curve corresponding to the vertical well gas test results and the mapping relationship, the logging sensitive parameters in the horizontal well logging curve are determined.

[0038] In some possible implementations, the logging-sensitive parameters in the horizontal well logging curve are determined by using the vertical well test results corresponding to the vertical well logging curve and the mapping relationship, including:

[0039] Based on the vertical well logging curves and the corresponding vertical well gas test results and the mapping relationship, a first interpretation chart for water saturation and porosity of horizontal wells, and a second interpretation chart for clay content and compensation density of horizontal wells are established.

[0040] Sensitivity analysis is performed based on the first and second interpretation charts to determine the logging sensitivity parameters in the horizontal well logging curves.

[0041] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0042] This disclosure obtains well logging sensitive parameters and rock mechanics parameters of horizontal wells in tight gas reservoirs; based on the well logging sensitive parameters and a comprehensive index model, it calculates a comprehensive index corresponding to different reservoirs in the horizontal well, whereby the comprehensive index characterizes the degree of superiority or inferiority of the comprehensive geological conditions of the reservoir; based on the comprehensive index, well logging sensitive parameters, and rock mechanics parameters, it determines the geological sweet spot level and engineering sweet spot level of the reservoir; and based on the geological sweet spot level and engineering sweet spot level of the reservoir, it evaluates the productivity of the horizontal well. In this way, by using the comprehensive index and well logging sensitive parameters determined by well logging sensitive parameters that have a significant impact on lithology, geological sweet spots can be accurately identified based on different data dimensions, and a synergistic evaluation can be performed using geological sweet spots and engineering sweet spots, while simultaneously considering the reservoir's storage capacity and stimulation capacity, significantly improving the accuracy of productivity prediction.

[0043] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0044] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0045] Figure 1 This is a flowchart illustrating a method for evaluating the productivity of a horizontal well in a tight gas reservoir, according to an exemplary embodiment.

[0046] Figure 2 This is a schematic diagram illustrating the mapping relationship between logging curves of vertical wells and logging curves of horizontal wells, according to an exemplary embodiment.

[0047] Figure 3 This is a schematic diagram of a first explanatory plate showing the water saturation and porosity of a horizontal well according to an exemplary embodiment.

[0048] Figure 4 This is a schematic diagram of a second explanatory plate showing the relationship between the clay content and the compensation density of a horizontal well according to an exemplary embodiment.

[0049] Figure 5 This is a flowchart illustrating a method for increasing production in a horizontal well of a tight gas reservoir according to an exemplary embodiment. Detailed Implementation

[0050] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0051] Among related technologies, tight sandstone gas reservoirs are widely developed unconventional natural gas reservoirs in my country, with abundant resources and huge development potential. However, they are characterized by complex development technologies and high development costs. Tight gas reservoirs refer to natural gas reservoirs with extremely low permeability, typically less than 0.1 mD. Gas flow is extremely difficult in these reservoirs, requiring specialized technologies such as horizontal wells and fracturing for economical and efficient extraction.

[0052] Horizontal wells are a drilling technique that involves drilling vertically to a certain depth, then bending the wellbore to extend horizontally over a long distance, such as hundreds or thousands of meters, within the target reservoir. This greatly increases the contact area between the wellbore and the reservoir, making it a key technology for developing tight gas reservoirs.

[0053] Currently, the development of tight sandstone gas reservoirs has gradually adopted horizontal well-scale development to increase the dynamic reserves and wellhead production of individual wells. Production capacity assessment is crucial for evaluating the predicted natural gas production that horizontal wells can generate after fracturing and is essential for development decisions.

[0054] In the development model of horizontal wells in tight gas reservoirs, the evaluation of gas well production potential can be used to formulate fracturing process optimization technologies and achieve large-scale fracturing stimulation. Then, the gas well's production dynamic curve is used to qualitatively analyze its stable production capacity. Based on the changes in production during the gas well production process, measures such as bubble drainage and gas lift are implemented to improve and stabilize production. However, this production improvement method does not consider the basic conditions of individual wells, such as geology and fracturing, and is highly empirical with poor reliability, resulting in significant differences in production among individual wells within the same well group.

[0055] Analysis revealed three main shortcomings in traditional capacity assessment methods: First, they rely excessively on fracturing process optimization, focusing solely on scaling up operations to increase short-term production without a systematic assessment of reservoir geological conditions. Second, when using production dynamic curves for qualitative analysis, they fail to establish the intrinsic correlation between geological and engineering parameters, leading to significant discrepancies between assessment results and actual capacity. Third, existing technologies often employ single-discipline evaluation models, using only geological or engineering sweet spots for capacity prediction, failing to achieve a synergistic evaluation of reservoir compressibility and gas content.

[0056] In other words, single-dimensional evaluation indicators cannot accurately reflect the comprehensive characteristics of reservoirs. This fragmented evaluation method leads to significant differences in production per well within the same well group, with some high-quality reservoirs failing to fully realize their production potential, while inefficient well locations consume a large amount of fracturing resources. Especially in development areas with long horizontal well sections and strong reservoir heterogeneity, existing methods struggle to accurately identify both geological and engineering sweet spots, severely restricting the efficient development of tight gas reservoirs. Therefore, a dual evaluation system incorporating both logging-sensitive parameters and rock mechanics parameters should be established.

[0057] Based on this, we propose to couple well logging sensitive parameters with rock mechanics parameters to construct a collaborative evaluation model for geological sweet spots and engineering sweet spots, and improve the reliability of the evaluation through multi-source data fusion.

[0058] Reference Figure 1 , Figure 1 This is a flowchart illustrating a method for evaluating the productivity of a horizontal well in a tight gas reservoir according to an exemplary embodiment, such as... Figure 1 As shown, a method for evaluating the productivity of a horizontal well in a tight gas reservoir includes the following steps.

[0059] In step S101, the logging sensitive parameters and rock mechanics parameters of the horizontal well in the tight gas reservoir are obtained;

[0060] In step S102, based on the logging sensitive parameters and the comprehensive index model, the comprehensive index corresponding to different reservoirs in the horizontal well is calculated. The comprehensive index is used to characterize the degree of superiority or inferiority of the comprehensive geological conditions of the reservoir.

[0061] In step S103, the geological sweet spot level and engineering sweet spot level of the reservoir are determined based on the comprehensive index, the logging sensitive parameters and the rock mechanics parameters.

[0062] In step S104, the productivity of the horizontal well is evaluated based on the geological sweet spot level and engineering sweet spot level corresponding to the reservoir.

[0063] For example, logging sensitive parameters refer to logging curve data that are sensitive to the gas content of a reservoir and can be used to describe the properties of the reservoir rock itself. These logging sensitive parameters may include sonic transit time curves, clay content, deep lateral resistivity, total hydrocarbon value, and total hydrocarbon base value, etc. These parameters are strongly correlated with the ability of tight gas reservoirs to store and release gas. Specifically, the sonic transit time curve is the time difference of sound wave propagation in the formation, which can be used to reflect rock porosity; clay content is the proportion of clay minerals in the formation, affecting reservoir permeability; deep lateral resistivity can be used to characterize formation fluid properties, with gas-bearing layers having high resistivity; and the total hydrocarbon value is the total amount of hydrocarbon gas in the drilling fluid, indicating gas abundance.

[0064] For example, rock mechanics parameters can be used as engineering parameters to characterize the ease of rock fracturing. Rock mechanics parameters are crucial for the development of tight gas reservoirs, especially for setting parameters in fracturing technology. Rock mechanics parameters can include Young's modulus, Poisson's ratio, compressive strength / tensile strength, in-situ stress, brittleness index, etc. Among them, Young's modulus, also known as the elastic modulus, reflects the rock's ability to resist deformation; Poisson's ratio reflects the ratio of lateral to longitudinal deformation of the rock under stress; compressive strength / tensile strength reflects the rock's ability to resist failure; in-situ stress can be any of the minimum horizontal principal stress, maximum horizontal principal stress, or vertical stress, and can be used to control the morphology and direction of fracturing; the brittleness index directly reflects the degree to which the rock is prone to brittle fracture under stress, and can be calculated based on Poisson's ratio; a higher value indicates higher relative brittleness.

[0065] For example, a comprehensive index model is a mathematical model or machine learning algorithm used to integrate and calculate a comprehensive index with overall representativeness based on multiple logging sensitive parameters. The comprehensive index can be used to quantify the combined impact of multiple key geological logging sensitive parameters, and can be used to characterize the degree of superiority or inferiority of the comprehensive geological conditions of the reservoir, aiming to more comprehensively characterize the natural gas storage capacity and flow potential of a certain layer or well section of the reservoir.

[0066] For example, a geological sweet spot refers to a region or section within a reservoir with the most favorable geological conditions, most conducive to natural gas enrichment and flow. The geological sweet spot level corresponding to the reservoir can be determined in advance based on a comprehensive index and logging sensitivity parameters. Geological sweet spots can be identified through their geological sweet spot level. For example, high porosity and high permeability provide ample storage space and good seepage channels; high gas saturation indicates a high proportion of natural gas in the pore space; and low clay content reduces pore blockage and the influence of bound water.

[0067] For example, an engineering sweet spot refers to a region or section within a reservoir where the rock mechanical properties and geostress conditions are most favorable for hydraulic fracturing and the formation of a complex and effective fracture network. The engineering sweet spot level corresponding to the reservoir can be determined based on rock mechanical parameters. Engineering sweet spots can be identified through their engineering sweet spot level. For example, a high brittleness index indicates that the rock is prone to fracturing into a complex network during fracturing, rather than forming only a single planar fracture; a low minimum horizontal principal stress indicates that the fracture opening pressure is low, making it easier to open; a low stress difference, i.e., the difference between the maximum and minimum horizontal principal stresses, can facilitate fracture propagation in multiple directions, forming a complex fracture network; and an appropriate Young's modulus, where too low a modulus cannot support the fracture, while too high a modulus will limit the fracture width.

[0068] For example, a combined sweet spot is a region or section that simultaneously possesses geological sweet spot conditions and engineering sweet spot conditions. It is a core target area for horizontal well deployment, staged fracturing design, and achieving high production. By identifying geological sweet spots and engineering sweet spots, the natural gas production capacity of different sections of the horizontal well can be predicted or assessed.

[0069] For example, well logging data can be pre-analyzed to identify sensitive logging parameters that significantly impact productivity, such as sonic transit time, clay content, and resistivity. These sensitive parameters are then input into a pre-defined comprehensive index model for calculation, outputting a continuous comprehensive index curve or numerical sequence. A higher comprehensive index value indicates greater geological sweet spot potential. Based on the calculated comprehensive index value and the sensitive logging parameters, and using methods such as set thresholds or clustering, the reservoirs traversed by the horizontal well trajectory are classified into different geological sweet spot levels.

[0070] For example, by combining well logging data with rock physics models and / or core experimental data, key rock mechanics parameters such as Poisson's ratio, maximum or minimum horizontal principal stress, and Young's modulus can be obtained. Based on these rock mechanics parameters, thresholds or rules can be set to classify the reservoirs traversed by the horizontal well trajectory into different engineering sweet spots, such as easily fractured, moderately fractured, and difficult to fracture.

[0071] For example, the geological sweet spot level and engineering sweet spot level of each well section are combined for analysis. The reservoir productivity assessment results may depend on the degree of matching between the two.

[0072] For example, a combination with both high geological sweetness and high engineering sweetness is the optimal combination, resulting in strong reservoir storage capacity and easy formation of effective fracture networks to connect the reservoir, thus leading to the highest expected production capacity.

[0073] For example, a geological sweet spot may be high, but an engineering sweet spot may be low; a reservoir may be good, but it may be difficult to fracturing, and production capacity may be limited. In such cases, it is necessary to optimize the fracturing design, such as increasing the scale or changing the fracturing process.

[0074] For example, a geological sweet spot may be of low quality but an engineering sweet spot may be of high quality; a reservoir may be poor but easily fractured. Even if it is fractured, the production capacity may not be high, and it may not be the preferred fracturing target.

[0075] For example, if both the geological sweetness and engineering sweetness are low, it represents the worst parameter combination with the lowest expected production capacity, and fracturing operations should be avoided in this segment.

[0076] For example, based on this combination relationship, the production potential of different sections throughout a horizontal well can be evaluated. By segmenting the horizontal well, it can be identified which sections are comprehensive sweet spots, which are secondary sweet spots, or which are non-sweet spots. Based on the evaluation results, it can be used to guide segmented fracturing design, such as focusing on fracturing in comprehensive sweet spot sections and reducing or eliminating fracturing in non-sweet spot sections.

[0077] Specifically, this method first collects multi-dimensional logging-sensitive data such as sonic transit time and resistivity in horizontal well sections, and then transforms these logging-sensitive parameters into a comprehensive index using a comprehensive index model. Subsequently, the comprehensive index is combined with logging-sensitive parameters such as porosity and permeability for analysis, and reservoir quality is classified according to a pre-set geological grading standard. Simultaneously, engineering indicators such as brittleness index are calculated based on rock mechanics parameters, and the fracturing potential is assessed according to engineering grading standards. Finally, by matching the levels of geological sweet spots and engineering sweet spots, a comprehensive production capacity evaluation conclusion is formed, providing a decision-making basis for the selection of horizontal well target areas and the optimization of fracturing parameters.

[0078] This disclosure obtains well logging sensitive parameters and rock mechanics parameters of horizontal wells in tight gas reservoirs; based on the well logging sensitive parameters and a comprehensive index model, it calculates a comprehensive index corresponding to different reservoirs in the horizontal well, whereby the comprehensive index characterizes the degree of superiority or inferiority of the comprehensive geological conditions of the reservoir; based on the comprehensive index, well logging sensitive parameters, and rock mechanics parameters, it determines the geological sweet spot level and engineering sweet spot level of the reservoir; and based on the geological sweet spot level and engineering sweet spot level of the reservoir, it evaluates the productivity of the horizontal well. In this way, by using the comprehensive index and well logging sensitive parameters determined by well logging sensitive parameters that have a significant impact on lithology, geological sweet spots can be accurately identified based on different data dimensions, and a synergistic evaluation can be performed using geological sweet spots and engineering sweet spots, while simultaneously considering the reservoir's storage capacity and stimulation capacity, significantly improving the accuracy of productivity prediction.

[0079] Furthermore, based on accurate production capacity forecasts, more targeted and robust transformation scales and / or fracturing processes can be designed to maximize production capacity, significantly increase the output of horizontal wells, and enhance the economic benefits of tight gas field development.

[0080] In some possible implementations, the logging sensitive parameters include sonic transit time curve, minimum sonic transit time curve, clay content, deep lateral logging resistivity, total hydrocarbon value in gas logging, and total hydrocarbon base value in gas logging.

[0081] The formula for calculating the composite index model is as follows:

[0082] ;

[0083] Where ZHZS represents the comprehensive index; DT represents the acoustic time difference curve; Vsh represents the minimum value of the sonic transit time curve; RLLD represents the deep lateral logging resistivity; QT represents the total hydrocarbon value from gas logging. This indicates the total hydrocarbon base value measured in gas.

[0084] For example, the sonic transit time curve refers to the distance that a sonic wave travels in the formation per unit distance, obtained through sonic logging. The unit is usually μs / ft or μs / m. Specifically, it can be continuously measured downhole using a sonic logging tool. It is a key parameter for calculating porosity and identifying lithology, and can be used to reflect the degree of reservoir porosity development. The greater the porosity, the slower the sonic wave propagation speed and the larger the transit time value.

[0085] For example, the minimum value of the sonic transit time curve refers to the lowest measured value of sonic transit time within the target formation, which can be determined through screening of well logging curve data and serves as a benchmark reference for reservoir tightness. DT / DT min The ratio DT is greater than 1. A larger DT indicates better porosity. min The smaller the value, the denser the data point. DT / DT min The larger the ratio, the better it indicates relative porosity.

[0086] For example, clay content refers to the volume percentage of clay minerals and fine silt in a rock. It can be calculated using logging methods such as gamma-ray (GR) and neutron-density cross-plotting, and is used to characterize reservoir heterogeneity. Clay content is an unfavorable component in the reservoir; it can clog pore throats, reduce effective porosity and permeability, and increase bound water saturation, thereby reducing the reservoir's gas content and permeability.

[0087] For example, deep lateral logging resistivity is used to reflect the resistivity of the original formation. Deep lateral logging resistivity, measured using a deep resistivity logging tool, is a core parameter for identifying gas-bearing reservoirs and calculating gas saturation. In gas-bearing reservoirs, resistivity is primarily influenced by gas saturation and formation water salinity. High resistivity generally indicates high gas saturation because the conductivity of oil and gas is much lower than that of formation water.

[0088] For example, the total hydrocarbon value in gas logging refers to the concentration of total hydrocarbon gases obtained after degassing and detection of gases carried to the surface through drilling fluid circulation during drilling. Examples of total hydrocarbon gases include methane, ethane, and propane. The total hydrocarbon value is a key parameter in logging-while-drilling, directly reflecting the total amount of hydrocarbon gases released into the drilling fluid from the encountered formation. A higher total hydrocarbon value indicates a better gas-bearing layer or more developed fractures / pores. Specifically, it can be obtained in real-time using a chromatographic analyzer, reflecting the gas abundance of the reservoir.

[0089] For example, the total hydrocarbon value measured in gas logging is a relatively stable background value of total hydrocarbons measured above the target formation or in a non-producing formation. It represents the hydrocarbon gas concentration level caused by non-reservoir factors during drilling and can be used to eliminate background gas interference and highlight the true gas anomalies in the reservoir. This is achieved by calculating the relative gas logging value, i.e., QT / QT. 基值 This can more accurately reflect the gas abundance of the reservoir.

[0090] For example, the composite index is a dimensionless value calculated using a composite index model. It can be combined with the aforementioned multiple logging-sensitive parameters to quantitatively evaluate the geological sweet spot potential of each point in each section of a horizontal well, comprehensively reflecting the geological quality of the reservoir. For instance, a higher composite index value indicates better geological conditions at that point, such as porosity, gas content, and lower clay content, making it more likely to be a geological sweet spot.

[0091] For example, in the comprehensive index model, any parameter that is too low will significantly lower the final ZHZS value. A geologically sound "sweet spot" needs to simultaneously satisfy a good porosity (i.e., DT / DT). min Larger, lower clay content (i.e., large 1-Vsh), higher gas saturation (i.e., high RLLD), and higher gas abundance (i.e., QT / QT). 基值 big).

[0092] In this way, by integrating multiple logging sensitive parameters into a comprehensive index, the quality of geological conditions along the horizontal well trajectory is significantly reduced, which helps to accurately locate truly high-quality geological sweet spots and overcomes the limitations of relying on a single parameter or qualitative interpretation in the past.

[0093] In some possible implementations, the geological sweet spot level and engineering sweet spot level of the reservoir are determined based on the comprehensive index, the well logging sensitive parameters, and the rock mechanical parameters, including:

[0094] Based on the comprehensive index, the well logging sensitive parameters, and the pre-set geological classification evaluation criteria, the geological sweet spot level corresponding to the reservoir is determined;

[0095] Based on the rock mechanics parameters and the pre-set engineering grading evaluation criteria, the engineering sweet spot level corresponding to the reservoir is determined.

[0096] For example, the geological sweet spot level refers to the result of a graded evaluation of the geological conditions of a reservoir. Typically, different sections of a horizontal well are divided into different levels based on the magnitude of a comprehensive index, the value of individual logging sensitive parameters, and pre-set geological grading evaluation standards.

[0097] For example, the engineering sweet spot level refers to the result of a graded evaluation of the reservoir's construction conditions. Based on rock mechanics parameters, such as brittleness index, minimum horizontal principal stress, stress difference, Young's modulus, Poisson's ratio, etc., and pre-set engineering grading evaluation standards, different sections of horizontal wells are divided into different levels.

[0098] For example, the geological grading criteria and the engineering grading criteria are predefined. The geological grading criteria are rules or thresholds used to map composite indices and / or other relevant logging-sensitive parameters to geological sweet spots for identification. The engineering grading criteria are rules or thresholds used to map rock mechanics parameters to engineering sweet spots.

[0099] For example, geological classification evaluation criteria can be pre-determined based on regional geological knowledge and experience, core analysis, statistical relationships between gas testing data and well logging parameters, or supervised learning and machine learning models trained using known production layer labels. Similarly, engineering classification evaluation criteria can be pre-determined based on rock mechanics experiments and fracturing simulation results, analyzing the correlation between historical fracturing operation data, post-fracturing microseismic monitoring data, production data, and rock mechanics parameters, or based on regional engineering practice experience and expert knowledge.

[0100] In this way, by classifying geological sweet spots and engineering sweet spots, areas with poor reservoir quality can be identified in advance, and areas with high fracturing difficulty and potentially poor results can be warned. This provides a clear and operable basis for optimizing horizontal well trajectories, fracturing segmentation and clustering, differentiated parameter design, and resource optimization, ultimately achieving the goals of increasing single-well production, reducing development costs, and improving economic benefits.

[0101] In some possible implementations, the geological sweet spot level corresponding to the reservoir is determined based on the comprehensive index, the well logging sensitive parameters, and a pre-set geological classification evaluation standard, including:

[0102] Based on the aforementioned logging sensitive parameters, the acoustic transit time, porosity, and permeability of the reservoir are determined;

[0103] Based on the established geological grading evaluation criteria, the first level is determined for the comprehensive index, the acoustic transit time, the porosity, and the permeability, respectively.

[0104] The minimum value among the first levels corresponding to the comprehensive index, the acoustic transit time, the porosity, and the permeability is determined as the geological sweet spot level corresponding to the reservoir.

[0105] In some possible implementations, the geological sweet spot level corresponding to the reservoir is determined based on the comprehensive index, the well logging sensitive parameters, and a pre-set geological classification evaluation standard, including:

[0106] Based on the aforementioned logging sensitive parameters, the acoustic transit time, porosity, and permeability of the reservoir are determined;

[0107] Based on the established geological grading evaluation criteria, the first level corresponding to the comprehensive index, the sonic transit time, the porosity, and the permeability are determined, as well as the first weight corresponding to the comprehensive index, the sonic transit time, the porosity, and the permeability.

[0108] The geological sweet spot level of the reservoir is determined based on the first level corresponding to the comprehensive index, the acoustic transit time, the porosity, and the permeability, respectively, and the first weight corresponding to the comprehensive index, the acoustic transit time, the porosity, and the permeability, respectively.

[0109] For example, sonic transit time is a logging parameter reflecting the elastic properties and porosity of rocks. A high sonic transit time value indicates high porosity, and the sonic transit time value at each point can be obtained through a sonic transit time curve. Porosity is the percentage of pore volume in a rock to its total volume, directly determining the size of the reservoir space. It can be calculated using logging interpretation methods such as neutron-density cross-section and sonic logging. Permeability can be used to characterize the ability of a rock to allow natural gas to pass through, determining the ease or difficulty of natural gas flow. It can be obtained through core experiments, or through logging interpretation models such as statistical relationships or machine learning models established from parameters such as porosity, clay content, and bound water saturation.

[0110] For example, the first level refers to the preliminary grade obtained after each individual parameter is independently evaluated according to its own preset standards. That is, each parameter among the comprehensive index, sonic transit time, porosity, and permeability has its own corresponding first level. The first weight is the importance coefficient of the first level assigned to each parameter when calculating the geological sweet spot level. The weight value is usually a number between 0 and 1, and the sum of the weights of all parameters is 1. The first weight can be determined based on geological understanding, parameter sensitivity analysis, and historical data statistics.

[0111] For example, the weight of the comprehensive index can be 0.4, the weight of the acoustic time difference can be 0.3, and the weights of porosity and permeability can each be 0.15.

[0112] For example, the geological sweet spot level corresponding to the reservoir is the final determined geological sweet spot level. A comprehensive score is obtained by weighted summation or weighted scoring of the first level of each parameter, and then mapped to the final level according to the preset threshold range corresponding to the comprehensive score.

[0113] For example, based on the measured values ​​of acoustic transit time, porosity, and permeability, the corresponding first level is matched in the geological classification evaluation standard.

[0114] For example, when the acoustic transit time is 220 μs / m, it can be classified as a second geological level; when the porosity is 8%, it can be classified as a second geological level; when the permeability is 0.25 mD, it can be classified as a third geological level. Then, the first level corresponding to each parameter is multiplied by its first weight. For example, the first level corresponding to the comprehensive index is 2, with a weight of 0.4; the first level corresponding to the acoustic transit time is 2, with a weight of 0.3; the first level corresponding to the porosity is 2, with a weight of 0.15; the first level corresponding to the permeability is 2, with a weight of 0.15. Based on the above data, a weighted calculation is performed, resulting in 2×0.4+2×0.3+2×0.15+3×0.15=2.15, ultimately determining the geological sweet spot level as the second geological level.

[0115] In this way, the advantages of the comprehensive index are preserved by using a multi-parameter weighted scoring method, while avoiding the misjudgment that may occur if the comprehensive index is relied upon alone. This increases the fault tolerance and can accurately reflect the comprehensive quality of the reservoir, providing a reliable basis for the selection of fracturing intervals in horizontal wells.

[0116] For example, a certain section might have abnormally high resistivity in deep lateral logging due to special reasons, such as calcareous cementation, but its actual porosity is very low, leading to an inflated composite index. Introducing a first-level classification corresponding to the actual porosity and permeability can effectively correct this bias and prevent the section from being mistakenly classified as a high-level sweet spot.

[0117] Thus, by calculating the geological sweet spot level corresponding to the reservoir, and taking into account the comprehensive index, sonic transit time, porosity and permeability that affect the reservoir's storage capacity, the parameters that have a greater impact on the reservoir's storage capacity can be reasonably determined based on the weighted evaluation method, effectively guiding the identification of geological sweet spots.

[0118] In some possible implementations, the rock mechanical parameters include minimum principal stress, Young's modulus, Poisson's ratio, and compressive strength; based on the rock mechanical parameters and pre-set engineering grading evaluation criteria, the engineering sweet spot level corresponding to the reservoir is determined, including:

[0119] The brittleness index is determined based on the Young's modulus and the Poisson's ratio.

[0120] Based on the established engineering classification evaluation criteria, the second level corresponding to the minimum principal stress, Young's modulus, brittleness index and compressive strength are determined respectively;

[0121] The minimum value among the second levels corresponding to the minimum principal stress, the Young's modulus, the brittleness index, and the compressive strength is determined as the engineering sweet spot level corresponding to the reservoir.

[0122] In some possible implementations, the rock mechanical parameters include minimum principal stress, Young's modulus, Poisson's ratio, and compressive strength; based on the rock mechanical parameters and pre-set engineering grading evaluation criteria, the engineering sweet spot level corresponding to the reservoir is determined, including:

[0123] The brittleness index is determined based on the Young's modulus and the Poisson's ratio.

[0124] Based on the established engineering classification evaluation criteria, the second level corresponding to the minimum principal stress, Young's modulus, brittleness index and compressive strength are determined, as well as the second weight corresponding to the minimum principal stress, Young's modulus, brittleness index and compressive strength.

[0125] The engineering sweet spot level of the reservoir is determined based on the second level corresponding to the minimum principal stress, Young's modulus, brittleness index, and compressive strength, respectively, and the second weight corresponding to the minimum principal stress, Young's modulus, brittleness index, and compressive strength, respectively.

[0126] For example, the minimum principal stress is the smallest of the three mutually perpendicular principal stresses at a point underground. In horizontal well hydraulic fracturing, the minimum horizontal principal stress determines the fracture initiation pressure and the initial direction of fracture propagation. The lower the minimum principal stress, the easier it is to fracture, the lower the required pump pressure, and the easier the fracturing process.

[0127] For example, Young's modulus is a physical quantity that measures a rock's ability to resist tensile / compressive strain during the elastic deformation stage. A higher Young's modulus indicates a stiffer rock, which is conducive to the formation of wider fractures; however, an excessively high modulus may lead to difficulty in fracture propagation or excessively straight fractures. A lower Young's modulus indicates a softer rock, narrower fractures, a higher risk of proppant embedding, and potentially reduced conductivity. Fracturing energy may be consumed more in the plastic deformation of the rock than in the formation of effective fractures.

[0128] For example, Poisson's ratio is the ratio of lateral strain to axial strain in a rock under uniaxial compression, and it can be used to describe the degree of lateral expansion of a rock under stress. A high Poisson's ratio indicates significant lateral expansion of the rock under compression, which is usually associated with low brittleness and high plasticity, and is not conducive to the formation of complex crack networks. A low Poisson's ratio indicates small lateral expansion of the rock under compression, which is usually associated with high brittleness, and the rock is more prone to brittle fracture under stress, which is conducive to the formation of complex crack networks.

[0129] For example, compressive strength is the maximum compressive stress that a rock can withstand before it fails under uniaxial pressure, and it can be used to reflect the rock's ability to resist compressive failure. The higher the compressive strength, the higher the pump pressure is usually required to initiate and propagate the crack.

[0130] For example, the brittleness index is a dimensionless index calculated by combining Young's modulus and Poisson's ratio. It is used to quantify the tendency of rocks to undergo brittle fracture rather than plastic deformation during hydraulic fracturing. The brittleness index can be calculated using methods such as the Rickman formula. The higher the brittleness index, the more brittle the rock. Brittle rocks are more likely to generate complex, multi-branched fracture networks during hydraulic fracturing, increasing the fracturing volume and improving production capacity.

[0131] For example, the second level refers to the preliminary grade obtained after each individual parameter is independently evaluated according to its own preset standards. That is, each parameter among minimum principal stress, Young's modulus, brittleness index, and compressive strength has its own corresponding second level. The second weight is the importance coefficient of the second level assigned to each parameter when calculating the engineering sweet spot level. The weight value is usually a number between 0 and 1, and the sum of the weights of all parameters is 1. The second weight can be determined based on geological understanding, parameter sensitivity analysis, and historical data statistics.

[0132] For example, the second weights of minimum principal stress, Young's modulus, brittleness index, and compressive strength are determined using the analytic hierarchy process (AHP). For instance, the second weights for minimum principal stress and brittleness index are 0.3, and for compressive strength and Young's modulus are 0.2. In implementation, the first level of each parameter is first determined based on measured data. Then, a weighted sum is performed using the corresponding weights. Finally, the engineering sweet spot level is determined based on the total score range.

[0133] For example, the engineering sweet spot level corresponding to the reservoir is the final determined geological sweet spot level. A comprehensive score is obtained by weighted summation or weighted scoring of the second level of each parameter, and then mapped to the final level according to the preset threshold range corresponding to the comprehensive score.

[0134] Thus, by calculating the engineering sweet spot level corresponding to the reservoir, and considering the four core factors affecting fracturing—stress conditions, rock elasticity, brittleness, and strength—based on a weighted evaluation method, parameters that have a greater impact on fracturing success rate and complexity can be reasonably determined, effectively guiding the relevant parameters for fracturing design.

[0135] In some possible implementations, the weights of each parameter in the geological classification evaluation criteria and / or the engineering classification evaluation criteria are obtained in advance using the analytic hierarchy process.

[0136] For example, the Analytic Hierarchy Process (AHP) constructs an importance comparison matrix among parameters based on expert experience and quantifies the weight of each parameter, such as giving permeability a greater weight than acoustic transit time. Alternatively, correlation analysis, principal component analysis, and other methods can be used to determine the weight of each parameter; this is not a limitation here.

[0137] For example, in geological grading evaluation standards, permeability has the greatest impact on production capacity. The first weight corresponding to permeability can be set to 0.4, the first weight corresponding to porosity can be set to 0.3, the first weight corresponding to the comprehensive index can be set to 0.2, and the first weight corresponding to acoustic transit time can be set to 0.1.

[0138] Compared to existing technologies, traditional methods typically rely on expert experience to directly assign weights, resulting in strong subjectivity and inconsistent standards among different evaluators. In contrast, the Analytic Hierarchy Process (AHP) constructs a mathematical judgment matrix, transforming subjective experience into quantitative data. Combined with a consistency check mechanism to eliminate logical contradictions, it ensures that the weight allocation results are verifiable and repeatable, effectively solving the problem of fluctuations in evaluation results caused by manual weighting.

[0139] Thus, by using the analytic hierarchy process (AHP), the weights of each parameter in the geological and engineering grading evaluation standards can be reasonably determined, which facilitates accurate identification of "sweet spots" and reduces the misjudgment rate.

[0140] In some possible implementations, the geological grading evaluation criteria include:

[0141] In the first geological level, the value of the comprehensive index is greater than 30, the value of the sonic transit time is greater than 225 μs / m, the value of the porosity is greater than 9%, and the value of the permeability is greater than 0.5 mD.

[0142] In the second geological level, the comprehensive index value is between 20 and 30, the sonic transit time value is between 218 and 225 μs / m, the porosity value is between 7.5 and 9%, and the permeability value is between 0.25 and 0.5 mD.

[0143] In the third geological level, the comprehensive index value is between 10 and 20, the acoustic transit time value is between 208 and 218 μs / m, the porosity value is between 6 and 7.5%, and the permeability value is between 0.1 and 0.25 mD.

[0144] For example, as shown in Table 1 below, the threshold ranges corresponding to each level in the geological grading evaluation standard are illustrated.

[0145] Table 1 Geological Classification Evaluation Standards

[0146]

[0147] Among them, the first geological level reservoir can represent a gas-bearing reservoir, the second geological level reservoir can represent a gas-bearing reservoir with a relatively small gas content, and the third geological level reservoir can represent a poor gas-bearing reservoir with a very low gas content. In addition, it can also include dry reservoirs with negligible gas content, wherein the comprehensive index value of the dry reservoir is less than 10, the sonic transit time value is less than 208 μs / m, the porosity value is less than 6%, and the permeability value is less than 0.1 mD.

[0148] In some possible implementations, the engineering grading evaluation criteria include:

[0149] In the first engineering level, the minimum principal stress is less than 37 MPa, the Young's modulus is less than 28 GPa, the brittleness index is greater than 55%, and the compressive strength is less than 70 MPa.

[0150] In the second engineering level, the minimum principal stress is between 37-39 MPa, the Young's modulus is between 28-30 GPa, the brittleness index is between 45-55%, and the compressive strength is between 70-110 MPa.

[0151] In the third engineering level, the minimum principal stress is greater than 39 MPa, the Young's modulus is greater than 30 GPa, the brittleness index is less than 45%, and the compressive strength is greater than 110 MPa.

[0152] For example, as shown in Table 2 below, the threshold ranges corresponding to each level in the engineering grading evaluation standard are illustrated.

[0153] Table 2 Engineering Grading Evaluation Standards

[0154]

[0155] Among them, the reservoirs of the first engineering level have relatively low minimum principal stress, Young's modulus, and compressive strength, but relatively high brittleness index, and the construction conditions are relatively good; the reservoirs of the second engineering level have minimum principal stress, Young's modulus, compressive strength, and brittleness index all within a moderate range, and the construction conditions are generally average; the reservoirs of the third engineering level have relatively high minimum principal stress, Young's modulus, and compressive strength, but relatively low brittleness index, and the construction conditions are relatively poor.

[0156] It is understood that the data for all parameters mentioned above are merely examples and should not be taken as a limitation on actual parameters.

[0157] In some possible implementations, before obtaining logging-sensitive parameters of a horizontal well in a tight gas reservoir, the method further includes:

[0158] Obtain logging curves for vertical wells and horizontal wells;

[0159] Determine the mapping relationship between the parameters in the vertical well logging curve and the horizontal well logging curve;

[0160] By using the vertical well logging curve corresponding to the vertical well gas test results and the mapping relationship, the logging sensitive parameters in the horizontal well logging curve are determined.

[0161] For example, a vertical well logging curve refers to a set of data reflecting the physical characteristics of the formation obtained through vertical drilling. Specifically, it can be implemented using sonic transit time, resistivity, and natural gamma logging curves, and is used to provide a benchmark reference for formation parameters.

[0162] For example, horizontal well logging curves refer to formation response data collected during the drilling process in the horizontal section. Specifically, they can be obtained using logging while drilling or wireline logging methods, and are used to characterize the geological features of the horizontal well section.

[0163] For example, the mapping relationship refers to the correlation model between logging parameters of vertical and horizontal wells. Specifically, it can be established using statistical regression or machine learning methods to eliminate the impact of well type differences on parameter comparability. For instance, parameter relationships can be fitted to the layers encountered by both vertical and horizontal wells, or machine learning methods such as random forest models can be used to predict horizontal well parameters using vertical well parameters as input.

[0164] For example, the results of vertical well testing refer to the actual production capacity data obtained through vertical well testing. Specifically, this can be achieved using well test analysis or production dynamic data, and is used to verify the correlation between logging parameters and production capacity.

[0165] For example, logging sensitive parameters refer to geological parameters that have a significant impact on reservoir productivity. These parameters can be specifically determined through interpretation chart screening and are used to guide the evaluation of sweet spots in horizontal wells.

[0166] For example, after acquiring logging data from a vertical well adjacent to a horizontal well and the horizontal well itself, mathematical transformation relationships between parameters are established by comparing the distribution characteristics of parameters such as sonic transit time and resistivity between the two types of wells. For instance, a multivariate regression method is used to model the correlation between the compensated density data of the horizontal well and the clay content of the vertical well. Subsequently, using the actual production data obtained from the vertical well test as a verification benchmark, the correlation between different combinations of logging parameters and production capacity is analyzed, and the production capacity of the vertical well test is correlated with the mapped logging parameters. Parameters with a strong correlation to production capacity can be selected as logging sensitive parameters, such as sonic transit time and total hydrocarbon values ​​in gas logging.

[0167] For example, by considering the differences between horizontal and vertical wells in terms of measurement environment and formation response, a parameter mapping relationship between vertical and horizontal wells is established to eliminate data deviation caused by well type differences. At the same time, the parameter sensitivity is verified by combining the gas test results of vertical wells as an objective production capacity indicator, thus solving the problem that parameter selection in traditional methods relies on experience judgment.

[0168] For example, the mapping relationship is as follows Figure 2 As shown, Rsh is the mud resistivity, Rv is the logging parameter for vertical wells, and Rh is the logging parameter for horizontal wells. Rv / Rsh represents the vertical conductivity of the formation, while Rh / Rsh represents the horizontal conductivity. Rv / Rsh and Rh / Rsh can be used to calculate geological characteristics such as mud content (Vsh). Rsd / Rsh is the ratio of pure sandstone resistivity to mud resistivity, used to calculate geological characteristics such as water saturation (Sw).

[0169] like Figure 2 As shown, it is clear that the clay content of the dry layer is between 17-25%; the clay content of the gas layer is less than 17%; and the poor gas layer... Figure 2 The Rsd / Rsh ratio of the gas-water layer to the water layer is 3.62, corresponding to a water saturation of 65%; the Rsd / Rsh ratio of the gas layer to the gas-water layer is 5.74, corresponding to a water saturation of 55%.

[0170] Compared with existing technologies, traditional methods typically rely on empirical formulas or single logging parameters to screen lithology-sensitive parameters, lacking verification from actual production data, leading to a disconnect between parameter selection and the actual gas production capacity of the reservoir. This disclosure, through correlation analysis between vertical well gas testing results and logging parameters, transforms actual production data into horizontal well parameter screening criteria, providing a clear production response basis for determining lithology-sensitive parameters and solving the problems of high subjectivity and low reliability of traditional methods.

[0171] Through the above technical solution, this application can establish a horizontal well parameter screening model based on the measured production capacity data of vertical wells, realize the integrated geological and engineering calibration of lithology-sensitive parameters, effectively improve the matching degree between production capacity evaluation results and actual reservoir gas production capacity, and provide a reliable data foundation for horizontal well sweet spot identification and fracturing optimization.

[0172] In this way, by mapping between parameters of vertical and horizontal wells, data acquisition bias can be eliminated, and highly correlated parameters can be screened by inverting the gas test results to construct a set of sensitive parameters suitable for horizontal wells. This can solve the problem of distortion in horizontal well logging data and provide a reliable data foundation for subsequent sweet spot classification evaluation.

[0173] In some possible implementations, the logging-sensitive parameters in the horizontal well logging curve are determined by using the vertical well test results corresponding to the vertical well logging curve and the mapping relationship, including:

[0174] Based on the vertical well logging curves and the corresponding vertical well gas test results and the mapping relationship, a first interpretation chart for water saturation and porosity of horizontal wells, and a second interpretation chart for clay content and compensation density of horizontal wells are established.

[0175] Sensitivity analysis is performed based on the first and second interpretation charts to determine the logging sensitivity parameters in the horizontal well logging curves.

[0176] For example, the first explanatory chart is a cross-plot between water saturation and porosity (φ, in %), which can be used to determine reservoir fluid properties, i.e., whether the reservoir contains mobile water. Wherein, as Figure 3 The first explanatory diagram shown addresses gas layers and differential gas layers, i.e. Figure 3 The air-water layer, water layer, and dry layer, and Figure 2 Correspondingly: the water saturation of the gas layer is less than 55%; the water saturation of the water layer is greater than 65%.

[0177] For example, the second interpretative chart is a cross-plot of clay content (Vsh, in %) and compensated density (DEN, in g / cm³), which can be used to quantify the impact of lithological purity and compaction degree on productivity. Among them, such as... Figure 4 The second explanatory diagram shown addresses gas layers and differential gas layers, i.e. Figure 4 The air-water layer, water layer, and dry layer, and Figure 2 Correspondingly: the clay content of the gas layer is less than 17%; the clay content of the dry layer is between 17% and 25%.

[0178] For example, sensitivity analysis uses statistical methods to quantify the impact of parameters on production capacity, and can be used to screen out the core parameters that are most effective for production capacity forecasting.

[0179] For example, the first interpretation chart refers to a two-dimensional cross-plot with porosity as the abscissa and water saturation as the ordinate. Specifically, this can be achieved by statistically analyzing the correlation between porosity and water saturation in vertical well gas testing results and analyzing the mapping relationship between porosity in vertical and horizontal well logging curves. This is used to screen sensitive parameters reflecting the gas-bearing capacity of reservoirs in horizontal wells. The second interpretation chart refers to a two-dimensional cross-plot with compensated density as the abscissa and clay content as the ordinate. Specifically, this can be achieved by analyzing the correlation between clay content and compensated density in vertical well gas testing results and analyzing the mapping relationship between porosity in vertical and horizontal well logging curves. This is used to screen sensitive parameters characterizing reservoir properties in horizontal wells.

[0180] For example, after obtaining logging curves and gas testing results in vertical wells, a first interpretation chart is established by statistically analyzing the porosity and water saturation distribution ranges of high-yield gas-testing intervals, thus determining the correlation between porosity and water saturation. Simultaneously, the correspondence between clay content and compensation density in the gas testing results is analyzed to establish a second interpretation chart, clarifying the influence pattern of clay content on reservoir properties. In horizontal well logging curves, porosity parameters significantly correlated with changes in water saturation are selected based on the first interpretation chart as sensitive indicators of gas content; compensation density parameters closely correlated with clay content are selected based on the second interpretation chart as sensitive indicators of physical properties. Through joint analysis of the two types of charts, a comprehensive combination of lithological sensitive parameters that can reflect both reservoir gas content and reservoir physical properties is determined in horizontal wells.

[0181] For example, by constructing a first interpretation chart of water saturation and porosity, the sensitivity of porosity to gas reservoir productivity can be identified; by constructing a second interpretation chart of clay content and compensating density, the impact of clay content on reservoir properties can be determined.

[0182] This disclosure uses a dual-map mapping of vertical well gas testing results and horizontal well logging data to directly correlate actual gas production capacity with logging response characteristics, forming a dynamic calibration mechanism that solves the problem of separation between geological parameters and engineering parameters in traditional methods.

[0183] In this way, based on the dual-dimensional analysis of reservoir fluid occurrence characteristics and lithological structure, the core geological parameters affecting production capacity can be accurately identified, providing a reliable data basis for the subsequent coordinated classification of geological sweet spots and engineering sweet spots, thereby improving the objectivity and accuracy of horizontal well production capacity evaluation.

[0184] like Figure 5As shown, this disclosure proposes an evaluation method for improving production in horizontal wells of tight sandstone gas reservoirs, based on geological deployment, drilling guidance, post-drilling evaluation, and fracturing optimization. Specifically, it implements comprehensive and precise risk control at each stage, including optimal geological deployment, drilling process tracking, detailed post-drilling evaluation, and fracturing parameters, to address the problem of substandard single-well production caused by unclear geological understanding or unreasonable engineering design. The technical solution of this disclosure is achieved through the following measures.

[0185] First, obtain geological data such as drilling, logging, seismic, well logging data, and core samples.

[0186] Step 1: Geological and seismic integrated optimization of horizontal well geological deployment, including the following steps 1.1-1.3.

[0187] Step 1.1: Detailed correlation of regional stratigraphy and structural features;

[0188] Based on completed vertical or directional wells in the region, and combined with 3D seismic results, a stratigraphic framework profile of the horizontal well deployment area is drawn, and fine division and comparison of the regional stratigraphy are carried out to build a stratigraphic framework for the whole region and to determine the macroscopic structural characteristics of the region.

[0189] Among them, with the above-mentioned stratigraphic framework as constraints, the stratigraphic position is precisely calibrated by well seismic analysis, multi-well comparative interpretation is carried out, a fine velocity field is established by well control and stratigraphic control, and the structural results compiled by different grids are compared. The 50×50 small grid smoothing mapping method is selected to finely depict the micro-structure, and further correction is carried out by combining horizontal well drilling data to accurately control the inter-layer structural trend.

[0190] Step 1.2, Distribution patterns of sand bodies and effective sand bodies;

[0191] Among these methods, by integrating the advantageous attributes of pre-stack and post-stack 3D seismic data volumes, the distribution of sand bodies in the target layer can be refined. Under the constraint of lithofacies bodies, geostatistical inversion can be further utilized to finely characterize the distribution features of sand bodies in each sublayer and clarify the distribution characteristics of high-quality reservoirs.

[0192] Step 1.3, Selecting the best well location and deployment;

[0193] Based on the understanding of regional micro-structural characteristics, sand body development scale, and effective sand body distribution, combined with regional actual drilling data, the target interval of a certain test horizontal well is taken as the main target layer. The selection principle is "gentle structural trend, widespread sand body distribution, continuous effective reservoir, good production effect of adjacent wells, and low regional water-gas ratio" to carry out horizontal well location deployment. Specifically, this includes using 3D seismic profiles and reservoir sand body prediction profiles to clarify the target points and bottom hole locations of horizontal well deployment.

[0194] Step 2: Integrated geological and seismic optimization of horizontal section geological guidance, including the following steps 2.1-2.2.

[0195] Step 2.1, optimize the trajectory design for the horizontal segment;

[0196] Based on the actual drilling data of adjacent wells and the reservoir identification and accurate calculation results of formation dip angle corresponding to 3D seismic data, the trajectory simulation is carried out using the integrated seismic geology and engineering platform software. This allows for the early identification of lithological and structural change points, filling gaps in the understanding between wells, optimizing the trajectory design of horizontal wells, designing multiple well location target points for horizontal segments, and precisely controlling the drilling location of the trajectory within the sand body.

[0197] Step 2.2, optimize the directional drilling of horizontal wells;

[0198] By combining multiple disciplines such as geology, seismology, and well logging, the entire process is tracked and analyzed, reasonable predictions are made, strategies are optimized and adjusted in real time, and the implementation of horizontal wells is efficiently guided.

[0199] Firstly, by using small-layer comparison and seismic prediction for target guidance, the spatial location of the target layer can be determined in real time, and the inclination increase can be adjusted in a timely manner to ensure accurate target entry point; based on the fine small-layer comparison of adjacent wells, the overlying strata are calibrated layer by layer, and combined with seismic data, the lithological and structural change trends between wells are reasonably predicted, and the relative position of the target layer can be predicted in advance, so as to achieve successful target entry in one go.

[0200] Secondly, by combining drilling data with 3D seismic data for horizontal section guidance, the reservoir structure and lithological changes can be accurately predicted, and the well inclination can be adjusted in a timely manner to ensure the effectiveness of the horizontal section implementation. For example, based on the starting well and combined with 3D seismic data, lithological change zones and structural low points in the early section of the trajectory, as well as structural high points in the middle and later sections, can be identified in advance. By avoiding these in advance, the horizontal section trajectory can be designed reasonably.

[0201] Step 3: Based on the "double sweet spot" comprehensive upgrade of fracturing parameters, including the following steps 3.1-3.2.

[0202] Step 3.1: Identification of "double sweet spots" in three-dimensional geological engineering of completed wells;

[0203] (1) Identification of geological desserts;

[0204] Based on the gas testing results of vertical wells, and combining the differences and relationships between logging parameters of horizontal wells (Rh) and vertical wells (Rv), interpretation charts for water saturation and porosity, as well as interpretation charts for clay content and compensation density, were established for horizontal wells. The results are as follows: Figure 2-4 As shown, a detailed interpretation of fluid flow in a single well is carried out.

[0205] (2) Establish static geological classification and evaluation standards for horizontal wells;

[0206] Based on the theoretical foundation of lithological gas reservoirs, lithology controls pore structure, and pore structure controls the occurrence state of fluids within the pores. Using the aforementioned interpretation charts and combining the results of the four-property relationship—the correlation characteristics between the reservoir's lithology, physical properties, electrical properties, and gas-bearing capacity—sensitive parameters are selected to establish a comprehensive index corresponding to the static geology of horizontal wells.

[0207] ;

[0208] Where ZHZS represents the comprehensive index; DT represents the acoustic time difference curve; Vsh represents the minimum value of the sonic transit time curve; RLLD represents the deep lateral logging resistivity; QT represents the total hydrocarbon value from gas logging. This indicates the total hydrocarbon base value measured in gas.

[0209] For example, the results of the four property relationships include: In the relationship between lithology and physical properties, changes in lithology directly affect reservoir physical properties. Tight sandstone reservoirs typically exhibit low porosity, low permeability, and strong heterogeneity. For instance, sections with high clay content have even lower porosity and lower permeability. In the relationship between electrical properties and gas-bearing properties, electrical properties are correlated with gas-bearing properties. Electrical parameters such as natural gamma, resistivity, and acoustic transit time are closely related to gas-bearing properties. For example, gas-bearing reservoirs typically exhibit high resistivity and low acoustic transit time, while water-bearing reservoirs exhibit low resistivity and high acoustic transit time. Cross-plots of electrical parameters can help delineate gas- and water-bearing layers.

[0210] Then, by combining the comprehensive geological index with sensitive logging curves, a geological classification evaluation standard corresponding to horizontal wells, as shown in Table 1 above, can be established.

[0211] (3) Engineering dessert identification;

[0212] Core analysis data and dipole sonic logging data can be used to calculate rock mechanics parameters such as Poisson's ratio, maximum / minimum principal stress, and Young's modulus. An engineering classification and evaluation standard corresponding to these rock mechanics parameters has been established to guide the design and optimization of fracturing parameters. This method is also used to identify comprehensive sweet spots in horizontal well sections based on geological sweet spot identification.

[0213] Step 3.2, upgrading fracturing parameters and monitoring fracturing effects;

[0214] Based on the identification of sweet spots in engineering fracturing, and addressing challenges such as high construction pressure, poor fracture formation, and difficulty in adding proppant, a volumetric fracturing mode was adopted, combining close-cut fracturing, high-intensity proppant addition, controlled fluid and proppant addition, and dynamic temporary plugging. This was combined with high-frequency pressure monitoring and wide-area electromagnetic monitoring to assess the fracturing effect. Specifically, for the aforementioned test horizontal wells, the optimized implementation results were as follows: the section length was reduced from 80-120m to 80-100m; the number of proppant clusters increased from 2-4 to 3-5; the proppant addition intensity increased from 2.0 to 2.7t / m; the pre-flush fluid ratio decreased from 50% to 30%; the proppant ratio increased from 16-20 to 20-25%; the proppant mesh was upgraded from 20 / 40 mesh to 20 / 40+40 / 70 mesh; and a high-strength, tapered, soluble temporary plugging agent with a compressive strength greater than 70MPa was used.

[0215] Step 4: Conduct field application and effect evaluation of the above-mentioned horizontal well production improvement measures. The verification results are shown in Table 4 below;

[0216] Table 4. Verification Results of Horizontal Well Production Enhancement Measures

[0217]

[0218] Among the actual verification results of the integrated geological and engineering drilling effect, 12 wells were completed, with an average horizontal section length of 1202m, a reservoir length of 1044m, an effective reservoir of 852m, and an effective reservoir drilling rate of 70.9%. Compared with previous years, the horizontal section length increased by 27m and the effective reservoir drilling rate increased by 8.7%.

[0219] Among the actual verification results of the fracturing and gas testing effect, a total of 6 wells have been tested so far. The average stable oil pressure has increased from 17.4 MPa to 18.4 MPa, and the casing pressure has increased from 18.0 MPa to 19.2 MPa. The daily gas production has increased from 83,000 cubic meters per day to 117,000 cubic meters per day. The unobstructed flow rate has increased from 388,000 cubic meters per day to 444,000 cubic meters per day, which is 14% higher than the production of neighboring wells. The gas testing effect has been significantly improved.

[0220] This disclosure can be used for production enhancement evaluation of reservoir geology and engineering before horizontal well gas production in tight sandstone gas reservoirs. It comprehensively considers the impact of geological deployment, drilling tracking, post-drilling evaluation, and fracturing parameters on production capacity, achieving precise risk control at each stage and solving the comprehensive production enhancement evaluation of reservoir geology and engineering before gas production. Compared with production enhancement measures such as drainage gas production and surface pressurization, it is more scalable, efficient, and practical.

[0221] This disclosure, among numerous gas well production enhancement methods, is the first to consider an integrated geological and engineering approach to production enhancement, encompassing both reservoir geology and engineering aspects before gas production processes. Compared to drainage gas production and surface production enhancement methods, it starts from the production foundation of tight sandstone gas reservoirs, increases the proportion of geological sweet spots encountered in horizontal wells, and solidifies the geological foundation for production. Furthermore, it considers optimizing fracturing parameters and process modification parameters to achieve full modification of the horizontal section and increase initial production. This method has advantages such as ease of implementation and rapid production enhancement.

[0222] Furthermore, the term “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as advantageous compared to other aspects or designs. Rather, the use of the term “exemplary” is intended to present the concept in a concrete manner. As used herein, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or clear from the context, “X applies A or B” is intended to mean any of the natural inclusive arrangements. That is, “X applies A or B” satisfies any of the foregoing instances if X applies A; X applies B; or both X applies A and B. Additionally, unless otherwise specified or clear from the context to refer to the singular form, the articles “a” and “an” as used in this disclosure and the appended claims are generally understood to mean “one or more.”

[0223] Similarly, although this disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art upon reading and understanding this specification and the accompanying drawings. This disclosure includes all such modifications and variations and is limited only by the scope of the claims. In particular, with respect to the various functions performed by the components described above (e.g., elements, resources, etc.), unless otherwise indicated, the terminology used to describe such components is intended to correspond to any component (functionally equivalent) that performs the specific function of the described component, even if structurally not equivalent to the disclosed structure. Furthermore, although specific features of this disclosure may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations, as may be desired and advantageous to any given or particular application. Moreover, with regard to the terms “comprising,” “owning,” “having,” “having,” or variations thereof as used in the detailed description or claims, such terms are intended to be inclusive in a manner similar to the term “including.”

[0224] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

[0225] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for evaluating the productivity of horizontal wells in tight gas reservoirs, characterized in that, include: To obtain logging-sensitive parameters and rock mechanics parameters of horizontal wells in tight gas reservoirs; Based on the logging sensitive parameters and comprehensive index model, the comprehensive index corresponding to different reservoirs in horizontal wells is calculated. The comprehensive index is used to characterize the degree of good or bad of the comprehensive geological conditions of the reservoir. Based on the comprehensive index, the well logging sensitive parameters, and the rock mechanics parameters, the geological sweet spot level and engineering sweet spot level of the reservoir are determined; Based on the geological sweet spot level and engineering sweet spot level corresponding to the reservoir, the productivity of the horizontal well is evaluated; The logging sensitive parameters include sonic transit time curve, minimum sonic transit time curve, clay content, deep lateral logging resistivity, gas logging total hydrocarbon value, and gas logging total hydrocarbon base value. The formula for calculating the composite index model is as follows: ； Where ZHZS represents the comprehensive index; DT represents the acoustic time difference curve; Vsh represents the minimum value of the sonic transit time curve; RLLD represents the deep lateral logging resistivity; QT represents the total hydrocarbon value from gas logging. This indicates the total hydrocarbon base value measured in gas. Based on the comprehensive index, the well logging sensitive parameters, and the rock mechanics parameters, the geological sweet spot level and engineering sweet spot level corresponding to the reservoir are determined, including: Based on the comprehensive index, the well logging sensitive parameters, and the pre-set geological classification evaluation criteria, the geological sweet spot level corresponding to the reservoir is determined; Based on the rock mechanics parameters and the pre-set engineering grading evaluation criteria, the engineering sweet spot level corresponding to the reservoir is determined.

2. The method according to claim 1, characterized in that, Based on the comprehensive index, the well logging sensitive parameters, and the pre-set geological classification evaluation criteria, the geological sweet spot level corresponding to the reservoir is determined, including: Based on the aforementioned logging sensitive parameters, the acoustic transit time, porosity, and permeability of the reservoir are determined; Based on the established geological grading evaluation criteria, the first level corresponding to the comprehensive index, the sonic transit time, the porosity, and the permeability are determined, as well as the first weight corresponding to the comprehensive index, the sonic transit time, the porosity, and the permeability. The geological sweet spot level of the reservoir is determined based on the first level corresponding to the comprehensive index, the acoustic transit time, the porosity, and the permeability, respectively, and the first weight corresponding to the comprehensive index, the acoustic transit time, the porosity, and the permeability, respectively.

3. The method according to claim 2, characterized in that, The rock mechanical parameters include minimum principal stress, Young's modulus, Poisson's ratio, and compressive strength; Based on the rock mechanical parameters and pre-set engineering classification evaluation criteria, the engineering sweet spot level corresponding to the reservoir is determined, including: The brittleness index is determined based on the Young's modulus and the Poisson's ratio. Based on the established engineering classification evaluation criteria, the second level corresponding to the minimum principal stress, Young's modulus, brittleness index and compressive strength are determined, as well as the second weight corresponding to the minimum principal stress, Young's modulus, brittleness index and compressive strength. The engineering sweet spot level of the reservoir is determined based on the second level corresponding to the minimum principal stress, Young's modulus, brittleness index, and compressive strength, respectively, and the second weight corresponding to the minimum principal stress, Young's modulus, brittleness index, and compressive strength, respectively.

4. The method according to claim 3, characterized in that, in, The weights corresponding to each parameter in the geological classification evaluation standard and / or the engineering classification evaluation standard are obtained in advance using the analytic hierarchy process.

5. The method according to claim 3, characterized in that, The geological grading evaluation criteria include: In the first geological level, the comprehensive index value is greater than 30, the sonic transit time value is greater than 225 μs / m, the porosity value is greater than 9%, and the permeability value is greater than 0.5 mD. In the second geological level, the comprehensive index value is between 20 and 30, the sonic transit time value is between 218 and 225 μs / m, the porosity value is between 7.5 and 9%, and the permeability value is between 0.25 and 0.5 mD. In the third geological level, the comprehensive index value is between 10 and 20, the sonic transit time value is between 208 and 218 μs / m, the porosity value is between 6 and 7.5%, and the permeability value is between 0.1 and 0.25 mD.

6. The method according to claim 5, characterized in that, The engineering grading and evaluation criteria include: In the first engineering level, the minimum principal stress is less than 37 MPa, the Young's modulus is less than 28 GPa, the brittleness index is greater than 55%, and the compressive strength is less than 70 MPa. In the second engineering level, the minimum principal stress is between 37-39 MPa, the Young's modulus is between 28-30 GPa, the brittleness index is between 45-55%, and the compressive strength is between 70-110 MPa. In the third engineering level, the minimum principal stress is greater than 39 MPa, the Young's modulus is greater than 30 GPa, the brittleness index is less than 45%, and the compressive strength is greater than 110 MPa.

7. The method according to any one of claims 1-4, characterized in that, Before obtaining the logging-sensitive parameters of a horizontal well in a tight gas reservoir, the method further includes: Obtain logging curves for vertical wells and horizontal wells; Determine the mapping relationship between the parameters in the vertical well logging curve and the horizontal well logging curve; By using the vertical well logging curve corresponding to the vertical well gas test results and the mapping relationship, the logging sensitive parameters in the horizontal well logging curve are determined.

8. The method according to claim 7, characterized in that, By using the vertical well logging curves and the corresponding vertical well gas test results, and the mapping relationship, the logging-sensitive parameters in the horizontal well logging curves are determined, including: Based on the vertical well logging curves and the corresponding vertical well gas test results and the mapping relationship, a first interpretation chart for water saturation and porosity of horizontal wells, and a second interpretation chart for clay content and compensation density of horizontal wells are established. Sensitivity analysis is performed based on the first and second interpretation charts to determine the logging sensitivity parameters in the horizontal well logging curves.

Images