Phase control model-based oil and gas resource quantity evaluation method, device, equipment and medium

Abstract

The invention discloses an oil and gas resource quantity evaluation method and device based on a phase control model, equipment and a medium, and relates to the technical field of geological resource exploration. The method comprises the following steps: establishing a facies belt vertical evolution model according to a sedimentary facies belt in a target reservoir; a three-dimensional geological entity of the target reservoir is constructed, and a total relation model describing the relation between the reservoir depth and the accumulated rock volume is obtained based on the three-dimensional geological entity; coupling the facies belt vertical evolution model and the total relation model to obtain a sub-relation model for describing the relation between the reservoir depth of each sedimentary facies belt and the accumulated rock volume; based on the physical property characteristics of the sedimentary facies belts, reservoir parameter probability distribution models of the sedimentary facies belts are established; calculating oil and gas resource quantity by adopting a Monte Carlo simulation iteration method; and obtaining an oil and gas resource quantity probability distribution diagram according to the oil and gas resource quantity calculated by N times of iteration. The problems that the exploration early-stage resource quantity evaluation result is disjointed with the geological reality, the efficiency is low and the credibility is insufficient are solved.

Description

Technical Field

[0001] This invention relates to the field of geological resource exploration technology, and in particular to a method, apparatus, equipment and medium for evaluating oil and gas resources based on phase control models. Background Technology

[0002] In oil and gas exploration, accurately assessing the geological resources of a trap is a crucial foundation for formulating exploration and development strategies and investment decisions. Especially in the early stages of exploration, the ability to reasonably assess the uncertainty of resource quantities under conditions of scarce data directly impacts exploration risk assessment and decision optimization.

[0003] Currently, the main methods for evaluating oil and gas resources include the traditional Monte Carlo method and the stochastic modeling method based on three-dimensional geological models. However, these methods have significant shortcomings in practical applications and are difficult to meet the evaluation needs in the early stages of exploration.

[0004] (1) The "parameter independence fallacy" of the traditional Monte Carlo method. This method treats reservoir geometric parameters (such as area and thickness) and physical property parameters (such as porosity, net-to-gross ratio, and oil saturation) as independent random variables, and calculates resource quantity only by setting probability distributions for individual parameters and randomly sampling. However, in real geological bodies, reservoir parameters are controlled by the genesis of the same sedimentary facies zone (such as the essential differences in porosity and net-to-gross ratio between high-energy beach-core facies and low-energy beach-limb facies), and there is a strong correlation between parameters. The traditional method ignores this geological correlation, which easily leads to geologically unreasonable parameter combinations such as "high porosity of beach-limb facies + low porosity of beach-core facies", resulting in a serious disconnect between the resource quantity calculation results and geological reality, and low credibility. At the same time, in data-sparse areas, the setting of parameter distribution is highly dependent on external analog data, which is highly subjective and further amplifies the error of the results.

[0005] (2) The stochastic modeling method based on three-dimensional geological models is too costly and has limited applicability. Although this method can reflect the spatial heterogeneity of reservoirs, it requires dense well location data (such as core and logging data from dozens of wells) and high-quality three-dimensional seismic data as constraints. The modeling process involves complex geological modeling software operation and a large amount of computing resources, resulting in extremely high time costs (usually several weeks or even months) and economic costs. In scenarios where data is limited in the early stages of exploration, it is not only difficult to obtain sufficient constraints, but the reliability of the modeling results is also greatly reduced due to insufficient data support, making it unable to efficiently serve early decision-making.

[0006] These problems lead to a disconnect between early resource assessments and geological realities, resulting in low efficiency and insufficient credibility. Summary of the Invention

[0007] This invention provides a method, apparatus, equipment, and medium for evaluating oil and gas resources based on a phase control model, in order to solve the above-mentioned technical problems.

[0008] This invention is achieved through the following technical solution:

[0009] In a first aspect, the present invention provides a method for evaluating oil and gas resources based on a phase control model, comprising:

[0010] Based on the sedimentary facies zones within the target reservoir, establish a vertical evolution model of the facies zones;

[0011] A three-dimensional geological entity of the target reservoir is constructed, and a general relationship model describing the relationship between reservoir depth and accumulated rock volume is obtained based on the three-dimensional geological entity.

[0012] By coupling the vertical evolution model of the facies zone with the overall relationship model, a sub-relationship model describing the relationship between reservoir depth and cumulative rock volume for each sedimentary facies zone is obtained.

[0013] Based on the physical properties of sedimentary facies zones, probability distribution models of reservoir parameters for each sedimentary facies zone are established.

[0014] The Monte Carlo simulation iterative method is used to calculate oil and gas resources; each iteration includes the following calculations:

[0015] (1) Randomly extract the oil-water interface depth under the boundary constraints of the three-dimensional geological entity;

[0016] (2) Based on the sub-relationship model, the cumulative rock volume of each sedimentary facies zone at the depth of the oil-water interface is obtained;

[0017] (3) Randomly extract the combination of physical property parameters of each sedimentary facies zone from the reservoir parameter probability distribution model of each sedimentary facies zone;

[0018] (4) Calculate the oil and gas resources by using the cumulative rock volume and physical property parameters of each sedimentary facies zone according to the standard geological resource volume method formula;

[0019] Based on the oil and gas resource quantity calculated through N iterations, a probability distribution map of oil and gas resource quantity is obtained, where N is the total number of iterations.

[0020] The method of this invention anchors the correlation of reservoir parameters to the genesis of sedimentary facies zones, couples the vertical evolution model of facies zones with the overall relationship model describing the relationship between reservoir depth and accumulated rock volume, and obtains a facies-controlled rock volume model for each facies zone, thereby realizing the facies-controlled allocation of reservoir volume. Finally, sampling is based on the probability distribution model of reservoir parameters specific to each facies zone to ensure that the parameter combination conforms to the geological characteristics of the facies zone, avoiding the problem of unreasonable parameter combinations in the traditional Monte Carlo method, and making the resource calculation results highly consistent with the reservoir geological laws.

[0021] Furthermore, by coupling the vertical evolution model of the sedimentary facies zone with the overall relationship model, a sub-relationship model describing the relationship between reservoir depth and accumulated rock volume for each sedimentary facies zone is obtained, including:

[0022] For any reservoir depth in the overall relationship model, the reservoir depth is converted into a height value in the vertical evolution model of the facies zone, and the volume ratio of each sedimentary facies zone is obtained by querying the vertical evolution model of the facies zone based on the height value.

[0023] The cumulative rock volume corresponding to the reservoir depth is allocated according to the volume ratio of each sedimentary facies zone to obtain the cumulative rock volume corresponding to each sedimentary facies zone;

[0024] Based on the cumulative rock volume corresponding to each sedimentary facies zone at any reservoir depth, a sub-relationship model describing the relationship between reservoir depth and cumulative rock volume for each sedimentary facies zone is obtained.

[0025] Furthermore, the phase zone vertical evolution model adopts a normalized reservoir height.

[0026] Furthermore, based on the physical properties of the sedimentary facies zones, probability distribution models of reservoir parameters for each sedimentary facies zone are established, including:

[0027] Based on the core analysis data and logging interpretation results of the drilled wells in the target reservoir area and adjacent areas, the data points of each physical property parameter are classified into the corresponding sedimentary facies zone, and parameter datasets for each sedimentary facies zone are established.

[0028] For each physical property parameter in the parameter dataset, multiple probability distributions are used to fit the data points to obtain multiple probability distribution models corresponding to each physical property parameter;

[0029] For each of the physical property parameters, multiple probability distribution models are fitted and validated, and the probability distribution model with the best fitting result is selected as the final probability distribution model of the physical property parameter.

[0030] By combining the final probability distribution models of each of the physical properties parameters of each of the sedimentary facies zones, the probability distribution models of reservoir parameters of each of the sedimentary facies zones are obtained.

[0031] Further, for each of the said physical property parameters, multiple probability distribution models are fitted and validated, and the probability distribution model with the best fitting result is selected as the final probability distribution model of the physical property parameter, including:

[0032] The Kolmogorov-Smirnov test was used to calculate the significance level of fit for each probability distribution model;

[0033] The AIC value of each probability distribution model was calculated using the Akaike Information Criterion formula;

[0034] The AIC value is linearly standardized and mapped to the 0-1 rating range so that the lowest AIC value corresponds to the highest rating.

[0035] The significance level value and score of the fit of each probability distribution model are calculated by weighting, and a comprehensive score is obtained. The probability distribution model with the highest comprehensive score is selected as the final probability distribution model of the physical property parameter.

[0036] Furthermore, a three-dimensional geological entity of the target reservoir is constructed, and a general relationship model describing the relationship between reservoir depth and accumulated rock volume is obtained based on the three-dimensional geological entity, including:

[0037] Using seismic reflection data, the top interface, bottom interface, and spatial boundary of the target reservoir are determined, and a three-dimensional geological entity is constructed through spatiotemporal transformation.

[0038] The correspondence between reservoir depth and cumulative rock volume is extracted from the three-dimensional geological entity, and a general relationship model is constructed based on the correspondence. The general relationship model describes the cumulative rock volume from the top layer of the three-dimensional geological entity to any reservoir depth.

[0039] Furthermore, the method also includes: obtaining the overestimated reserve P10, the median reserve P50, and the conservative reserve value P90 based on the probability distribution map of oil and gas resources; wherein, in the probability distribution map of oil and gas resources, 10% of the oil and gas resources exceed P10, 50% of the oil and gas resources exceed P50, and 90% of the oil and gas resources exceed P90.

[0040] A second aspect of the present invention provides an oil and gas resource assessment device based on a phase control model, comprising:

[0041] The first modeling module is used to establish a vertical evolution model of the sedimentary facies zones based on the sedimentary facies zones within the target reservoir.

[0042] The second modeling module is used to construct a three-dimensional geological entity of the target reservoir and obtain a general relationship model describing the relationship between reservoir depth and accumulated rock volume based on the three-dimensional geological entity.

[0043] The model coupling module is used to couple the vertical evolution model of the facies zone with the overall relationship model to obtain a sub-relationship model describing the relationship between reservoir depth and cumulative rock volume for each sedimentary facies zone.

[0044] The third modeling module is used to establish the probability distribution model of reservoir parameters for each sedimentary facies zone based on the physical properties of the sedimentary facies zone.

[0045] The iterative calculation module is used to calculate oil and gas resource quantities using the Monte Carlo simulation iterative method; each iteration includes:

[0046] (1) Randomly extract the oil-water interface depth under the boundary constraints of the three-dimensional geological entity;

[0047] (2) Based on the sub-relationship model, the cumulative rock volume of each sedimentary facies zone at the depth of the oil-water interface is obtained;

[0048] (3) Randomly extract the combination of physical property parameters of each sedimentary facies zone from the reservoir parameter probability distribution model of each sedimentary facies zone;

[0049] (4) Calculate the oil and gas resources by using the cumulative rock volume and physical property parameters of each sedimentary facies zone according to the standard geological resource volume method formula;

[0050] The resource evaluation module is used to obtain a probability distribution map of oil and gas resources based on the oil and gas resource quantities calculated through N iterations, where N is the total number of iterations.

[0051] A third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the oil and gas resource quantity evaluation method based on a phase control model as described in any one of the first aspects of the present invention.

[0052] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the oil and gas resource quantity evaluation method based on a phase control model as described in any one of the first aspects of the present invention.

[0053] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0054] (1) The method of the present invention does not require the construction of a full three-dimensional geological model. It only constructs a simplified three-dimensional geological entity and extracts the total rock volume model through seismic data, and then couples it with a regional facies vertical evolution model, which greatly reduces the dependence on dense well locations and high-quality seismic data. It is especially suitable for the early stage of exploration with limited data.

[0055] (2) Monte Carlo iterative calculation focuses on hierarchical sampling. The calculation process is simple and efficient. It can complete tens of thousands of iterations in tens of seconds. Compared with the three-dimensional random modeling method, the efficiency is significantly improved, which is suitable for the needs of sparse data and short decision-making cycle in the early stage of exploration.

[0056] (3) This invention integrates uncertainty in multiple dimensions: First, it randomly extracts the depth of the oil-water interface under the constraint of the three-dimensional geological entity boundary to cover the uncertainty of the macroscopic reservoir boundary; Second, it independently samples from the phase parameter probability model to cover the randomness of the physical property parameters; Finally, it outputs the probability distribution map of oil and gas resources through N iterations, which intuitively presents the probability range of different resource values. Compared with the single numerical result of the traditional deterministic method, it can significantly improve the scientific nature of risk assessment. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0058] Figure 1 This is a flowchart of a method for evaluating oil and gas resources based on a phase control model;

[0059] Figure 2 This is a technical roadmap for evaluating the oil and gas resources in exploration area A.

[0060] Figure 3 This is a schematic diagram illustrating the vertical evolution of carbonate particle beach complexes;

[0061] Figure 4 This is a schematic diagram of the vertical evolution model of the facies zone of the target reservoir in exploration block A;

[0062] Figures 5A-5D This is a schematic diagram of porosity data fitting under different fitting models;

[0063] Figure 6 This is a schematic diagram of a seismic profile of a target reservoir in exploration block A.

[0064] Figure 7A This is a schematic diagram of the total rock volume-depth relationship model of the target reservoir in exploration block A.

[0065] Figure 7B This is a schematic diagram of the volume-depth relationship model of the beach core facies rocks of the target reservoir in a certain exploration block A.

[0066] Figure 7C This is a schematic diagram of the volume-depth relationship model of the beach flank facies rocks of the target reservoir in a certain exploration block A.

[0067] Figure 8 This is a probability distribution map of oil and gas resources in the target reservoir of a certain exploration block A. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0069] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims, and accompanying drawings of this invention are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to other steps or units inherent in the device.

[0070] The terminology used in the various embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to limit the various embodiments of the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of the invention pertain. The terms (such as those defined in commonly used dictionaries) are to be interpreted as having the same meaning as in the context of the relevant technical field and are not to be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of the invention.

[0071] The purpose of this invention is to provide a method for evaluating oil and gas resources based on a phase control model. It aims to establish a new evaluation paradigm that closely couples geological genesis with probability statistics, fundamentally solving the problem of discrepancies between calculation results and geological reality caused by the "parameter independence fallacy" in the background technology. At the same time, it significantly improves the efficiency and reliability of resource evaluation under the condition of limited data in the early stages of exploration.

[0072] See Figure 1 , Figure 1 The diagram shows a flowchart of an oil and gas resource quantity evaluation method based on a phase control model according to an embodiment of the present invention.

[0073] Step 1: Establish a vertical evolution model of the sedimentary facies zones based on the sedimentary facies zones within the target reservoir.

[0074] This step aims to mathematize regional sedimentological patterns and establish a standardized a priori model that can characterize the vertical evolution of facies zones within a reservoir. Specifically, it includes the following sub-steps.

[0075] (1) Geological pattern analysis: Based on regional geological studies of the basin or region, well data and sedimentological theory, identify the main sedimentary facies zone types in the target reservoir, and analyze and summarize the systematic evolution or superposition patterns of these facies zones in the vertical direction.

[0076] (2) Model quantification: The above-mentioned qualitative geological evolution laws are mathematically quantified to establish a function or chart with reservoir height as the independent variable and the percentage of rock volume occupied by each major sedimentary facies zone as the dependent variable. This model is the facies zone vertical evolution model, which is a quantitative expression of sedimentary laws and the core geological template for subsequent volume allocation.

[0077] Preferably, the vertical evolution model of the phasor zone is standardized, that is, the independent variable adopts the normalized reservoir height. For example, the top of the reservoir is counted as 0 and the bottom of the reservoir is counted as 100%. The normalized reservoir height is expressed as the percentage of the actual height to the total height.

[0078] Step 2: Construct a three-dimensional geological entity of the target reservoir, and obtain a general relationship model describing the relationship between reservoir depth and accumulated rock volume based on the three-dimensional geological entity.

[0079] This step aims to apply the regional, universal evolutionary model established in the first step to a specific exploration target, construct a phase-controlled rock volume model for that specific target, and thus generate a phase-separated rock volume distribution model within that specific target.

[0080] Specifically, the method for constructing a three-dimensional geological entity involves using seismic reflection data to interpret and delineate the top, bottom, and spatial boundaries of the exploration target (i.e., the target reservoir), and then constructing a three-dimensional geological entity through spatiotemporal transformation. The correspondence between reservoir depth and accumulated rock volume is extracted from this entity and represented as a depth-volume curve or data table, thus obtaining the overall relationship model. This model describes the accumulated rock volume from the top layer of the three-dimensional geological entity to any reservoir depth.

[0081] Step 3: Couple the vertical evolution model of the sedimentary facies zone with the overall relationship model to obtain a sub-relationship model describing the relationship between reservoir depth and cumulative rock volume in each sedimentary facies zone.

[0082] Specifically, the "facies zone vertical evolution model" established in the first step is coupled with the "total rock volume-depth relationship" obtained in the previous step. The coupling method includes:

[0083] For any reservoir depth in the overall relational model, the reservoir depth is converted into a height value (or normalized height) in the facies vertical evolution model. Based on the height value, the volume ratio of each sedimentary facies zone is obtained from the facies vertical evolution model. Then, the cumulative rock volume corresponding to the reservoir depth is allocated according to the volume ratio of each sedimentary facies zone, thus obtaining the cumulative rock volume corresponding to each sedimentary facies zone. Finally, based on the cumulative rock volume corresponding to each sedimentary facies zone at any reservoir depth, a sub-relationship model describing the relationship between reservoir depth and cumulative rock volume for each sedimentary facies zone is obtained.

[0084] The accumulated rock volume is calculated from top to bottom, eventually generating several independent "phase rock volume-depth" relationships that describe each phase zone.

[0085] Step 4: Based on the physical properties of the sedimentary facies zones, establish probability distribution models of reservoir parameters for each sedimentary facies zone.

[0086] This step aims to establish a set of reservoir parameter probability distribution models for each phase type identified in the first step, accurately reflecting its physical properties. This effectively overcomes the subjectivity in the selection of distribution types in traditional methods. The specific process is as follows.

[0087] S4-1: Based on the core analysis data and logging interpretation results of the drilled wells in the target reservoir area and adjacent areas, classify the data points of each physical property parameter (such as porosity, net-to-gross ratio, and oil saturation) to their respective sedimentary facies zones and establish parameter datasets for each sedimentary facies zone.

[0088] S4-2, for each physical property parameter in each parameter dataset, the data points are fitted using a probability distribution model to obtain the probability distribution model corresponding to each physical property parameter.

[0089] S4-3 combines the probability distribution models of each physical property parameter of each sedimentary facies zone to obtain the probability distribution model of reservoir parameters of each sedimentary facies zone, which is also a set of probability distribution models corresponding to multiple physical property parameters.

[0090] Preferably, in step S4-2, for each physical property parameter, multiple probability distributions are used to fit its data points to obtain multiple corresponding probability distribution models. For example, the maximum likelihood estimation method is used to fit multiple common probability distributions: normal distribution, Beta distribution, triangular distribution, etc. Then, the multiple probability distribution models corresponding to each physical property parameter are fitted and verified separately, and the probability distribution model with the best fitting result is selected as the final probability distribution model of that physical property parameter. Finally, the final probability distribution models corresponding to the physical property parameters contained in each sedimentary facies are combined to obtain the reservoir parameter probability distribution model of each sedimentary facies.

[0091] Furthermore, an automatic optimization method based on a multi-criteria weighted scoring probability distribution model is employed. The method for calculating the score for each probability distribution model is as follows.

[0092] (1) The Kolmogorov-Smirnov test was used to calculate the significance level (p-value) of the probability distribution model fit.

[0093] (2) Calculate the AIC value of the probability distribution model using the Akaike Information Criterion formula; expressed as:

[0094]

[0095] Where k is the number of model parameters, and L is the maximum likelihood function value of the model. The smaller the AIC value, the simpler the model is while ensuring good fit, thus avoiding overfitting.

[0096] (3) The AIC value is linearly standardized and mapped to the 0-1 score range so that the lowest AIC value corresponds to the highest score.

[0097] (4) Calculate the weighted significance level value and score to obtain the comprehensive score.

[0098] (5) Select the probability distribution model with the highest comprehensive score among the multiple probability distribution models corresponding to each physical property parameter as the final probability distribution model of that physical property parameter.

[0099] The p-value is directly used as the goodness-of-fit score; a larger p-value indicates a better fit between the data and the theoretical assumptions of the distribution. For weighted scoring, to ensure uniformity of measurement, the AIC value is first linearly standardized, mapping it to a 0-1 scoring range. Then, the p-value and AIC scores are weighted and summed to calculate the overall score for each distribution. For example, the p-value score is used as the primary weight (60%) to ensure the statistical acceptability of the distribution assumptions, while the AIC score is used as a secondary weight (40%) to prevent overcomplication of the model. Finally, the distribution with the highest overall score is automatically selected as the optimal probability distribution model for that parameter, and its characteristic parameters, such as mean and standard deviation, are determined.

[0100] Step 5: Calculate oil and gas resources using the Monte Carlo simulation iterative method.

[0101] The Monte Carlo simulation iterative method is employed to systematically integrate the uncertainties of all geological and physical parameters in oil and gas reservoirs through multiple independent random sampling and calculation cycles, ultimately generating a probability distribution of total geological resources. The following layered calculations are performed in each iteration.

[0102] (1) First, the oil-water interface depth OWC is randomly selected under the boundary constraints of the three-dimensional geological entity.

[0103] (2) Based on the sub-relationship model established in step three, the oil-water interface depth OWC is extracted and substituted into the sub-relationship model to obtain the cumulative rock volume of each sedimentary facies zone at the oil-water interface depth.

[0104] (3) Randomly extract the combination of physical property parameters of each sedimentary facies zone from the reservoir parameter probability distribution model of each sedimentary facies zone.

[0105] Independently and randomly select a set of physical property parameters, such as: net-to-wool ratio, average effective porosity, and average oil saturation.

[0106] (4) Calculate the oil and gas resources by using the cumulative rock volume and physical property parameters of each sedimentary facies zone according to the standard geological resource volume method formula.

[0107] The formula for the standard geological resource volume method is as follows:

[0108] STOIIP_facies= (( GRV_facies ×( N / G_facies )× Porosity_facies × So_ facies ) / Bo )× C

[0109] In the formula, STOIIP_facies : The amount of original geological resources on the surface of a specific facies zone, in buckets (bbl).

[0110] GRV_facies : The effective total rock volume of a specific facies zone, in cubic meters ( );

[0111] N / G_facies : The net-to-gross ratio for a specific phase zone, which is the ratio of net reservoir thickness to total reservoir thickness, and is dimensionless;

[0112] Porosity_facies : The average effective porosity of a specific phase zone, which is the ratio of pore volume to rock volume, and is dimensionless;

[0113] So_facies : The average oil saturation of a specific phase zone, dimensionless;

[0114] Bo : is the crude oil volume coefficient, which is the ratio of underground oil and gas volume to surface degassed oil volume, and is dimensionless;

[0115] C : This is a unit conversion factor used to convert cubic meters to barrels; its value is 6.2898 bbl / .

[0116] Repeat steps (1) to (4) until the maximum number of iterations N is reached.

[0117] After calculating the geological resources of each facies zone within the oil column using the standard geological resource volume method formula, the resources of all facies zones are summed to obtain the total geological resources for this iteration.

[0118] Step 6: Based on the oil and gas resource quantity calculated through N iterations, obtain the probability distribution map of oil and gas resource quantity.

[0119] This graph statistically sorts the total resource amounts from all single iterations, visually demonstrating the probability that the resource amount is greater than or equal to a specific value. Based on this, key risk assessment indicators are identified.

[0120] Furthermore, based on the probability distribution map of oil and gas resources, we obtained the overestimated reserves P10, the median reserves P50, and the conservative reserves P90.

[0121] P90 (Conservative Reserve Value): There is a 90% probability that the resource quantity will exceed this value, representing a highly certain reserve baseline.

[0122] P50 (Median Reserves): There is a 50% probability that the resource quantity will exceed this value. It represents the median prediction of the reserves and is the most likely reserve.

[0123] P10 (Overvalued Reserves): There is only a 10% probability that the resource quantity will exceed this value, representing an overvalued reserve under favorable conditions.

[0124] The following uses an exploration block A as an example to assess its oil and gas resources using the evaluation method of this invention. See the technical roadmap below. Figure 2 As shown.

[0125] (1) First, establish the phase zone vertical evolution model of block A.

[0126] Based on regional geological studies, sedimentological analysis, and statistical analysis of drilling data from surrounding developed oilfields, the Y Formation was identified as a grain-shoal complex sedimentary formation in a shallow-water carbonate rock slope setting. The reservoirs are primarily developed within the grain-shoal facies, which can be further subdivided into a high-energy "shoal core" facies and a medium- to low-energy "shoal flank" facies.

[0127] Studies have shown that this type of shoal exhibits a clear vertical evolution pattern, such as... Figure 3 As shown: In the initial stage of beach body development (lower part of the reservoir), the hydrodynamics are relatively weak, and the beach flank facies are the main deposits; as the sea level relatively drops and the hydrodynamics increase, the proportion of the beach core facies gradually increases, while the proportion of the beach flank facies decreases accordingly; at the end of the depositional period (top of the reservoir), the beach body is in the highest energy environment, and the sediments are almost entirely composed of the beach core facies.

[0128] By thoroughly understanding the drilling and seismic data from surrounding developed areas and quantifying this geological understanding, a universally applicable vertical evolution model of facies zones for the region can be established, such as... Figure 4 As shown, this model takes normalized height as input and outputs the volume percentage of the beach core facies and beach flank facies at any height. It will be used as a geological constraint in the evaluation of any specific beach target in the region.

[0129] (2) Establish a probability database of phase attributes.

[0130] Formation data of Group Y from 24 wells in Block A and its surrounding area were collected. Based on core and logging characteristics, the data were divided into beach core facies and beach flank facies. A total of 564 porosity and oil saturation data points and 22 net-to-gross ratio data points were obtained for the beach core facies; and 756 porosity and oil saturation data points and 25 net-to-gross ratio data points for the beach flank facies.

[0131] Taking the porosity of the beach-nuclear facies as an example, a multi-criteria weighted scoring optimization method was used to test four common probability distributions: normal distribution, log-normal distribution, triangular distribution, and uniform distribution. The fitting results are shown in the figure below. Figures 5A-5D As shown in Table 1, the comprehensive scoring results indicate that the normal distribution has the highest comprehensive score. Therefore, the porosity of the beach-nuclear facies is determined to follow a normal distribution with a mean μ of 19.96% and a standard deviation σ of 2.93%.

[0132] Table 1

[0133]

[0134] Using the same method, the optimal probability distribution model for each parameter was finally determined as follows:

[0135] Beach-core facies: porosity follows a normal distribution (μ=19.96%, σ=2.93%); oil saturation follows a normal distribution (μ=80.42%, σ=5.16%); net-to-gross ratio follows a triangular distribution (minimum=0.75, most likely=0.92, maximum=1.0).

[0136] Beach facies: Porosity follows a normal distribution (μ=14.36%, σ=4.35%); oil saturation follows a normal distribution (μ=65.12%, σ=10.32%); net-to-gross ratio follows a triangular distribution (minimum=0.4, most likely=0.75, maximum=0.95).

[0137] (3) Construct phase-controlled rock volume models for specific targets.

[0138] Within Block A, a shoal target was identified in Formation Y through comprehensive analysis of single-well and 3D seismic data volumes. This target is shown in the seismic profile as follows: Figure 6As shown, it exhibits a set of strong amplitude, continuous mound-shaped reflectors with a significantly different internal structure from the surrounding intershoal sediments and clear geophysical response characteristics.

[0139] Based on these geophysical response characteristics, specialized seismic interpretation software (such as Landmark and Petrel) was used to meticulously characterize the top and bottom reflection interfaces of the beach body through three-dimensional spatial tracking and attribute analysis. Subsequently, a time-depth conversion was performed on the velocity model established based on well-seismic calibration to generate depth grid data representing the top and bottom interfaces of the beach body, determining the depth of the top interface to be -2449m and the depth of the bottom interface to be -2775m. By calculating the space between these two depth grids, a three-dimensional geological entity of the target was constructed, and the "total rock volume-depth" relationship was extracted from it, such as... Figure 7A As shown, its total rock volume is 8.5 × 10¹ 0 .

[0140] Subsequently, the grain shoal "facies vertical evolution model" established in the first step was applied to the volumetric model of this target. Through coupled calculations, the total rock volume was proportionally distributed layer by layer vertically to the core and flank facies, ultimately obtaining the target-specific, facies-specific "rock volume-depth" relationship, as shown below. Figure 7B and 7C As shown. These two relationships precisely describe the cumulative rock volume contributed by the core facies and flank facies respectively within the target, from top to arbitrary depth, and are direct inputs for subsequent probabilistic calculations of resource quantities.

[0141] (4) Probabilistic iterative calculation based on phase splitting model.

[0142] Studies of the Y-formation reservoir in Block A show that the hydrocarbon saturation (which determines the location of the oil-water interface) of the shoal body exhibits a triangular distribution, ranging from 60% to 90%, with the most likely value being 70%. This corresponds to an oil-water interface (OWC) depth distribution between -2744m and -2643m, most likely at -2676m. The crude oil volume factor is determined to be 1.4985.

[0143] Now we will simulate one of the iterations.

[0144] (1) Assume that the depth of OWC extracted in this iteration is -2666m.

[0145] (2) Based on this OWC, utilize Figure 7B and 7C The volume-depth relationship of the phase separations shown indicates that the effective rock volume of the shoal core facies within the oil column is approximately 2.2 × 10¹. 0 The effective rock volume of the beach facies is approximately 9.3 × 10⁻⁶. 9 .

[0146] (3) Next, samples are taken from the database established in the second step for the two phase zones respectively: it is assumed that the net-to-gross ratio of the beach core phase is 0.95, the porosity is 20.5%, and the oil saturation is 90%; the net-to-gross ratio of the beach wing phase is 0.85, the porosity is 16.3%, and the oil saturation is 83%.

[0147] (4) Calculation of resource quantity: The resource quantity of the beach core phase is 1620 MMbbl, the resource quantity of the beach wing phase is 380 MMbbl, and the total resource quantity of this iteration is 2000 MMbbl.

[0148] (5) Perform 10,000 such iterations, and sort the 10,000 total resource quantities from low to high, as shown in the following figure. Figure 8 As shown. The final result is:

[0149] P90: 13348 MMbbl;

[0150] P50: 17872 MMbbl;

[0151] P10: 24692 MMbbl;

[0152] Mean: 18538 MMbbl.

[0153] Embodiments of the present invention also provide an oil and gas resource assessment device based on a phase control model, used to execute the oil and gas resource assessment method based on a phase control model according to any of the above embodiments of the present invention. The device includes:

[0154] The first modeling module is used to establish a vertical evolution model of the sedimentary facies zones based on the sedimentary facies zones within the target reservoir.

[0155] The second modeling module is used to construct a three-dimensional geological entity of the target reservoir and obtain a general relationship model describing the relationship between reservoir depth and accumulated rock volume based on the three-dimensional geological entity.

[0156] The model coupling module is used to couple the vertical evolution model of the facies zone with the overall relationship model to obtain a sub-relationship model describing the relationship between reservoir depth and cumulative rock volume for each sedimentary facies zone.

[0157] The third modeling module is used to establish the probability distribution model of reservoir parameters for each sedimentary facies zone based on the physical properties of the sedimentary facies zone.

[0158] The iterative calculation module is used to calculate oil and gas resource quantities using the Monte Carlo simulation iterative method; each iteration includes:

[0159] (1) Randomly extract the depth of the oil-water interface under the boundary constraints of the three-dimensional geological entity;

[0160] (2) Based on the sub-relationship model, the cumulative rock volume of each sedimentary facies zone at the oil-water interface depth is obtained;

[0161] (3) Randomly extract the combination of physical property parameters of each sedimentary facies zone from the probability distribution model of reservoir parameters of each sedimentary facies zone;

[0162] (4) Calculate oil and gas resources by combining the cumulative rock volume and physical property parameters of each sedimentary facies zone according to the standard geological resource volume method formula;

[0163] The resource evaluation module is used to obtain a probability distribution map of oil and gas resources based on the oil and gas resource quantities calculated through N iterations, where N is the total number of iterations.

[0164] Furthermore, the resource evaluation module is also used to obtain the overestimated reserves P10, the median reserves P50, and the conservative reserves P90 based on the probability distribution map of oil and gas resources.

[0165] Embodiments of the present invention also provide an electronic device, which includes a processor and a memory, wherein the number of processors may be one or more. The memory, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules. The processor executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory, thereby realizing the phase control model-based oil and gas resource evaluation method of any of the above embodiments of the present invention.

[0166] The memory may primarily comprise a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, the memory may include high-speed random access memory (RAM) and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the memory may further include memory remotely located relative to the processor, which can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks (LANs), mobile communication networks, and combinations thereof.

[0167] Embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the oil and gas resource quantity evaluation method based on a phase control model according to any embodiment of the present invention.

[0168] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0169] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0170] Embodiments of the present invention also provide a computer program product that, when run on a computer, causes the computer to execute the oil and gas resource quantity evaluation method based on phase control model of any of the above embodiments of the present invention.

[0171] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating oil and gas resources based on a phase control model, characterized in that, include: Based on the sedimentary facies zones within the target reservoir, establish a vertical evolution model of the facies zones; A three-dimensional geological entity of the target reservoir is constructed, and a general relationship model describing the relationship between reservoir depth and accumulated rock volume is obtained based on the three-dimensional geological entity. By coupling the vertical evolution model of the facies zone with the overall relationship model, a sub-relationship model describing the relationship between reservoir depth and cumulative rock volume for each sedimentary facies zone is obtained. Based on the physical properties of sedimentary facies zones, probability distribution models of reservoir parameters for each sedimentary facies zone are established. The Monte Carlo simulation iterative method is used to calculate oil and gas resources; each iteration includes the following calculations: (1) Randomly extract the oil-water interface depth under the boundary constraints of the three-dimensional geological entity; (2) Based on the sub-relationship model, the cumulative rock volume of each sedimentary facies zone at the depth of the oil-water interface is obtained; (3) Randomly extract the combination of physical property parameters of each sedimentary facies zone from the reservoir parameter probability distribution model of each sedimentary facies zone; (4) Calculate the oil and gas resources by using the cumulative rock volume and physical property parameters of each sedimentary facies zone according to the standard geological resource volume method formula; Based on the oil and gas resource quantity calculated through N iterations, a probability distribution map of oil and gas resource quantity is obtained, where N is the total number of iterations.

2. The method for evaluating oil and gas resources based on a phase control model according to claim 1, characterized in that, By coupling the vertical evolution model of the sedimentary facies zone with the overall relationship model, a sub-relationship model describing the relationship between reservoir depth and accumulated rock volume for each sedimentary facies zone is obtained, including: For any reservoir depth in the overall relationship model, the reservoir depth is converted into a height value in the vertical evolution model of the facies zone, and the volume ratio of each sedimentary facies zone is obtained by querying the vertical evolution model of the facies zone based on the height value. The cumulative rock volume corresponding to the reservoir depth is allocated according to the volume ratio of each sedimentary facies zone to obtain the cumulative rock volume corresponding to each sedimentary facies zone; Based on the cumulative rock volume corresponding to each sedimentary facies zone at any reservoir depth, a sub-relationship model describing the relationship between reservoir depth and cumulative rock volume for each sedimentary facies zone is obtained.

3. The method for evaluating oil and gas resources based on a phase control model according to claim 1 or 2, characterized in that, The phase zone vertical evolution model uses a normalized reservoir height.

4. The method for evaluating oil and gas resources based on a phase control model according to claim 1, characterized in that, Based on the physical properties of sedimentary facies zones, probability distribution models of reservoir parameters for each sedimentary facies zone are established, including: Based on the core analysis data and logging interpretation results of the drilled wells in the target reservoir area and adjacent areas, the data points of each physical property parameter are classified into the corresponding sedimentary facies zone, and parameter datasets for each sedimentary facies zone are established. For each physical property parameter in the parameter dataset, multiple probability distributions are used to fit the data points to obtain multiple probability distribution models corresponding to each physical property parameter; For each of the physical property parameters, multiple probability distribution models are fitted and validated, and the probability distribution model with the best fitting result is selected as the final probability distribution model of the physical property parameter. By combining the final probability distribution models of each of the physical properties parameters of each of the sedimentary facies zones, the probability distribution models of reservoir parameters of each of the sedimentary facies zones are obtained.

5. The method for evaluating oil and gas resources based on a phase control model according to claim 4, characterized in that, For each of the aforementioned physical property parameters, multiple probability distribution models are fitted and validated. The probability distribution model with the best fitting result is selected as the final probability distribution model for the physical property parameter, including: The Kolmogorov-Smirnov test was used to calculate the significance level of fit for each probability distribution model; The AIC value of each probability distribution model was calculated using the Akaike Information Criterion formula; The AIC value is linearly standardized and mapped to the 0-1 rating range so that the lowest AIC value corresponds to the highest rating. The significance level value and score of the fit of each probability distribution model are calculated by weighting, and a comprehensive score is obtained. The probability distribution model with the highest comprehensive score is selected as the final probability distribution model of the physical property parameter.

6. The method for evaluating oil and gas resources based on a phase control model according to claim 1, characterized in that, Construct a three-dimensional geological entity of the target reservoir, and based on the three-dimensional geological entity, obtain a general relationship model describing the relationship between reservoir depth and accumulated rock volume, including: Using seismic reflection data, the top interface, bottom interface, and spatial boundary of the target reservoir are determined, and a three-dimensional geological entity is constructed through spatiotemporal transformation. The correspondence between reservoir depth and cumulative rock volume is extracted from the three-dimensional geological entity, and a general relationship model is constructed based on the correspondence. The general relationship model describes the cumulative rock volume from the top layer of the three-dimensional geological entity to any reservoir depth.

7. The method for evaluating oil and gas resources based on a phase control model according to claim 1, characterized in that, The method further includes: obtaining the overestimated reserve P10, the median reserve P50, and the conservative reserve value P90 based on the probability distribution map of oil and gas resources; wherein, in the probability distribution map of oil and gas resources, 10% of the oil and gas resources exceed P10, 50% of the oil and gas resources exceed P50, and 90% of the oil and gas resources exceed P90.

8. An oil and gas resource assessment device based on a phase control model, characterized in that, include: The first modeling module is used to establish a vertical evolution model of the sedimentary facies zones based on the sedimentary facies zones within the target reservoir. The second modeling module is used to construct a three-dimensional geological entity of the target reservoir and obtain a general relationship model describing the relationship between reservoir depth and accumulated rock volume based on the three-dimensional geological entity. The model coupling module is used to couple the vertical evolution model of the facies zone with the overall relationship model to obtain a sub-relationship model describing the relationship between reservoir depth and cumulative rock volume for each sedimentary facies zone. The third modeling module is used to establish the probability distribution model of reservoir parameters for each sedimentary facies zone based on the physical properties of the sedimentary facies zone. The iterative calculation module is used to calculate oil and gas resource quantities using the Monte Carlo simulation iterative method; each iteration includes: (1) Randomly extract the oil-water interface depth under the boundary constraints of the three-dimensional geological entity; (2) Based on the sub-relationship model, the cumulative rock volume of each sedimentary facies zone at the depth of the oil-water interface is obtained; (3) Randomly extract the combination of physical property parameters of each sedimentary facies zone from the reservoir parameter probability distribution model of each sedimentary facies zone; (4) Calculate the oil and gas resources by using the cumulative rock volume and physical property parameters of each sedimentary facies zone according to the standard geological resource volume method formula; The resource evaluation module is used to obtain a probability distribution map of oil and gas resources based on the oil and gas resource quantities calculated through N iterations, where N is the total number of iterations.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the oil and gas resource quantity evaluation method based on the phase control model as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the oil and gas resource quantity evaluation method based on the phase control model as described in any one of claims 1-7.

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