Construction method and application of gas well productivity splitting model

Abstract

The present invention relates to a method for constructing a gas well productivity splitting model and its application, and relates to the field of gas reservoir engineering technology. The method comprises: obtaining the test gas production of multiple single layers in a sample gas well and the measurement data of productivity influencing factors to be determined, wherein the sample gas well includes reservoirs of different gas reservoir types; determining the target influencing factors of each gas reservoir type according to the test gas production and the measurement data; constructing a productivity splitting model according to the target influencing factors, wherein the productivity splitting model is used to split the productivity of gas wells including reservoirs of multiple gas reservoir types. In this way, the target influencing factors used to construct the productivity splitting model are more consistent with the actual productivity influencing factors of reservoirs of different gas reservoir types, so that the constructed productivity splitting model can be used to perform productivity splitting on gas wells including reservoirs of different gas reservoir types, so as to improve the accuracy of the single-layer productivity splitting results of reservoirs of different gas reservoir types.

Description

Technical Field

[0001] The present invention relates to the technical field of gas reservoir engineering, and in particular to a method for constructing a gas well productivity splitting model and its application. Background Art

[0002] Production splitting based on multi-layer pressure-separated combined tests is a challenge that hinders gas well productivity evaluation. Conventional methods for splitting gas well productivity focus solely on the production of multi-layer combined tests within a single reservoir type. However, reservoir spaces and seepage mechanisms vary significantly across different reservoir types, leading to significant discrepancies between the production split and the actual gas production of the reservoir. Summary of the Invention

[0003] In order to overcome the problems existing in the related art, the present invention provides a method for constructing a gas well productivity splitting model and its application.

[0004] In a first aspect, the present invention provides a method for constructing a gas well productivity splitting model, comprising:

[0005] Obtaining measurement data of test gas production and factors influencing production to be determined for multiple individual layers in a sample gas well, wherein the sample gas well includes reservoirs of different gas reservoir types;

[0006] determining target influencing factors of the reservoir of each gas reservoir type based on the test gas production and the measurement data;

[0007] According to the target influencing factors, a productivity splitting model is constructed, and the productivity splitting model is used to split the productivity of gas wells including reservoirs of multiple gas reservoir types.

[0008] Optionally, determining target influencing factors of the reservoir of each gas reservoir type based on the test gas production and the measurement data includes:

[0009] For the reservoir of the target gas reservoir type, determining a weighted coefficient value for each reservoir based on the measurement data;

[0010] determining a correlation coefficient between each productivity influencing factor and the test gas production according to the weighted coefficient value, the test gas production, and the measurement data;

[0011] According to the correlation coefficient, target influencing factors of the reservoir of the target gas reservoir type are determined.

[0012] Optionally, the productivity influencing factors include acoustic transit time and effective thickness, and determining a weighted coefficient value for each reservoir based on the measurement data includes:

[0013] For the reservoirs of the target gas reservoir type, determining the total thickness of all reservoirs of the target gas reservoir type and the target thicknesses of multiple reservoirs within the same acoustic transit time interval based on the acoustic transit time interval of each reservoir and the effective thickness measurement data of each reservoir;

[0014] The weighting coefficient value of each reservoir is determined according to the ratio of the target thickness corresponding to different acoustic wave time difference intervals to the total thickness.

[0015] Optionally, determining a correlation coefficient between each productivity influencing factor and the test gas production according to the weighted coefficient value, the test gas production, and the measurement data includes:

[0016] For the reservoir of the target gas reservoir type, determining a weighted average value of each productivity influencing factor for each reservoir according to the weighted coefficient value of each reservoir and the measurement data;

[0017] According to the test gas production of each reservoir in the target gas reservoir type and the weighted average value, a correlation coefficient between each productivity influencing factor and the test gas production is determined.

[0018] In a second aspect, the present invention provides a gas well productivity splitting method, comprising:

[0019] Obtaining the total gas production of the gas well to be split, as well as geological factor data and fracturing parameter data corresponding to multiple single layers in the gas well to be split, wherein the gas well to be split includes reservoirs of different gas reservoir types;

[0020] According to the total gas production, the geological factor data, the fracturing parameter data and the productivity splitting model, the gas production of each single layer after splitting is obtained, wherein the productivity splitting model is obtained by the method for constructing the gas well productivity splitting model described in the first aspect.

[0021] Optionally, the productivity splitting model includes an energy storage splitting coefficient, a permeability splitting coefficient, and a transformation splitting coefficient; the target influencing factors include geological factors and fracturing parameters; the multiple single layers include a tight sandstone type reservoir and a carbonate rock type reservoir; the geological factors of the tight sandstone type reservoir and the geological factors of the carbonate rock type reservoir both include a well logging gas measurement amplitude value, a sonic wave time difference, a permeability, and an effective thickness; the fracturing parameters of the tight sandstone type reservoir include a construction displacement; the fracturing parameters of the carbonate rock type reservoir include a first acid injection displacement and a second acid injection displacement; the first acid injection displacement and the second acid injection displacement correspond to different injection pressures;

[0022] The gas production of each single layer after splitting is obtained according to the total gas production, the geological factor data, the fracturing parameter data and the productivity splitting model, including:

[0023] For a target single layer, a first target value of the energy storage splitting coefficient is obtained based on the mud logging gas measurement amplitude value, the acoustic wave time difference, and the effective thickness; a second target value of the permeability splitting coefficient is obtained based on the permeability, the acoustic wave time difference, and the effective thickness; and a third target value of the reconstruction splitting coefficient is obtained based on the construction displacement, the first acid injection displacement, and the second acid injection displacement; the target single layer is any single layer among the multiple single layers;

[0024] determining the target gas production of the single-layer splitting according to the total gas production, the first target value, the second target value, and the third target value;

[0025] Any single layer among the multiple single layers is used as the target single layer, and the above steps are repeated until the gas production of each single layer is obtained.

[0026] Optionally, the calculation formula for calculating the energy storage splitting coefficient is as follows:

[0027]

[0028] Among them, C i represents the energy storage splitting coefficient; T Gi represents the weighted average value of all hydrocarbons in the well logging of the i-th single layer; T G基值i Indicates the full hydrocarbon base value of the logging of the i-th single layer; AC i represents the weighted average of the acoustic time difference of the i-th single layer; H 有效i Represents the effective thickness of the i-th single layer.

[0029] Optionally, the calculation formula for calculating the osmotic energy splitting coefficient is as follows:

[0030]

[0031] Among them, S i represents the energy splitting coefficient; K 1i represents the weighted average permeability of the tight sandstone reservoir in the i-th single layer; K 2i represents the weighted average permeability of the carbonate reservoir in the i-th single layer; H 有效i Represents the effective thickness of the i-th single layer.

[0032] Optionally, the calculation formula for calculating the transformation splitting coefficient is as follows:

[0033]

[0034] Among them, G i represents the transformation splitting coefficient; P 1irepresents the construction displacement of the i-th single layer of tight sandstone reservoir; P Gi represents the first acid injection rate of the i-th single-layer carbonate reservoir; P pi represents the second acid injection displacement of the i-th single-layer carbonate reservoir, and the injection pressure corresponding to the first acid injection displacement is greater than the injection pressure corresponding to the second acid injection displacement.

[0035] Optionally, the calculation formula corresponding to the capacity splitting model is as follows:

[0036]

[0037] Among them, Q i represents the gas production of the i-th single layer in the gas well to be split; Q represents the total gas production of the gas well to be split; C i Represents the energy storage splitting coefficient; S i represents the energy splitting coefficient; G i represents the transformation splitting coefficient.

[0038] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects:

[0039] The present invention uses measured data from the test gas production of multiple individual layers in a sample gas well and the factors influencing the production capacity to be determined to determine the target factors influencing each reservoir type. Based on the target factors, a production capacity splitting model is constructed. The sample gas wells include reservoirs of different reservoir types, and the production capacity splitting model is used to split the production capacity of gas wells containing reservoirs of multiple reservoir types. This allows the target factors used to construct the production capacity splitting model to better reflect the actual production capacity influencing factors of different reservoir types, allowing the constructed production capacity splitting model to be used for production capacity splitting of gas wells containing reservoirs of different reservoir types, thereby improving the accuracy of the individual layer production capacity splitting results for different reservoir types.

[0040] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0042] Figure 1 The present invention is a flowchart showing a method for constructing a gas well productivity splitting model according to an exemplary embodiment.

[0043] Figure 2 FIG. 1 is a schematic diagram showing a multi-layer pressure-separation test of multiple reservoir types according to an exemplary embodiment.

[0044] Figure 3 The figure is a flow chart showing a method for splitting gas well productivity according to an exemplary embodiment. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the specification of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0046] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0047] In the oil and gas field sector, multi-layer pressure-separation testing typically refers to a test conducted on underground oil and gas formations to assess parameters such as productivity, permeability, and pressure. This testing aims to better understand the physical properties and dynamic behavior of the formations, thereby guiding field development and production. During this testing, one or more wells are selected and measurement equipment, such as sensors and flowmeters, is installed. Then, by controlling the pressure and flow at the wellhead, injection or production is conducted into the formation to observe its response. By recording data such as pressure, temperature, and flow at the bottom and wellhead, and combining it with geological data and mathematical models, formation properties can be evaluated and predicted. This helps petroleum engineers better understand formation characteristics, guide wellbore design and production control, and improve oil and gas recovery efficiency and production. During multi-layer pressure-separation testing in oil and gas fields, production splitting is performed based on the production of different formations or intervals. This allows for a better understanding and utilization of the productivity of individual formations within a multi-layered oil and gas field, thereby maximizing oil and gas recovery efficiency.

[0048] Production splitting involves restoring the wellhead production of oil and water wells to the corresponding production well sections or reservoir sections at the bottom of the well, according to specific rules and methods. This is a key step in accurately understanding reservoir production and improving refined reservoir development. While some key gas wells have production profile testing, which can identify the productivity contribution of each layer, most gas wells do not. This makes it impossible to determine the productivity contribution of each layer after combined testing, making it difficult to accurately identify the key contributing layers and deploy further production expansion. Production splitting from multi-layer, pressure-separated combined testing has long been a challenge hindering productivity evaluation and dynamic analysis. Currently, the main methods for production splitting include geological parameter methods, numerical simulation methods, and catastrophe theory. However, these methods focus on splitting the production of multi-layer combined testing within a single reservoir type. However, the differences in reservoir types, geology, and fracturing parameters, and their impact on productivity, vary significantly. Therefore, these methods are not suitable for splitting the production of gas wells tested across multiple reservoir types.

[0049] This invention provides a method for constructing a gas well reservoir productivity splitting model, which is more suitable for splitting the productivity of each layer in multi-layer combined testing of different gas reservoir types. Using this productivity splitting model, the results of splitting the productivity of gas wells containing multiple reservoir types are more consistent with the actual splitting results of multi-type reservoir combined testing.

[0050] For details, see Figure 1 , Figure 1 FIG. 1 is a flow chart of a method for constructing a gas well reservoir productivity splitting model according to an exemplary embodiment. Figure 1 As shown, the method for splitting the gas well reservoir productivity includes the following steps.

[0051] S101, obtaining measurement data of test gas production and to-be-determined production capacity influencing factors of multiple single layers in a sample gas well, where the sample gas well includes reservoirs of different gas reservoir types;

[0052] S102. Determine target influencing factors for each gas reservoir type based on the test gas production and measurement data;

[0053] S103. Construct a productivity splitting model based on target influencing factors. The productivity splitting model is used to split the productivity of gas wells including reservoirs of various gas reservoir types.

[0054] For example, the test gas production of multiple single layers in a sample gas well can be obtained through measurement. The factors affecting the production capacity to be determined include multiple factors, and multiple influencing factors of reservoirs of different gas reservoir types can be obtained through data collection. The sample gas well includes multiple single layers, and further includes reservoirs of different gas reservoir types. Here, the sample gas well includes at least two reservoir types, and the storage space and seepage mechanism of at least two reservoir types are very different. Here, the sample gas well can include tight sandstone gas reservoir type reservoirs and carbonate gas reservoir type reservoirs, see Figure 2 For example, the sample gas well includes 5 single layers, further including 3 layers of tight sandstone gas reservoir type reservoirs and 2 layers of carbonate gas reservoir type reservoirs.

[0055] In one example, different gas reservoir types include tight sandstone reservoirs and carbonate reservoirs. Factors influencing the productivity of tight sandstone reservoirs may include sand volume, average sand ratio, operation displacement, pre-pad fluid, displacement fluid, sand-carrying fluid, total in-ground fluid volume, liquid nitrogen displacement, liquid nitrogen injection volume, fracture pressure, and pump-off pressure. Factors influencing the productivity of carbonate reservoirs may include viscosifying acid volume, dilute acid volume, displacement volume, total in-ground fluid volume, liquid nitrogen volume, high-pressure acid injection pressure and displacement, and conventional acid pressure and displacement.

[0056] The present invention uses measured data from the test gas production of multiple individual layers in a sample gas well and the factors influencing the production capacity to be determined to determine the target factors influencing each reservoir type. Based on the target factors, a production capacity splitting model is constructed. The sample gas wells include reservoirs of different reservoir types, and the production capacity splitting model is used to split the production capacity of gas wells containing reservoirs of multiple reservoir types. This allows the target factors used to construct the production capacity splitting model to better reflect the actual production capacity influencing factors of different reservoir types, allowing the constructed production capacity splitting model to be used for production capacity splitting of gas wells containing reservoirs of different reservoir types, thereby improving the accuracy of the individual layer production capacity splitting results for different reservoir types.

[0057] As an optional embodiment, based on the test gas production and measurement data, target influencing factors of the reservoir for each gas reservoir type are determined, including:

[0058] For the target gas reservoir type, determine the weighted coefficient value of each reservoir based on the measured data;

[0059] Determine the correlation coefficient between each capacity influencing factor and the test gas production based on the weighted coefficient value, test gas production and measurement data;

[0060] According to the correlation coefficient, the target influencing factors of the reservoir of the target gas reservoir type are determined.

[0061] For example, the measured data of the productivity influencing factors to be determined and the corresponding test gas production can be used to determine the target influencing factors that have a greater impact on productivity. As shown in Tables 1 and 2 below, the relationship between the test gas production of tight sandstone and carbonate rock and the measured data of productivity influencing factors is shown. Among them, due to the heterogeneity of the reservoir, there are many data points with large differences in logging sampling points. The characteristics of the reservoir thickness in different intervals can be used to perform weighted average statistics on the logging curves and the total hydrocarbon logging of the logging parameters within a certain reservoir thickness. The relationship between the test gas production and the corresponding parameters is analyzed to determine the correlation coefficient, and the sensitive parameters are selected as the target influencing factors.

[0062] Table 1 Measurement data of test gas production and factors affecting productivity of tight sandstone single layer

[0063]

[0064] Table 2 Measurement data of test gas production and factors affecting productivity of carbonate rock monolayer

[0065]

[0066] Among them, tight sandstone is a reservoir rock with very low porosity and permeability, making it difficult to explore and develop. It is generally composed of dense crushed rock, mainly including siltstone, fine sandstone and some medium-coarse sandstone. Tight sandstone gas reservoirs are closely related to deep basin gas reservoirs, basin center gas reservoirs and persistent accumulation gas reservoirs. Carbonate rock refers to the general term for rocks composed of carbonate minerals formed by sedimentation, mainly limestone and dolomite. Carbonate rocks themselves can also be useful minerals, such as limestone, dolomite, siderite, rhodochrosite, magnesite, etc., which are widely used in metallurgy, construction, decoration, chemical industry and other industries. Carbonate rocks are rich in oil, natural gas and groundwater.

[0067] For example, for tight sandstone, the single-layer test gas production and combined parameters such as logging gas measurement amplitude value, three-porosity curve, resistivity curve, sand body thickness, effective thickness, porosity, matrix permeability, water saturation, formation coefficient, construction displacement, pre-fluid, total fluid volume injected into the ground and fracture pressure were analyzed, and fracturing parameters such as logging gas measurement amplitude value, acoustic wave time difference, effective thickness, formation coefficient and construction displacement were selected as sensitive parameters of tight sandstone. Carbonate rock analysis included single-layer gas testing parameters and combined parameters such as mud logging gas logging amplitude, three-porosity curves, resistivity curves, sand body thickness, effective thickness, porosity, matrix permeability, fracture permeability, water saturation, formation coefficient, and other fracturing parameters, as well as high-pressure acid injection rate, conventional acid injection rate, viscosified acid volume, diluted acid volume, and total in-ground fluid volume. The results showed that mud logging gas logging amplitude, acoustic transit time, effective thickness, formation coefficient, matrix permeability, fracture permeability, high-pressure acid injection rate, and conventional acid injection rate were selected as sensitive parameters for carbonate rock. Therefore, mud logging gas logging amplitude, acoustic transit time, effective thickness, formation coefficient, and operation rate can be used as target influencing factors for tight sandstone, and mud logging gas logging amplitude, acoustic transit time, effective thickness, formation coefficient, matrix permeability, fracture permeability, high-pressure acid injection rate, and conventional acid injection rate can be used as target influencing factors for carbonate rock.

[0068] Mud logging refers to the measurement and recording of wellbore properties during drilling using logging tools to obtain data on formation rock properties and the properties of fluids within the wellbore. Mud logging gas amplitude typically refers to the measurement of gas presence and recording of its amplitude during well logging. This data can be used to analyze the gas content and properties within the reservoir. In geophysical exploration, acoustic transit time refers to the time difference between sound waves emitted from a seismic source reaching different underground rock layers and bouncing back to the surface. By analyzing these transit times, geophysicists can infer the properties of underground rock formations, including rock type, density, and thickness. Permeability refers to the ability of rocks within a formation reservoir to penetrate fluids (such as water, oil, or gas). It is a critical reservoir property that directly affects the rate of fluid migration within the reservoir. High permeability indicates good permeability to fluids, while low permeability indicates relatively poor permeability. Effective thickness refers to the thickness of the portion of the reservoir capable of industrial oil production, that is, the thickness of the reservoir containing movable oil within an industrial oil well. It takes into account the influence of different rock types and properties within the reservoir, providing more accurate information on fluid migration capacity and can be obtained through actual surveys. Operation displacement typically refers to the volumetric flow rate of liquid or gas guided through pipelines or other equipment during an engineering or construction process. In drilling projects, operation displacement may refer to the flow rate of mud, water, or other drilling fluids discharged from the bottom of the well to the wellhead.

[0069] Among them, high-pressure acid injection displacement refers to the displacement of high-pressure acid injection liquid used in high-pressure acid injection operations carried out in oil field or natural gas development to improve the productivity of wellbores or reservoirs. This operation aims to improve the permeability of oil and gas wells and increase productivity. Ordinary acid displacement refers to the flow rate of acid liquid discharged during general acid injection operations in oil field or natural gas development. Among them, the high-pressure acid injection displacement and ordinary acid displacement can be determined based on the relationship curve between injection pressure and acid injection displacement. The high-pressure acid injection displacement and ordinary acid displacement here can be defined according to the formation. For example, the high-pressure acid injection displacement of carbonate rock ranges from 1.8 to 4.0 m 3 / min, the normal acid injection displacement range is 1.2-2.5m 3 / min.

[0070] As an optional embodiment, factors affecting productivity include acoustic transit time and effective thickness. Based on the measurement data, a weighted coefficient value for each reservoir is determined, including:

[0071] For the reservoirs of the target gas reservoir type, the total thickness of all reservoirs of the target gas reservoir type and the target thickness of multiple reservoirs in the same acoustic transit time interval are determined based on the acoustic transit time interval of each reservoir and the effective thickness measurement data of each reservoir.

[0072] The weighted coefficient value of each reservoir is determined according to the ratio of the target thickness corresponding to different acoustic wave time difference intervals to the total thickness.

[0073] Specifically, in one embodiment, based on the acoustic transit time curve, the acoustic transit time of tight sandstone gas reservoirs is primarily distributed in the range of 200-250 μs / m, while the acoustic transit time of carbonate gas reservoirs is primarily distributed between 150-200 μs / m. Therefore, based on the well logging response characteristics, the acoustic transit time of tight sandstone reservoirs is divided into four intervals: 200-210 μs / m, 210-220 μs / m, 220-230 μs / m, and greater than 230 μs / m. The acoustic transit time of carbonate reservoirs is divided into five intervals: 150-160 μs / m, 160-170 μs / m, 170-180 μs / m, 180-190 μs / m, and greater than 190 μs / m. For example, based on the logging sampling interval of 0.125m, with 0.125 as the unit, the proportion of the total thickness of the reservoir corresponding to each acoustic wave time difference interval to the total thickness of all reservoirs is counted. The average thickness of the reservoirs in different acoustic wave time difference intervals can be used to determine the weighted average value of each reservoir.

[0074] As an optional embodiment, the correlation coefficient between each capacity influencing factor and the test gas production is determined based on the weighted coefficient value, the test gas production, and the measurement data, including:

[0075] For the target gas reservoir type, determine the weighted average value of each productivity influencing factor for each reservoir based on the weighted coefficient value and measurement data of each reservoir;

[0076] Based on the test gas production and weighted average of each reservoir in the target gas reservoir type, the correlation coefficient between each productivity influencing factor and the test gas production is determined.

[0077] For example, a weighted average value of each productivity-influencing factor for each reservoir can be determined based on the weighted coefficient value and measured data for each reservoir. Furthermore, a correlation coefficient between each productivity-influencing factor and the test gas production can be determined based on the test gas production of each reservoir in the target gas reservoir type and the weighted average value. For example, a geological parameter corresponding to each individual layer can be obtained, and a scatter plot can be plotted in an Excel spreadsheet. The sensitivity can be determined by the correlation coefficient in the trend line: a relatively high correlation coefficient can be considered high sensitivity, while a relatively low correlation coefficient can be considered low sensitivity.

[0078] See also Figure 3 , Figure 3 FIG. 1 is a flow chart showing a method for splitting gas well productivity according to an exemplary embodiment. Figure 3 As shown, the gas well productivity splitting method includes the following steps.

[0079] In step S301, the total gas production of the gas well to be split, as well as geological factor data and fracturing parameter data corresponding to multiple single layers in the gas well to be split, are obtained, wherein the gas well to be split includes reservoirs of different gas reservoir types;

[0080] In step S302, the gas production of each split layer is obtained based on the total gas production, geological factor data, fracturing parameter data and the productivity splitting model, wherein the productivity splitting model is obtained by the above-mentioned gas well productivity splitting model construction method.

[0081] The total gas production of the gas well to be split can be measured, where the total gas production can be the daily gas production of the gas well to be split. The gas well to be split includes multiple single layers, further including reservoirs of different gas reservoir types. Here, the gas well to be split includes at least two reservoir types, and the at least two reservoir types have significantly different storage spaces and seepage mechanisms.

[0082] Among them, by measuring the total gas production of the gas well to be split, as well as the geological factor data and fracturing parameter data corresponding to multiple single layers in the gas well, the geological factor data include reservoir thickness, porosity, permeability, etc., and the fracturing parameter data include fracturing fluid type, fracturing pressure, fracturing fluid volume, etc. Geological factors can determine the gas content and enrichment of reservoirs of different gas reservoir types, as well as the different pore space types of reservoirs of different gas reservoir types, taking into account the differences in geological factors between different types of reservoirs. Based on the analysis of factors affecting the height of hydraulic fractures, the construction displacement is the key controllable factor affecting the extension of fracture height, and then the communication effect between the fracturing transformation fractures and the natural fractures of the reservoir affects the effect of post-fracturing production capacity. Therefore, the fracturing parameters can determine the seepage mechanism and engineering transformation effect of reservoirs of different gas reservoir types, fully combining the development factors of gas wells, and considering the differences between the fracturing parameters of different types of reservoirs.

[0083] The productivity splitting model can calculate the gas production of each individual layer using total gas production, geological factor data, and fracturing parameter data. It is understood that the productivity splitting model can be a mathematical calculation model or a neural network model. Both mathematical calculation and neural network models are fitted and verified based on pre-measured data to obtain the productivity splitting model.

[0084] For example, the gas production of each split layer can be obtained based on the total gas production, geological factor data, fracturing parameter data, and the productivity splitting model. Furthermore, based on the gas production of each layer, an evaluation result for evaluating the productivity contribution of each layer can be obtained. For example, the productivity contribution evaluation result for each layer can be determined based on the ratio of the gas production of each layer to the total gas production.

[0085] The present invention uses the total gas production of a gas well to be split, geological factor data and fracturing parameter data corresponding to multiple individual layers in the gas well to be split, and a productivity splitting model to determine the gas production of each individual layer after splitting. The gas well to be split includes reservoirs of different gas reservoir types. Thus, the gas production of each individual layer derived from the productivity splitting model and the geological factor data and fracturing parameter data corresponding to the multiple individual layers takes into account the differences in storage space and seepage mechanisms among reservoirs of different gas reservoir types. This ensures that the gas production of each individual layer after splitting is more consistent with its actual productivity, thereby improving the accuracy of the productivity splitting results for individual layers of different gas reservoir types.

[0086] As an optional embodiment, the productivity splitting model includes a storage splitting coefficient, a permeability splitting coefficient, and a reformation splitting coefficient. Based on the total gas production, geological factor data, fracturing parameter data, and the productivity splitting model, the gas production of each single layer after splitting is obtained, including:

[0087] Obtaining a first target value of the energy storage splitting coefficient and a second target value of the energy permeability splitting coefficient based on geological factor data of a target single layer, wherein the target single layer is any single layer among the multiple single layers;

[0088] Obtaining a third target value of the stimulation splitting coefficient through the fracturing parameter data of the target single layer;

[0089] Determining a target gas production of a single-layer split according to the total gas production, the first target value, the second target value, and the third target value;

[0090] Any of the remaining monolayers is taken as a new target monolayer, and the above steps are repeated until the gas production of each monolayer is obtained.

[0091] For example, taking the productivity splitting model as a mathematical calculation model, when establishing the productivity splitting model, a calculation formula for the energy storage splitting coefficient can be established according to the gas content and enrichment degree of reservoirs of different gas reservoir types, a calculation formula for the permeability splitting coefficient can be established according to the different pore space types of reservoirs of different gas reservoir types, and a calculation formula for the transformation splitting coefficient can be established according to the fracturing parameters of reservoirs of different gas reservoir types, thereby establishing a calculation formula for the productivity splitting model based on the energy storage splitting coefficient, the permeability splitting coefficient and the transformation splitting coefficient.

[0092] For example, geological factor data can reflect the gas content and enrichment of reservoirs of different gas reservoir types, as well as different pore space types. Therefore, the calculation formula of the energy storage splitting coefficient and the calculation formula of the permeability splitting coefficient can be used, and the geological factor data of the target single layer can be used to obtain the first target value of the energy storage splitting coefficient and the second target value of the permeability splitting coefficient. The calculation formula of the transformation splitting coefficient can be used to obtain the third target value of the transformation splitting coefficient through the fracturing parameter data of the target single layer. The gas production of the target single layer split can be further calculated through the calculation formula of the productivity splitting model. The remaining single layers are used as new target single layers, and the values ​​of the energy storage splitting coefficient, permeability splitting coefficient and transformation splitting coefficient corresponding to each single layer are repeatedly calculated, and the gas production of each single layer can be further obtained.

[0093] As an optional embodiment, the target single layer includes a tight sandstone reservoir and a carbonate reservoir, the geological factor data of the tight sandstone reservoir and the geological factor data of the carbonate reservoir both include a well logging gas measurement amplitude value, a sonic wave time difference, a permeability, and an effective thickness, the fracturing parameter data of the tight sandstone reservoir includes a construction displacement, and the fracturing parameter data of the carbonate reservoir includes a first acid injection displacement and a second acid injection displacement, and the first acid injection displacement and the second acid injection displacement correspond to different injection pressures;

[0094] The first target value of the energy storage splitting coefficient and the second target value of the energy permeability splitting coefficient are obtained through the geological factor data of the target single layer, including:

[0095] The first target value is calculated based on the well logging gas measurement amplitude value, the acoustic wave time difference and the effective thickness;

[0096] The second target value is calculated based on the permeability, the acoustic time difference and the effective thickness;

[0097] The third target value of the stimulation splitting coefficient is obtained through the fracturing parameter data of the target single layer, including:

[0098] The third target value is calculated based on the construction displacement, the first acid injection displacement and the second acid injection displacement.

[0099] The first and second acid injection rates can be high-pressure and conventional acid injection rates, respectively. The first target value can be calculated based on the logging gas amplitude, acoustic transit time, and effective thickness. The second target value can be calculated based on the permeability, acoustic transit time, and effective thickness. Furthermore, the third target value can be calculated based on the operation rate, the first and second acid injection rates. These first, second, and third target values ​​can be substituted into the productivity splitting model to determine the gas production of each split layer.

[0100] As an optional example, the first target value is calculated based on the well logging gas measurement amplitude value, the acoustic wave time difference, and the effective thickness, including:

[0101] Determine the weight coefficient value of each reservoir in the target single layer according to the acoustic wave time difference and effective thickness;

[0102] According to the logging gas logging amplitude value and the weighted coefficient value of each reservoir, the weighted average value of the logging total hydrocarbon and the logging total hydrocarbon base value of the target single layer are calculated;

[0103] Calculate the weighted average of the acoustic wave time difference of the target single layer according to the acoustic wave time difference and the weighted coefficient value of each reservoir;

[0104] The first target value is calculated based on the weighted average value of all hydrocarbons in the well logging, the base value of all hydrocarbons in the well logging, the weighted average value of the acoustic wave time difference and the effective thickness.

[0105] For example, the reservoir is highly heterogeneous and the arithmetic mean cannot represent the actual situation of the reservoir. Therefore, the thickness of different single-layer reservoirs can be determined based on the acoustic wave time difference, and then the weighted coefficient value of the single-layer reservoir can be obtained by using the proportion of the thickness of different single-layer reservoirs to the total thickness, and then the weighted average geological parameters can be performed.

[0106] The full hydrocarbon logging data is stable within a certain range of values ​​with minimal fluctuations. This value is called the full hydrocarbon logging base value. The weighted average of the acoustic transit time for the target single layer is calculated by weighted averaging the acoustic transit time and the weighted coefficients for each reservoir. The weighted average of the acoustic transit time for the target single layer is calculated by weighted averaging the acoustic transit time and the weighted coefficients for each reservoir. Based on the full hydrocarbon logging weighted average, the full hydrocarbon logging base value, the weighted average acoustic transit time, and the effective thickness, the first target value corresponding to the energy storage splitting coefficient can be calculated using the formula for the energy storage splitting coefficient.

[0107] As an optional embodiment, the calculation formula for calculating the energy storage splitting coefficient is as follows:

[0108]

[0109] Among them, C i represents the energy storage splitting coefficient; T Gi represents the weighted average value of all hydrocarbons in the well logging of the i-th single layer; T G基值i Indicates the full hydrocarbon base value of the logging of the i-th single layer; AC i represents the weighted average of the acoustic time difference of the i-th single layer; H 有效i Represents the effective thickness of the i-th single layer.

[0110] In this way, the first target value corresponding to the energy storage splitting coefficient can be calculated using the above calculation formula of the energy storage splitting coefficient according to the weighted average value of all hydrocarbons in well logging, the weighted average value of all hydrocarbon base values ​​in well logging, the weighted average value of the acoustic wave time difference and the effective thickness.

[0111] As an optional embodiment, calculating the second target value according to the permeability, the acoustic transit time, and the effective thickness includes:

[0112] Calculate the weighted average permeability of the carbonate reservoir and the weighted average permeability of the tight sandstone reservoir based on the permeability and the weighted coefficient value of each reservoir in the target single layer;

[0113] The second target value is calculated according to the weighted average permeability of the carbonate reservoir, the weighted average permeability of the tight sandstone reservoir and the effective thickness.

[0114] The weighted average permeability of the target single layer is calculated by taking the weighted average of the permeability and the weighted coefficient of each reservoir. Based on the weighted average permeability of the carbonate reservoir and the weighted average permeability of the tight sandstone reservoir, as well as their effective thickness, the second target value corresponding to the permeability splitting coefficient is calculated using the formula for the permeability splitting coefficient.

[0115] As an optional embodiment, the weighting coefficient value of each reservoir in the target single layer is determined based on the acoustic wave time difference and the effective thickness, including:

[0116] Determine the thickness of each reservoir layer based on the acoustic time difference of the target single layer;

[0117] The weighted coefficient value of each reservoir is determined according to the ratio of the thickness of each reservoir to the effective thickness.

[0118] In one embodiment, based on the acoustic transit time curve, the acoustic transit time of tight sandstone gas reservoirs is primarily distributed in the range of 200-250 μs / m, while the acoustic transit time of carbonate gas reservoirs is primarily distributed between 150-200 μs / m. Therefore, based on the well logging response characteristics, the acoustic transit time of tight sandstone reservoirs is divided into four intervals: 200-210 μs / m, 210-220 μs / m, 220-230 μs / m, and greater than 230 μs / m. The acoustic transit time of carbonate reservoirs is divided into five intervals: 150-160 μs / m, 160-170 μs / m, 170-180 μs / m, 180-190 μs / m, and greater than 190 μs / m. The weighted average thickness of each reservoir is determined using the average reservoir thickness in different acoustic transit time intervals.

[0119] In addition to the above method for determining the weighting coefficient for each reservoir layer, a weighting coefficient for each reservoir layer can also be pre-determined based on the ratio of the thickness of the different reservoir layers within each detected single layer to the effective thickness of the single layer. The thickness of the different reservoir layers within each single layer can be determined based on the acoustic wave transit time curve. Furthermore, weighting coefficients corresponding to different reservoir types can be directly set. For example, the weighting coefficient for tight sandstone reservoirs can be set to 0.3, the weighting coefficient for carbonate reservoirs can be set to 0.6, and the weighting coefficients for other reservoir types can be set to 0.1.

[0120] As an optional embodiment, the calculation formula for calculating the permeability splitting coefficient is as follows:

[0121]

[0122] Among them, S i represents the permeability splitting coefficient; K 1i represents the weighted average permeability of the tight sandstone reservoir in the i-th single layer; K 2i represents the weighted average permeability of the carbonate reservoir in the i-th single layer; H 有效i Represents the effective thickness of the i-th single layer.

[0123] In this way, the second target value corresponding to the permeability splitting coefficient can be calculated using the above-mentioned calculation formula of the permeability splitting coefficient according to the weighted average permeability of the carbonate rock type reservoir, the weighted average permeability of the tight sandstone type reservoir and the effective thickness.

[0124] As an optional embodiment, the calculation formula for calculating the transformation splitting coefficient is as follows:

[0125]

[0126] Among them, G i represents the transformation splitting coefficient; P 1i represents the construction displacement of the i-th single layer of tight sandstone reservoir; P Gi represents the first acid injection rate of the i-th single-layer carbonate reservoir; P pi It represents the second acid injection displacement of the i-th single-layer carbonate reservoir. The injection pressure corresponding to the first acid injection displacement is greater than the injection pressure corresponding to the second acid injection displacement.

[0127] For example, based on an analysis of factors influencing hydraulic fracture height, the construction displacement is a key controllable factor affecting fracture height extension, which in turn influences the post-fracture production rate through the communication between the stimulated fracture and the natural fractures in the reservoir. By establishing the aforementioned stimulation splitting coefficient, the third target value corresponding to the stimulation splitting coefficient can be calculated using the aforementioned formula for the permeability splitting coefficient, based on the construction displacement for tight sandstone reservoirs and the first and second acid injection displacements for carbonate reservoirs.

[0128] As an optional embodiment, the calculation formula corresponding to the capacity splitting model is as follows:

[0129]

[0130] Among them, Q i represents the gas production after the i-th single layer is split; Q represents the total gas production of the gas well to be split; C i represents the energy storage splitting coefficient; S i represents the energy splitting coefficient; G i Represents the transformation splitting coefficient.

[0131] For example, the above-mentioned production capacity splitting model including the energy storage splitting coefficient, the permeability splitting coefficient and the transformation splitting coefficient can be used, combined with the calculation formula of the above-mentioned energy storage splitting coefficient, the calculation formula of the permeability splitting coefficient and the calculation formula of the transformation splitting coefficient, through the logging gas measurement amplitude value, acoustic wave time difference, permeability and effective thickness of the tight sandstone type reservoir and the carbonate rock type reservoir, the fracturing parameter data of the tight sandstone type reservoir including the construction displacement, and the fracturing parameter data of the carbonate rock type reservoir including the first acid injection displacement and the second acid injection displacement, to obtain the gas production of each single layer after splitting. In addition, the above-mentioned calculation formulas for the energy storage splitting coefficient, the calculation formulas for the permeability splitting coefficient, and the calculation formulas for the transformation splitting coefficient, as well as the productivity splitting model of the present invention, are based on traditional methods such as the formation coefficient method and the effective thickness method, and comprehensively consider the fluid storage capacity of the storage space of tight sandstone gas reservoirs and carbonate gas reservoirs, the seepage mechanism of the two gas reservoirs, and the impact of engineering fracturing on reservoir productivity. There is a significant difference in the gas test conclusions before and after splitting, and the gas test conclusions after splitting are more consistent with the geological characteristics.

[0132] As another optional implementation, the method further includes:

[0133] According to the gas production of each single layer, an evaluation result for evaluating the production capacity contribution rate of each single layer is obtained, wherein, when the gas production of the target single layer is less than the first threshold, the evaluation result of the target single layer is determined to be a gas-containing layer, and the target single layer is any layer of the multiple single layers; when the gas production of the target single layer is greater than or equal to the first threshold and less than the second threshold, the evaluation result of the target single layer is determined to be a poor gas layer; when the gas production of the target single layer is greater than or equal to the second threshold, the evaluation result of the target single layer is determined to be a gas layer.

[0134] For example, the first threshold and the second threshold can be set according to actual conditions. It can be understood that the contribution rate of the gas layer is greater than the contribution rate of the poor gas layer, and the contribution rate of the poor gas layer is greater than the contribution rate of the gas-bearing layer. The gas well production splitting statistics table shown in Table 1 below can be referred to, wherein the first digit of the layer number represents the well number, and the second digit represents the layer number. According to the production splitting daily gas production, the production splitting daily gas production is 1×10 4 m 3 The single layer with a daily gas production of more than 0.5×10 4 m 3 / d to 1×10 4 m 3 The single layer between 10 and 20 is a poor gas layer, and the daily gas production of the production split is 0.1×10 4 m 3 / d~0.5×10 4 m 3 The single layer between / d is the gas-bearing layer.

[0135] As can be seen from Table 1, the present invention is used to split the production of multi-layer combined test gas wells in two types of reservoirs, tight sandstone gas reservoirs and carbonate rocks. It comprehensively considers the differences in different reservoir types, geological factors, and fracturing parameters and their impact on reservoir productivity, achieving combined test gas well production splitting of reservoir types with significant differences, further solving the problem of evaluating the productivity contribution rate of each layer in multi-layer combined tests of different gas reservoir types. Compared with the splitting method of single-layer gas reservoirs, it is more consistent with the actual combined test splitting results of multiple types of reservoirs. Therefore, the evaluation results for different single layers are more consistent with the actual productivity of each single layer, improving the accuracy of the single-layer productivity splitting evaluation results of different gas reservoir types. In addition, the present invention has a good guiding role in the correct understanding of reservoir geological characteristics, detailed logging interpretation, and reservoir transformation and fracturing plans.

[0136] Table 1 Gas well production split statistics

[0137]

[0138] In addition, the ratio of the target single layer’s gas production to the daily gas production can also be used to evaluate the contribution rate of the target single layer. For example, in Table 1 above, the daily gas production of the single layer with layer 12 in Well No. 1 after splitting is 0.17×10 4 m 3 / d, so the total daily gas production of this single layer is 2.822×10 4 m 3 / d contributed about 6%.

[0139] This method, unlike many other commingled gas well production splitting methods, is the first to incorporate the concept of integrated geology and fracturing. Compared to parametric methods, it not only considers geological factors but also the seepage mechanisms and engineering transformation effects of different gas reservoir types, fully incorporating gas well development factors. Compared to numerical simulation and catastrophe theory methods, it offers advantages such as simplicity, ease of implementation, and relatively easy data collection. It can quickly and accurately split the production of commingled wells, providing a basis for detailed reservoir description, remaining gas distribution, stratified dynamic reserve evaluation, and gas leakage area calculation.

[0140] Finally, it should be noted that the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features therein may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for constructing a gas well productivity splitting model, characterized in that: include: Obtaining measurement data of test gas production of multiple single layers in a sample gas well and factors affecting production capacity to be determined, wherein the sample gas well includes reservoirs of different gas reservoir types; Determining target influencing factors of the reservoir of each gas reservoir type according to the test gas production and the measurement data; According to the target influencing factors, a productivity splitting model is constructed, wherein the productivity splitting model is used to split the productivity of gas wells including reservoirs of various gas reservoir types; The production capacity splitting model includes a storage splitting coefficient, a permeability splitting coefficient and a transformation splitting coefficient; the target influencing factors include geological factors and fracturing parameters; the multiple single layers include a tight sandstone type reservoir and a carbonate rock type reservoir; the geological factors of the tight sandstone type reservoir and the geological factors of the carbonate rock type reservoir both include logging gas measurement amplitude value, acoustic wave time difference, permeability and effective thickness; the fracturing parameters of the tight sandstone type reservoir include a construction displacement; the fracturing parameters of the carbonate rock type reservoir include a first acid injection displacement and a second acid injection displacement; the first acid injection displacement and the second acid injection displacement correspond to different injection pressures; The calculation formula for calculating the energy storage splitting coefficient is as follows: ， Among them, C i represents the energy storage splitting coefficient; T Gi represents the weighted average value of all hydrocarbons in the logging of the i-th single layer; T G基值i Indicates the total hydrocarbon base value of the logging of the i-th single layer; AC i H represents the weighted average of the acoustic time difference of the i-th single layer; 有效i Indicates the effective thickness value of the i-th single layer; the logging full hydrocarbon base value and the logging full hydrocarbon weighted average value are obtained according to the logging gas measurement amplitude value and the weighted coefficient value of each reservoir; the weighted coefficient value of each reservoir is obtained according to the acoustic wave time difference and the effective thickness; the acoustic wave time difference weighted average value is obtained according to the acoustic wave time difference and the weighted coefficient value of each reservoir; The calculation formula for calculating the permeability splitting coefficient is as follows: ， Among them, S i represents the energy splitting coefficient; K 1i represents the weighted average permeability of the tight sandstone reservoir in the i-th single layer; K 2i represents the weighted average permeability of the carbonate reservoir in the i-th single layer; H 有效i represents the effective thickness value of the i-th single layer; the weighted average permeability of the tight sandstone type reservoir and the weighted average permeability of the carbonate type reservoir are obtained according to the permeability and the weighted coefficient value of each reservoir; The calculation formula for calculating the transformation splitting coefficient is as follows: ， Among them, G i represents the transformation splitting coefficient; P 1i represents the construction displacement of the i-th single layer of tight sandstone reservoir; P Gi represents the first acid injection displacement of the i-th single-layer carbonate reservoir; P pi represents a second acid injection displacement of the i-th single-layer carbonate reservoir, the injection pressure corresponding to the first acid injection displacement being greater than the injection pressure corresponding to the second acid injection displacement; The calculation formula corresponding to the capacity splitting model is as follows: ， Among them, Q i represents the gas production of the i-th single layer in the gas well after splitting; Q represents the total gas production of the gas well; C i represents the energy storage splitting coefficient; S i represents the energy splitting coefficient; G i represents the transformation splitting coefficient.

2. The method according to claim 1, characterized in that According to the test gas production and the measured data, target influencing factors of the reservoir of each gas reservoir type are determined, including: For the reservoir of the target gas reservoir type, determining the weight coefficient value of each reservoir according to the measurement data; Determining a correlation coefficient between each capacity influencing factor and the test gas production according to the weighted coefficient value, the test gas production and the measurement data; According to the correlation coefficient, the target influencing factor of the reservoir of the target gas reservoir type is determined.

3. The method according to claim 2, characterized in that The productivity influencing factors include acoustic wave time difference and effective thickness. According to the measurement data, the weighted coefficient value of each reservoir is determined, including: For the reservoirs of the target gas reservoir type, according to the acoustic time difference interval in which the acoustic time difference measurement data of each reservoir is located and the measurement data of the effective thickness of each reservoir, the total thickness of all reservoirs of the target gas reservoir type and the target thickness of multiple reservoirs in the same acoustic time difference interval are determined; The weighted coefficient value of each reservoir is determined according to the ratios of the target thicknesses corresponding to different acoustic wave time difference intervals to the total thickness.

4. The method according to claim 2 or 3, characterized in that: Determining a correlation coefficient between each capacity influencing factor and the test gas production according to the weighted coefficient value, the test gas production and the measurement data, including: For the reservoir of the target gas reservoir type, determining the weighted average value of each productivity influencing factor for each reservoir according to the weighted coefficient value of each reservoir and the measurement data; According to the test gas production of each reservoir in the target gas reservoir type and the weighted average value, a correlation coefficient between each productivity influencing factor and the test gas production is determined.

5. A gas well productivity splitting method, characterized in that: include: Acquire the total gas production of the gas well to be split, as well as geological factor data and fracturing parameter data corresponding to multiple single layers in the gas well to be split, wherein the gas well to be split includes reservoirs of different gas reservoir types; The gas production of each single layer after splitting is obtained according to the total gas production, the geological factor data, the fracturing parameter data and the productivity splitting model, wherein the productivity splitting model is obtained according to the method for constructing a gas well productivity splitting model according to any one of claims 1-4.

6. The method according to claim 5, characterized in that According to the total gas production, the geological factor data, the fracturing parameter data and the productivity splitting model, the gas production of each single layer after splitting is obtained, including: For a target single layer, a first target value of the energy storage splitting coefficient is obtained according to the logging gas measurement amplitude value, the acoustic wave time difference and the effective thickness; and a second target value of the permeability splitting coefficient is obtained according to the permeability, the acoustic wave time difference and the effective thickness; and a third target value of the transformation splitting coefficient is obtained according to the construction displacement, the first acid injection displacement and the second acid injection displacement; the target single layer is any single layer among the multiple single layers; Determining the target single-layer splitting gas production according to the total gas production, the first target value, the second target value, and the third target value; Any single layer among the multiple single layers is taken as the target single layer, and the above steps are repeated until the gas production of each single layer is obtained.

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