A universal method for interpreting pressure flow

Abstract

The present invention belongs to the field of electrical digital data processing, and in particular relates to a universal pressure measurement fluidity interpretation method. The present invention comprehensively utilizes a variety of advanced algorithms and technologies such as numerical simulation, orthogonal experimental design, neural network algorithm and iterative calculation. These algorithms and technologies cooperate with each other and complement each other to form a complete pressure measurement fluidity interpretation system. Numerical simulation provides a detailed geological and fluid model, orthogonal experimental design clarifies the main controlling factors, neural network algorithm realizes intelligent prediction, and iterative calculation further optimizes the fluidity interpretation results. This comprehensive use of multiple algorithms not only improves the accuracy and efficiency of pressure measurement fluidity interpretation, but also enhances the reliability and stability of the interpretation results. It can better meet the needs of accurate interpretation of pressure measurement fluidity under different probe types, reservoir characteristics and pumping parameters, and can better adapt to the needs of fluidity interpretation under complex geological conditions.

Description

Technical Field

[0001] The invention belongs to the field of electronic digital data processing, and in particular relates to a universal pressure measurement and flow interpretation method. Background Art

[0002] Currently, the primary interpretation model for pressure-measured fluidity interpretation is based on single-phase flow. This assumes the fluid in the formation pores is a single-phase fluid, and considering various factors influencing the testing process, analytical or numerical methods are used to determine the relationship between formation and instrument parameters and the test pressure response. Commonly used pressure-measured fluidity interpretation methods include pseudo-steady-state pressure drop, spherical flow pressure recovery, radial flow pressure recovery, formation flow analysis, automatic fitting analysis, neural networks, and least squares methods. Among these, pseudo-steady-state pressure drop, spherical flow pressure recovery, and radial flow pressure recovery are the most commonly used methods for determining formation fluidity. The pseudo-steady-state pressure drop method uses the pseudo-steady-state solution of the spherical flow equation to calculate the permeability. However, permeability is a parameter to be measured and requires repeated measurements to determine the critical permeability. Therefore, this method is generally only used for qualitative interpretation. Both the spherical flow and radial flow pressure recovery methods use the straight line segment that appears on the double logarithmic curve of pressure and time during pressure recovery in the later stage of the test to calculate the formation permeability. This requires extended testing time, increases the risk of instrument jamming, and ignores the dynamic changes in pressure in the early and middle stages of the test. When the straight line segment of the double logarithmic curve is difficult to appear, data interpretation cannot be performed, resulting in low accuracy in pressure measurement and flow interpretation.

[0003] In addition to being influenced by the interpretation model used, the analysis results of pressure-flow analysis are also affected by the probe type, the time and speed of pressure measurement extraction, and the size of the formation permeability. Pressure measurement tools used in actual field applications mainly include Schlumberger's MDT and Shell Hughes' RCI. The structures of these instruments vary greatly, and their probe sizes vary, resulting in significant differences in the contact between them and the formation. To improve work efficiency, the pressure measurement process is often short, on the order of tens of seconds, while the fluid extraction rate is high, resulting in large pressure fluctuations, which have a significant impact on the pressure measurement interpretation results. Low-permeability reservoirs are dense, with poor fluid seepage capacity. The pressure propagation range is limited, making it difficult to achieve steady-state or pseudo-steady-state flow. The flow form is mainly unsteady, resulting in poor reliability of pressure-flow interpretation results in tight reservoirs.

[0004] Chinese patent document CN112147051A discloses a pressure measurement fluidity standardization method based on permeability distribution morphology, comprising: 1) selecting test data of a certain formation testing instrument in a certain area as a standard, and establishing fluidity and corresponding permeability data volumes for different wells and different depths; 2) establishing corresponding relationship data between the test fluidity of a pseudo-standardized well and the reservoir permeability, and producing a frequency distribution diagram of the permeability data; 3) producing a frequency distribution diagram of the permeability data of each well according to the same permeability interval as the pseudo-standardized well, and calculating the similarity coefficient between the permeability distribution morphology of the pseudo-standardized well and the permeability distribution morphology of each standard well; and 4) finding wells or sub-data volumes whose permeability distribution morphology of the pseudo-standardized well data is similar to that of the standard test results, and performing standardization processing on the pseudo-standardized well data. However, this method is only applicable to the standardization of mobility data measured by different instruments when the permeability distribution morphology is similar; when the permeability distribution morphology is greatly different, it is impossible to accurately standardize the mobility, especially under complex geological conditions such as low permeability and dense geological conditions, its applicability is limited; in addition, this method mainly relies on the similarity of permeability distribution morphology to standardize the mobility, which may be subjective to a certain extent, and does not fully consider the main controlling factors of pressure measurement fluidity, lacks in-depth mining of dynamic data, and its interpretation accuracy is limited; it also has defects such as strong data dependence, single data processing method, and insufficient processing of differences between different instruments.

[0005] The Chinese paper "Application of the MDT Pressure Drop Mobility Trend Translation Method to Assist Well Logging Interpretation Calibration" analyzed MDT pressure drop mobility from cored wells of 30 different oilfield types in the Pearl River Mouth Basin and found that the MDT pressure drop mobility trend was highly consistent with the permeability trend. Therefore, without considering the dimension, the MDT pressure drop mobility trend can be translated left or right to achieve the calibrated permeability. After analyzing key wells in each oilfield to obtain a pressure drop mobility calibration range, this calibration range was applied to other wells in the same oilfield and to oilfields of the same type. Using the MDT pressure drop mobility trend translation method to calibrate the permeability of well logging interpretations for intervals without cores or with unqualified core analysis can further improve the accuracy of well logging interpretation and reserve evaluation, compensate for the lack of core data, and maximize cost reduction and efficiency. This method mainly relies on the consistency of the MDT pressure drop mobility trend and the permeability trend for calibration. Although this trend shift method can improve the logging interpretation accuracy to a certain extent, it does not fully consider the main influencing factors of pressure drop mobility, such as probe area, pressure measurement time, permeability, pressure measurement speed and fluid viscosity, which may lead to certain deviations in the calibration results; in addition, this method mainly focuses on the trend shift of the data. For the complexity of the seepage law of low permeability reservoirs and the reliability of the pressure drop mobility interpretation results, no in-depth research has been conducted to solve the limited pressure drop mobility interpretation ability under special geological conditions such as low permeability reservoirs.

[0006] Therefore, the relevant research currently published mainly focuses on the correction of existing pressure measurement fluidity interpretation models, and fails to fully consider the influence of probe type, pumping parameters, reservoir seepage capacity, etc., and has not formed a universal and accurate fluidity interpretation method. In addition, at present, the fluidity interpretation methods that come with most cable formation testing instruments on the market are mainly based on simplified theoretical models, and usually do not consider the influence of complex factors such as extraction time and speed. When dealing with low permeability reservoirs or contaminated reservoirs, these traditional methods often cause a significant decrease in interpretation accuracy due to the neglect of these key factors. For example, failure to consider the influence of pressure measurement time and speed may lead to misjudgment of the dynamic response of the reservoir. Therefore, this simplified method is difficult to meet the needs of high-precision reservoir evaluation under complex geological conditions. The present invention intends to clarify the main controlling factors of the pressure measurement fluidity interpretation results through orthogonal experimental design, and propose a universal and accurate interpretation method for pressure measurement fluidity. Summary of the Invention

[0007] To address these issues, the present invention provides a universal method for interpreting pressure-measured flow. First, numerical simulation and orthogonal experimental design are used to identify the primary influencing factors of pressure-measured flow. Second, a series of parameters are set based on reservoir characteristics and tool type differences to establish a sample library of G correction coefficients for pressure-measured flow interpretation. Finally, a neural network algorithm is used, through extensive training and testing, to construct a relationship model between the G correction coefficient and pumping and reservoir parameters, achieving accurate interpretation of pressure-measured flow.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] A general pressure measurement flow interpretation method includes the following steps:

[0010] S1. Establish a detailed reservoir numerical simulation model for the pumping and pressure measurement process;

[0011] S2. Based on the refined reservoir numerical simulation model established in step S1, determine the main influencing factors of pumping pressure and fluidity through orthogonal experimental design;

[0012] S3, based on the main influencing factors of the pumping pressure test fluidity determined in step S2, using the fine reservoir numerical simulation model established in step S1, establish a pumping pressure test fluidity G correction coefficient database model;

[0013] S4. Based on the pump pressure measurement flow rate G correction coefficient database model established in step S3, a neural network algorithm is used to construct a G correction coefficient prediction model based on the neural network algorithm through training and verification;

[0014] S5. Based on the G correction coefficient prediction model established in step S4, a general reservoir mobility accurate interpretation method based on an iteration method is established.

[0015] Preferably, the step S1 includes establishing a multi-layer radial grid geological model and a multi-phase and multi-component seepage model module, a model operation module and a model result processing module.

[0016] Further preferably, the module for establishing a multi-layer radial grid geological model and a multi-phase multi-component seepage model is based on process parameters of oil and gas reservoirs, fluid properties, probe size, and pumping dynamics, and adopts grid encryption, fluid partitioning, and a multi-phase fluid state equation to establish a fine multi-layer radial grid geological model and a multi-phase multi-component seepage model to simulate fluid flow during the pumping and pressure measurement process;

[0017] The model operation module is to establish a probe model, combine the multi-layer radial grid geological model and the multi-phase and multi-component seepage model established above, and quickly generate a fine reservoir numerical simulation model that truly reflects the pumping and pressure measurement process;

[0018] The model result processing module runs the generated fine reservoir numerical simulation model of the pumping and pressure measurement process and directly outputs relevant dynamic curves.

[0019] Preferably, the orthogonal experimental design in step S2 refers to selecting possible influencing factors of the pressure measurement flow rate based on actual site conditions, setting the range of variation of the influencing factors, and determining the experimental plan using the orthogonal experimental design method.

[0020] Further preferably, the possible influencing factors of the pressure measurement fluidity include one or more combinations of reservoir permeability, fluid viscosity, pressure measurement extraction time and speed, and probe area.

[0021] More preferably, the possible factors affecting the pressure measurement fluidity include reservoir permeability, fluid viscosity, pressure measurement extraction time and speed, and probe area.

[0022] Further preferably, the step S2 is to simulate the experimental scheme based on the fine reservoir numerical simulation model of the pumping and pressure measurement process established in step S1: the interpreted fluidity is calculated using the area integration method, and the fluidity calculation deviation and step difference of each scheme are obtained based on the actual fluid fluidity.

[0023] More preferably, in step S2, the main controlling factors of the pressure flow interpretation result are determined according to the calculated level difference: the level differences are sorted from large to small, the influencing factors with the smallest level difference are eliminated, and the remaining influencing factors are used as the main controlling factors of the pressure flow interpretation result.

[0024] Preferably, step S3 is specifically as follows: according to the actual situation on site, a series of parameters are set for the main influencing factors of the pressure measurement fluidity determined in step S2, a Latin hypercube sampling method is introduced to determine the simulation scheme, the fine reservoir numerical simulation model of the pumping pressure measurement process established in step S1 is used for simulation, the interpreted fluidity is calculated by applying the area integration method, the G correction coefficient of each scheme is calculated based on the actual fluid fluid mobility, and a database model between the main influencing factors of the pressure measurement fluidity and the G correction coefficient is constructed, wherein G=actual fluid fluid mobility / interpreted fluidity.

[0025] Further preferably, the neural network algorithm in step S4 is ANN.

[0026] Preferably, step S5 includes:

[0027] 1) Determine the parameter values of the G correction coefficient prediction model established in step S4 and input the determined parameter values into the G correction coefficient prediction model established in step S4 to obtain the G correction coefficient. Use the area integration method to calculate the fluid mobility. This fluid mobility is the interpreted mobility ( δ,v,t,k ), the parameters include probe area, pressure extraction speed, pressure extraction time, permeability, the interpreted mobility ( δ,v,t,k ), δ is the probe area, v is the pressure extraction speed, t is the pressure extraction time, k is the permeability;

[0028] 2) Calculate the fluid mobility based on the permeability described in 1), which is the true fluid mobility of the reservoir;

[0029] 3) Explain the fluidity ( δ,v,t,k ) is compared with the actual fluidity of the reservoir: if the set termination condition is not met, it is iterated until the set termination condition is met;

[0030] 4) Derive the final fluid mobility and realize the interpretation of universal fluid mobility.

[0031] Further preferably, the parameter values of the G correction coefficient prediction model established in the input S4 are determined according to the following method:

[0032] For the actual pumping pressure test curve, the probe area is determined according to the cable formation test tool and construction technology used;

[0033] Analyze the speed and time data extracted during the actual pressure measurement process to determine the pressure measurement extraction speed and pressure measurement extraction time input to the model;

[0034] Based on the pressure recovery section data, data point encryption and extended regression processing are performed on it, and the initial permeability of the test section formation is given by integrating the coring, logging and well logging data.

[0035] Further preferably, the termination condition is that the interpretation error is less than 3%, and the interpretation error is the interpretation fluidity ( δ,v,t,k ) and the true reservoir mobility.

[0036] The present invention also provides an application of a universal pressure-measurement fluidity interpretation method, which is used for the development of a universal pressure-measurement fluidity interpretation module, and for fluidity interpretation of effective points in a mine or fluidity interpretation of dense points in a mine.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] (1) Through the orthogonal experimental design in step S2, the main factors affecting the interpretation of pressure flow during the pumping pressure test can be systematically analyzed and determined. This method avoids the previous empirical or one-sided selection of influencing factors, thereby providing a more scientific and accurate basis for subsequent model establishment and flow interpretation. Combined with the fine reservoir numerical simulation model established in step S1, the orthogonal experimental design can be carried out in a highly realistic simulation environment, making the determined main controlling factors more consistent with the actual pressure test conditions, thereby significantly improving the accuracy of pressure flow interpretation.

[0039] (2) In step S3, based on the determined main controlling factors, a sample library of the G correction coefficient of the pressure flow rate is constructed using the Latin hypercube sampling method. This process not only takes into account the comprehensiveness of the main controlling factors, but also ensures the diversity and representativeness of the samples through advanced sampling technology. The establishment of the sample library provides a rich and accurate data foundation for the subsequent prediction model, enabling the model to better learn and capture the complex relationship between the pressure flow rate and various influencing factors. Combined with steps S1 and S2, the sample library construction process fully utilizes the sophistication of the numerical simulation model and the scientific nature of the orthogonal experimental design, ensuring the quality and reliability of the data.

[0040] (3) Step S4 uses a neural network algorithm to establish a prediction model for the G correction coefficient. The neural network algorithm has strong nonlinear fitting and generalization capabilities and can automatically learn and extract features from a large amount of sample data, thereby achieving accurate prediction of the G correction coefficient. Combined with steps S1-S3, the prediction model is established based on precise numerical simulation, scientific experimental design, and a comprehensive sample library, making the model more accurate and reliable. At the same time, the efficient computing power of the neural network algorithm also greatly improves the computational efficiency of the pressure flow interpretation, and can quickly process and interpret the actual pressure measurement data.

[0041] (4) The present invention comprehensively utilizes a variety of advanced algorithms and technologies such as numerical simulation, orthogonal experimental design, neural network algorithm and iterative calculation. These algorithms and technologies cooperate with each other and complement each other to form a complete pressure measurement fluidity interpretation system. Numerical simulation provides a detailed geological and fluid model, orthogonal experimental design clarifies the main controlling factors, neural network algorithm realizes intelligent prediction, and iterative calculation further optimizes the fluidity interpretation results. This comprehensive use of multiple algorithms not only improves the accuracy and efficiency of pressure measurement fluidity interpretation, but also enhances the reliability and stability of the interpretation results, and can better meet the requirements for accurate interpretation of pressure measurement fluidity under different probe types, reservoir characteristics and pumping parameters. At the same time, the method proposed by the present invention is particularly suitable for pressure measurement fluidity interpretation under complex geological conditions such as low permeability and dense conditions. Under these geological conditions, traditional pressure measurement fluidity interpretation methods often find it difficult to obtain accurate results. The present invention, by comprehensively considering multiple influencing factors, establishes a detailed numerical simulation model and an intelligent prediction model, which can better meet the requirements for fluidity interpretation under complex geological conditions.

[0042] (6) In view of the current complex seepage laws of low-permeability reservoirs, the large differences in the fluidity interpretation results of different instrument methods for similar reservoirs, and the insufficient interpretation of dynamic data mining such as pumping pressure drop and pressure recovery, the present invention innovatively proposes a universal pressure measurement fluidity interpretation method based on a neural network algorithm. The method is easy to understand and simple and quick to apply. It can quickly obtain accurate pressure measurement fluidity based on the actual pumping pressure measurement dynamics of the target block, and can be used to subsequently calculate the production capacity of the layer section. It has a strong guiding significance for the fluidity interpretation and production capacity evaluation of tight low-permeability reservoirs.

[0043] (7) Based on the orthogonal experimental design to clarify the main controlling factors of the interpretation results of pressure measurement flow rate, the present invention integrates multiple algorithms such as artificial neural network and iterative calculation to propose a universal pressure measurement flow rate interpretation method applicable to different probe types, reservoir characteristics and pumping parameters, which solves the problem that the interpretation results of pressure measurement flow rate of different instruments are very different and the dense points cannot be explained. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic diagram of a numerical simulation model for pumping and pressure measurement in a certain oil reservoir in Example 1 of the present invention;

[0045] Figure 2 This is a schematic diagram of the pumping process of the cable formation test in Example 1 of the present invention;

[0046] Figure 3 This is a schematic diagram of the near-wellbore mud contamination zone in Example 1 of the present invention;

[0047] Figure 4 This is a schematic diagram of oil-water relative permeability in Example 1 of the present invention;

[0048] Figure 5 This is a schematic diagram of a multi-layer radial grid geological model with probes according to Example 1 of the present invention;

[0049] Figure 6 This is a pressure drop-recovery diagram in Example 1 of the present invention;

[0050] Figure 7 This is a ranking diagram of the influence of various factors on the interpretation results of pressure flow measurement in Example 1 of the present invention;

[0051] FIG8( a ) and FIG8 ( b ) are schematic diagrams of training and testing results of the G coefficient correction prediction model based on a neural network in Example 1 of the present invention;

[0052] Figure 9 This is a technical flowchart of the universal pressure measurement flow interpretation method of Example 1 of the present invention. DETAILED DESCRIPTION

[0053] The present invention will be described in further detail below with reference to specific implementation methods and the accompanying drawings, but the implementation methods of the present invention are not limited thereto.

[0054] Example 1

[0055] S1. Establish a detailed reservoir numerical simulation model for the pumping and pressure measurement process;

[0056] Considering the current on-site pumping and pressure measurement process, a reservoir numerical simulation method is used to establish a fine reservoir numerical simulation model that simulates the pumping and pressure measurement process. Model establishment can be mainly divided into three modules: establishing a multi-layer radial grid geological model and a multi-phase and multi-component seepage model, model operation, and model result processing. The establishment of a multi-layer radial grid geological model and a multi-phase and multi-component seepage model is mainly for the establishment of geological models and fluid attribute parameters, that is, based on oil and gas reservoirs, fluid properties and probe size, and pumping dynamic process parameters, grid encryption, fluid partitioning, and multi-phase fluid state equations are used to establish a fine multi-layer radial grid geological model and a multi-phase and multi-component seepage model to simulate fluid flow in the pumping and pressure measurement process; the model operation module is to establish a probe model, combined with the established multi-layer radial grid geological model and multi-phase and multi-component seepage model, to quickly generate a fine reservoir numerical simulation model that truly reflects the pumping and pressure measurement process; the model result processing module can directly output the pumping fluid volume and bottom hole flow pressure dynamic curves, such as Figure 1 , a schematic diagram of the numerical simulation model of pumping pressure measurement in a certain oil reservoir is given.

[0057] Specifically, Eclipse numerical modeling software was used to construct a multi-layer radial grid geological model and simulate the dynamic laws of the pressure measurement pumping process based on the multi-phase and multi-component seepage model.

[0058] The parameters of the multi-layer radial grid geological model are as follows:

[0059] (1) Model shape and size: The model adopts a cylindrical shape with a radius of 1000 cm and a thickness of 50 cm. This size design can fully consider the fluid flow characteristics and formation heterogeneity in the near-wellbore area, while taking into account both computational efficiency and accuracy. Figure 2 As shown in the figure, a schematic diagram of the cable formation test pumping process is given.

[0060] (2) Grid division: With the wellbore as the center, a multi-layer radial grid division is used with a grid number of 10,000. The drilling fluid filtrate invasion in the near-wellbore area has a radius of less than 50 cm, and the solid phase contamination area has a radius of 1 cm. Local grid refinement is used to describe the area with a grid size of 0.1 cm. Local grid refinement can more accurately capture the fluid flow and pressure changes in the near-wellbore area, thereby improving the accuracy of the simulation.

[0061] (3) Permeability and fluid distribution settings

[0062] The permeability of the original formation is set to 0.01-10.0mD based on actual reservoir data, and the porosity is 0.104. A solid phase contamination zone is set within a length of 1 cm around the wellbore. The permeability in this area is significantly reduced. The degree of reduction is controlled by the degree of solid phase contamination and is 10%-90% of the permeability of the original formation. In the near-wellbore area, within a radius of 50 cm, a filtrate invasion zone is set, and the permeability is 80% of the original formation. Figure 3 As shown in Figure 1, a schematic diagram of the mud contamination zone near the wellbore is given.

[0063] The model contains two fluids: crude oil and drilling fluid filtrate. The crude oil composition is C1-C6 + , N2, and CO2, with a viscosity range of 0.205-7.5 cP. The drilling fluid filtrate becomes primarily formation water with a viscosity of 0.204 cP. Near the wellbore, the fluid in the filtrate invasion zone is primarily drilling fluid filtrate, while in areas further from the wellbore, the fluid is primarily crude oil. The model considers the impact of water saturation on fluid flow, setting the initial water saturation of the undisturbed formation at 0.4. Due to drilling fluid invasion, the water saturation in the contaminated area increases to 0.8. This difference in water saturation simultaneously affects the relative permeability and flow resistance of the reservoir fluid, making it more realistic.

[0064] The multi-phase multi-component seepage model is established as follows:

[0065] The original reservoir pressure of the model is 38.5 MPa, and the original reservoir temperature is 120°C. Considering the complex phase change characteristics during the reservoir fluid pressure measurement process, a multi-phase and multi-component seepage simulation model is adopted, and the viscosity, density, relative permeability of the fluid and the heterogeneity of the reservoir are comprehensively considered. The establishment of the model is based on Darcy's seepage law and combined with multiphase flow theory to accurately simulate the fluid flow behavior in the actual reservoir. The viscosity of the oil phase fluid is relatively high, about 0.205-7.5 cP, and the relative permeability of the oil phase decreases with the increase of water saturation. The viscosity of the water phase fluid is relatively low, about 0.2 cP, and the relative permeability of the water phase 𝑘𝑟𝑤 increases with the increase of water saturation, such as Figure 4 shown.

[0066] The establishment of the probe model is specifically as follows:

[0067] (1) Probe size: The probe size is set according to the actual reservoir cable formation test conditions, and the size range is 0.04-5.97 in. 2 .

[0068] (2) Probe position: The well is located in the center of the model, and the probe depth is in the middle of the model. The pressure at the contact point between the probe and the formation is taken as the cable formation test pressure. By changing the pressure measurement extraction time and pressure measurement extraction speed parameters in the model, the simulated pressure change curve under different conditions can be obtained, such as Figure 5 As shown, a multi-layer radial grid geological model with probes is given.

[0069] S2. Based on the refined reservoir numerical simulation model established in step S1, determine the main influencing factors of pumping pressure and fluidity through orthogonal experimental design;

[0070] Considering the actual situation on site, reservoir permeability, fluid viscosity, pressure measurement extraction time and speed, and probe area were selected as the influencing factors of pressure measurement flowability. The range of variation of each factor parameter was set. The orthogonal experimental design method was used, and the orthogonal design table L16 (45) was selected to determine the experimental plan. A total of 16 groups of experiments were conducted, and each factor had 4 levels. Table 1 is the statistical table of the orthogonal experimental design plan.

[0071] Table 1 Orthogonal experimental scheme design table of factors affecting pressure and flow control

[0072]

[0073] The fine reservoir numerical simulation model of the pumping and pressure measurement process established in step S1 is used to simulate and calculate the above-designed schemes. The interpreted mobility is calculated using the area integration method. The mobility calculation deviation and step difference of each scheme are obtained based on the actual fluid mobility.

[0074] The simulation calculation is specifically as follows: inputting reservoir permeability, fluid viscosity, pressure measurement extraction time and speed, and probe area, using the fine reservoir numerical simulation model of the pumping process established in step S1, and calculating the interpreted mobility using the area integration method.

[0075] Area integration method, a method of calculating fluid flow rate by using the area integration of the pressure curve. The area integration method is to integrate the pressure over time, and this method is a further improvement on the standard flow calculation formula. Assume that the experimental core is an infinite formation, fully saturated with liquid, and has constant porosity, permeability and storage capacity coefficient ΦCt. There is a circular wellbore passing through the formation in the vertical direction. When the wellhead pressure is greater than the formation pressure, no filtrate invades the formation; when the wellhead pressure is less than the formation pressure, the filtrate flows into the intermediate container through the probe. According to the area integration method calculation formula, it is only necessary to time-integrate the pressure signal from the pressure drop during pumping to the pressure recovery and equilibrium after the pump is stopped, and calculate the total volume of the pumped fluid. For example Figure 6 As shown in Figure 1, a pressure drop-recovery diagram is given. This method is applicable to various situations in fluidity testing: non-constant pumping speed, mud adhesion to the wellbore wall, and anisotropic formations. The main calculation formulas are formulas (1) to (3).

[0076] (1)

[0077] (2)

[0078] From formulas 1 and 2, we can deduce:

[0079] (3)

[0080] in, k is the permeability, mD ; μ is the fluid viscosity, cP ; P fl is the flow pressure, MPa ; P i is the initial formation pressure, MPa ; t 2 is the time when pumping starts, s ; t max It is the time when the pressure returns to a stable state after the pump is stopped. s ; V T for T Total pumped fluid volume at time, cm 3 ; V t for tTotal pumped fluid volume at time, cm 3 ; V The total pumping volume of fluid during the experimental test cm 3 ; G Rf is the experimental correction coefficient value; Δp is the pressure drop during the manometric extraction process, MPa ; Δt is the time during the pressure measurement and extraction process, s ; is the flow efficiency function; is the perforation hole radius, cm ; is the wellbore radius, cm ; is the horizontal permeability of the formation, mD ; is the vertical permeability of the formation, mD .

[0081] In the actual cable formation test process, the correction coefficient value G Rf Only equipment factors and some formation factors such as permeability are considered, and other formation factors such as fluid viscosity and process factors are not considered, which leads to a large error between the interpreted mobility and the actual mobility. Therefore, the present invention will add other formation factors and process factors to the following experiments. G Rf The influence of the value can form a more accurate interpretation method of pressure measurement flow.

[0082] The actual fluid mobility is calculated using formula (4):

[0083] (4)

[0084] in, is the fluidity, mD / cP ; k is the permeability, mD ; μ is the fluid viscosity, cP The permeability and fluid viscosity are artificially set in the fine reservoir numerical simulation model based on the target area data, specifically the permeability and fluid viscosity input into the model for each experimental scheme.

[0085] like Figure 7 , gives the ranking of the influence of each factor on the fluidity interpretation results:

[0086] Through differential analysis of different experimental schemes, the degree of influence of each factor on the accuracy of pressure-measurement fluidity interpretation was clarified. The specific ranking is: probe area > pressure-measurement extraction time > reservoir permeability > pressure-measurement extraction rate > fluid viscosity. After eliminating the influencing factor with the smallest differential, the remaining influencing factors are considered the main controlling factors of the pressure-measurement fluidity interpretation results. A larger probe area can reduce fluid flow resistance and improve test efficiency, while a smaller probe area can lead to larger fluidity interpretation errors, especially in low-permeability reservoirs where pumping out is prone to occur. Pressure-measurement time and pressure-measurement rate are also important influencing factors. Together, they determine the total amount of fluid extracted and the flow state, which in turn affects the shape of the pressure-measurement curve and the fluidity interpretation results. Although reservoir permeability and fluid viscosity have a certain impact on fluidity interpretation, their influence is relatively small among the above factors.

[0087] S3, based on the main influencing factors of the pumping pressure test fluidity determined in step S2, using the fine reservoir numerical simulation model established in step S1, establish a pumping pressure test fluidity G correction coefficient database model;

[0088] The main factors affecting the pressure flow rate determined in step S2, namely the probe area, pressure extraction speed and time, and reservoir permeability, are set to a range of 0.1 to 10 in according to the actual situation on site. 2 , 0.01~10cc / s, 5~50s and 0.001~10mD, the Latin hypercube sampling method was introduced to determine the simulation scheme, a total of 1000 groups, the numerical simulation model establishment method established in step S1 was used to simulate respectively, the area integration method was applied to calculate the interpreted mobility, and the actual fluid mobility was used as the benchmark to calculate the G correction coefficient of each scheme, that is, G( δ,v,t,k ), and then build a database model between the main influencing factors of pressure measurement and flow rate and the G correction coefficient.

[0089] Specifically, the database model consists of independent variables and dependent variables, wherein the independent variables are pressure measurement extraction speed, pressure measurement extraction time, probe size, and reservoir permeability, and the dependent variable is the G correction coefficient, where G = actual fluid mobility / interpreted mobility.

[0090] The interpretation of fluidity is the same as formula (3).

[0091] The actual fluid mobility is the same as formula (4), where the fluid viscosity is determined by the proportion of each component and the corresponding reservoir temperature and pressure conditions set in the fine reservoir numerical simulation model. The corresponding fluid viscosity can be obtained by running the fine reservoir numerical simulation model.

[0092] S4, based on the pump pressure measurement flow rate G correction coefficient database model established in step S3, using a neural network algorithm, through training and verification, to construct a G correction coefficient prediction model based on the neural network algorithm;

[0093] The core formula of the G correction coefficient prediction model is shown in (5), which introduces a key coefficient G( δ,v,t,k ), explaining the fluidity ( ,v,t, ) is calculated as follows:

[0094] (5)

[0095] in, k is the permeability, mD ; μ is the fluid viscosity, cP ; P f （t） for t Constant flow pressure, MPa ; P i is the initial formation pressure, MPa ; t 0 is the time when pumping starts, s ; V f （T） for T Total pumped fluid volume at time, cm 3 ; V f （t 0 ） for t Total pumped fluid volume at time 0, cm 3 ; G ( δ,v,t,k ) is a dimensionless correction factor, which is used to adjust and correct the interpretation results of pressure flow measurement to improve the accuracy of the interpretation.

[0096] The calculation method of actual fluid mobility is the same as formula (4).

[0097] Based on the pump pressure measurement flow G correction coefficient database model established in S3, the artificial intelligence neural network algorithm ANN is used to take 70% of the data in the database as the training set and the remaining 30% as the validation set. Reasonable neural network input parameters are set for training to ensure that the training accuracy reaches more than 98%. The training results are shown in Figure 8 (a), and the training accuracy can reach 99.7%. Based on this, according to the trained model, the validation set is used to test the accuracy of the training model to ensure that the prediction accuracy reaches more than 95%. As shown in Figure 8 (b), the accuracy reaches 98%, which makes the model highly credible. By comparing with the experimental results, the results show that the flow interpretation error of the established model is 1.2%~4.6%, which confirms the accuracy and reliability of the established G correction coefficient prediction model, among which the interpretation error is the interpretation of flow ( δ,v,t,k ) and the actual fluid flow rate.

[0098] S5, based on the G correction coefficient prediction model established in step S4, establishing a general reservoir mobility accurate interpretation method based on an iteration method;

[0099] For the actual pumping pressure test curve, the probe area size is determined according to the cable formation test tool and construction technology used; the speed and time data extracted during the actual pressure test process are analyzed to determine the pressure test extraction speed and pressure test extraction time input to the model; based on the pressure recovery section data, data point encryption and extended regression processing are performed on it, and the initial permeability of the test section formation is given based on the core sampling, logging and well logging data. The fluid mobility is calculated based on this permeability, and this fluid mobility is the true fluid mobility of the reservoir; the above parameters, namely the probe area, pressure test extraction speed, pressure test extraction time, and permeability, are input into the G correction coefficient prediction model established in S4 to obtain the G correction coefficient. The fluid mobility is calculated using the area integration method. This fluid mobility is the interpreted mobility ( δ,v,t,k ), explaining the fluidity ( δ,v,t,k ) is compared with the actual fluidity of the reservoir: if the set termination condition is not met, iterative calculation is performed until the set termination condition is reached, and the final fluid mobility is derived to achieve the interpretation of universal fluid mobility.

[0100] The termination condition is: the interpretation error is less than 3%.

[0101] The interpretation error is the interpretation flow ( δ,v,t,k ) and the true reservoir mobility.

[0102] The true fluidity of the reservoir is calculated according to formula (4), where the actual fluid viscosity is obtained based on the field pump sampling data and fluid samples, and the physical properties of the fluid such as viscosity are obtained by PVT analysis in the ground laboratory.

[0103] Explain the fluidity ( δ,v,t,k )The calculation formula is the same as (5).

[0104] Based on the area integration method and seepage theory, this paper establishes a new universal pressure measurement flow precision interpretation model, aiming to improve the accuracy and reliability of flow interpretation. The core formula of the new model is shown in (5), in which a key coefficient G( δ,v,t,k ), which comprehensively considers the reservoir characteristic permeability k, the pressure measurement process parameters in the well conditions, including the probe area δ, the pressure measurement extraction speed v and pressure measurement time t By incorporating these factors into the model, the new model can more comprehensively reflect the flow characteristics of reservoir fluids during actual pressure measurement, thus providing a more accurate theoretical basis for mobility interpretation.

[0105] Based on this new model, this study established a complete set of universal pressure flow interpretation methods. The technical route of this method is as follows: Figure 9 As shown in the figure, it covers the whole process from data collection, model parameter determination to mobility interpretation. It mainly includes the following steps:

[0106] (1) Data acquisition: Data acquisition is the foundation of the entire interpretation process, and its quality directly affects the accuracy of the final interpretation results. Raw data is obtained through field pressure testing, including key parameters such as pressure change curves, pressure measurement time, and pressure measurement speed. At the same time, combined with laboratory analysis, reservoir characteristic parameters such as permeability, porosity, and fluid viscosity are obtained.

[0107] (2) Model parameter determination: Key parameters in the model are determined, including the reservoir characteristic parameter permeability and the pressure measurement process parameters probe size, extraction rate, and extraction time. These parameters are used to calculate the coefficient G in the model, which comprehensively reflects the influence of reservoir characteristics, pressure measurement technology, and well conditions on mobility.

[0108] (3) Mobility interpretation: After determining the model parameters, the pressure curve is processed using the area integration method. The processed pressure curve is interpreted for mobility in combination with the seepage theory to calculate the true mobility of the reservoir.

[0109] (4) Result verification: The accuracy of the interpretation results is evaluated by comparing them with the actual flow data. If there is a large deviation between the interpretation results and the actual data, the model parameters are adjusted and the interpretation is repeated until the interpretation results are consistent with the actual data.

Claims

1. A general pressure flow interpretation method, characterized in that: The following steps are involved: S1. Establish a detailed reservoir numerical simulation model for the pumping and pressure measurement process; S2. Based on the refined reservoir numerical simulation model established in step S1, determine the main influencing factors of pumping pressure and fluidity through orthogonal experimental design; S3. Based on the main influencing factors of the pumping pressure measurement fluidity determined in step S2, a pumping pressure measurement fluidity G correction coefficient database model is established using the fine reservoir numerical simulation model established in step S1, wherein the pumping pressure measurement fluidity G correction coefficient database model is a database model constructed based on the main influencing factors of the pressure measurement fluidity and the G correction coefficient; S4. Based on the pump pressure measurement flow rate G correction coefficient database model established in step S3, a neural network algorithm is used to construct a G correction coefficient prediction model based on the neural network algorithm through training and verification; S5. Based on the G correction coefficient prediction model established in step S4, a general reservoir mobility accurate interpretation method based on an iterative method is established; The step S5 is specifically as follows: 1) Determine the parameter values of the G correction coefficient prediction model established in step S4 and input the determined parameter values into the G correction coefficient prediction model established in step S4 to obtain the G correction coefficient. Use the area integration method to calculate the fluid mobility. This fluid mobility is the interpreted mobility ( δ,v,t,k ), the parameters include probe area, pressure extraction speed, pressure extraction time, permeability, the interpreted mobility ( δ,v,t,k ), δ is the probe area, v is the pressure extraction speed, t is the pressure extraction time, k is the permeability; 2) Calculate the fluid mobility based on the permeability described in 1), which is the true fluid mobility of the reservoir; 3) Explain the fluidity ( δ,v,t,k ) is compared with the actual fluidity of the reservoir: if the set termination condition is not met, it is iterated until the set termination condition is met; 4) Derive the final fluid mobility and realize the interpretation of universal fluid mobility.

2. The universal pressure measurement flow interpretation method according to claim 1, characterized in that: The orthogonal experimental design described in step S2 refers to selecting possible influencing factors of pressure measurement flowability based on consideration of actual site conditions, setting the range of variation of the influencing factors, and determining the experimental scheme using the orthogonal experimental design method; step S2 is based on the fine reservoir numerical simulation model of the pumping pressure measurement process established in step S1, and simulates and calculates the experimental scheme: the area integration method is used to calculate the interpreted flowability, and the actual fluid flowability is used as a benchmark to obtain the flowability calculation deviation and step difference of each scheme.

3. The universal pressure measurement flow interpretation method according to claim 2, characterized in that: The possible factors affecting the pressure measurement fluidity include one or more combinations of reservoir permeability, fluid viscosity, pressure measurement extraction time and speed, and probe area.

4. The universal pressure measurement flow interpretation method according to claim 2, characterized in that: In step S2, the main controlling factors of the pressure flow interpretation result are determined according to the calculated step difference size: the step differences are sorted from large to small, the influencing factor with the smallest step difference is eliminated, and the remaining influencing factors are used as the main controlling factors of the pressure flow interpretation result.

5. The universal pressure measurement fluidity interpretation method according to claim 1, characterized in that: Step S3 specifically includes: according to the actual situation on site, a series of parameters are set for the main influencing factors of the pressure measurement fluidity determined in step S2, and the Latin hypercube sampling method is introduced to determine the simulation scheme. The fine reservoir numerical simulation model of the pumping pressure measurement process established in step S1 is used for simulation, and the interpreted fluidity is calculated by applying the area integration method. Based on the actual fluid fluid mobility, the G correction coefficient of each scheme is calculated, and a database model between the main influencing factors of the pressure measurement fluidity and the G correction coefficient is constructed, where G = actual fluid mobility / interpreted fluid mobility.

6. The universal pressure measurement flow interpretation method according to claim 1, characterized in that: The neural network algorithm in step S4 is ANN.

7. The universal pressure measurement fluidity interpretation method according to claim 1, characterized in that: The parameter values of the G correction coefficient prediction model established in the input S4 are determined according to the following method: For the actual pumping pressure test curve, the probe area is determined according to the cable formation test tool and construction technology used; Analyze the speed and time data extracted during the actual pressure measurement process to determine the pressure measurement extraction speed and pressure measurement extraction time input to the model; Based on the pressure recovery section data, data point encryption and extended regression processing are performed on it, and the initial permeability of the test section formation is given by integrating the coring, logging and well logging data.

8. The universal pressure measurement fluidity interpretation method according to claim 1, characterized in that: The termination condition is that the interpretation error is less than 3%, and the interpretation error is the interpretation flow ( δ,v,t,k ) and the true reservoir mobility.

9. The universal pressure measurement fluidity interpretation method according to any one of claims 1 to 8, and its application in the development of a universal pressure measurement fluidity interpretation module, and in the interpretation of fluidity at effective points or dense points in a mine.

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