Shale reservoir pore pressure prediction method and device, equipment and medium

Abstract

The invention relates to the technical field of shale reservoir rock mechanics evaluation, in particular to a shale reservoir pore pressure prediction method and device, equipment and a medium. The method comprises the steps that a logging curve of a research area is obtained, and the organic carbon content, the total porosity, the clay content and the water saturation are determined according to the logging curve; establishing a longitudinal wave time difference correction model according to the longitudinal wave time difference theoretical value of the quartz, the organic carbon content, the total porosity, the clay content and the water saturation, and obtaining the corrected longitudinal wave time difference by using the correction model; according to the logging curve, effective vertical stress of the actually-measured section of the target well is determined; and establishing a regression model of the corrected longitudinal wave time difference and the effective vertical stress, and determining the pore pressure according to the regression model. The invention aims to solve the problems that the current method for predicting the pore pressure through the actually measured longitudinal wave velocity is greatly influenced by organic carbon, porosity, lithology, fluid components and the like, so that the error of the final prediction result is large, and the reservoir prediction precision requirement cannot be met.

Description

Technical Field

[0001] This invention relates to the field of rock mechanics evaluation technology for shale reservoirs, and in particular to a method, apparatus, equipment, and medium for predicting pore pressure in shale reservoirs. Background Technology

[0002] Formation pore pressure refers to the pressure acting on the pore fluid in the formation. Currently, there are many methods for predicting pore pressure in shale reservoirs, mainly including the Eaton method, the equivalent depth method, and the Bowers method. The Eaton method and the equivalent depth method are based on the principle of undercompaction, constructing an acoustic compaction trend line and using the amplitude of the deviation of the P-wave transit time from the compaction trend line to predict pore pressure. The Bowers method is based on the principle of hydrocarbon generation and pressurization, using the amplitude of the P-wave velocity and the acoustic velocity of the original uncompacted formation to predict pore pressure. All of these methods directly use measured P-waves to predict pore pressure. However, because the P-wave transit time or velocity is greatly affected by organic carbon, porosity, lithology, and fluid composition, the calculation results have large errors and cannot meet the needs of reservoir logging evaluation. Summary of the Invention

[0003] This invention proposes a method, apparatus, equipment, and medium for predicting pore pressure in shale reservoirs. This addresses the problem that current methods for predicting pore pressure based on measured longitudinal wave velocity are greatly affected by factors such as organic carbon, porosity, lithology, and fluid composition, resulting in large errors in the final prediction results and failing to meet the accuracy requirements for reservoir prediction.

[0004] According to one aspect of the present invention, a method for predicting pore pressure in shale reservoirs is provided, comprising:

[0005] Obtain well logging curves for the study area, and determine organic carbon content, total porosity, clay content, and water saturation based on the well logging curves.

[0006] Based on the theoretical value of P-wave transit time of quartz and the organic carbon content, total porosity, clay content and water saturation, a P-wave transit time correction model is established, and the corrected P-wave transit time is obtained using the correction model.

[0007] Based on the logging curves, determine the effective vertical stress of the measured section of the target well;

[0008] A regression model is established between the corrected longitudinal wave time difference and the effective vertical stress. Based on the regression model, the pore pressure is determined.

[0009] Preferably, the method for determining the organic carbon content based on the well logging curve includes:

[0010] Based on the sonic transit time and deep lateral resistivity curve in the well logging curve, the organic carbon content is determined using formula (1).

[0011] TOC = a × DT + b × log(R) LLD )-c (1);

[0012] In the formula: TOC is the organic carbon content, %; DT is the acoustic transit time, us·ft -1 ;R LLD ρ is the deep lateral resistivity, in Ω·m; a, b, and c are coefficients.

[0013] Preferably, the method for determining total porosity based on the well logging curve includes:

[0014] Based on the compensated neutron curve, lithological density curve and sonic transit time curve in the well logging curve, the total porosity is determined using formula (2).

[0015] Φ 总 =a×DT+b×CNL-c×DEN+d (2);

[0016] Where: Φ 总 Total porosity, %; DT is acoustic transit time, µs·ft. -1 CNL represents compensated neutrons, f; DEN represents lithological density, g / cm³. 3 a, b, c, and d are coefficients.

[0017] Preferably, the method for determining the clay content based on the well logging curve includes:

[0018] Acquire elemental logging data and XRD whole-rock analysis experimental data for the study area;

[0019] Based on elemental logging data and XRD whole-rock analysis experimental data, the conversion relationship between mineral content and elemental content was determined, and the clay content was determined based on the conversion relationship.

[0020] If elemental logging data cannot be obtained, a clay content regression calculation model is established based on the XRD whole-rock analysis experimental data and conventional logging curves, and the clay content is determined according to the model.

[0021] Preferably, the method for determining water saturation based on the well logging curve includes:

[0022] Based on the well logging curves, the water saturation is determined using a saturation model, wherein the saturation model is: Archie saturation model, or double water saturation model, or Simondu saturation model, or Indonesian saturation model.

[0023] Preferably, the water saturation is determined using the Simon saturation model described in the formula based on the well logging curve;

[0024] The Simon saturation model includes:

[0025]

[0026] In the formula: S w For water saturation, f; R w R is the resistivity of formation water, in Ω·m; t R represents the resistivity of the original formation, in Ω·m. cl V represents the resistivity of clay, in Ω·m. cl denoted as clay content (f); Φ as effective porosity (f); m as cementation index; n as saturation index; and a and b as coefficients.

[0027] Preferably, the P-wave time difference correction model includes:

[0028]

[0029] Where: Δt c The P-wave time difference after multi-parameter correction is expressed in μs·ft. -1 Δt is the measured P-wave time difference, us·ft -1 TOC represents total organic carbon content, in g / g; ρ b Lithological density, g / cm³ 3 Φ represents total porosity, v / v; V sh S represents the clay content, v / v; w ν represents water saturation, v / v.

[0030] Preferably, the method for determining the effective vertical stress of the measured section of the target well based on the logging curve includes:

[0031] The overlying formation pressure is determined based on the well logging curves.

[0032] Based on the overlying stratum pressure, the effective vertical stress is determined using the effective vertical stress equation.

[0033] Preferably, the method for determining the overlying formation pressure based on the well logging curve includes:

[0034] Based on the density curve in the well logging curve, the overlying formation pressure is determined using formula (5);

[0035]

[0036] Where: σ v ρ is the overlying formation pressure, MPa; D is the vertical depth, m; z The density at depth z is kg / m³. 3 .

[0037] Preferably, the effective vertical stress equation includes:

[0038] σ ve =σ v -P p (6);

[0039] Where: σ ve For effective vertical stress, MPa; σ v The overlying formation pressure is expressed in MPa; P p ρ represents the formation pore pressure, in MPa.

[0040] According to one aspect of the present invention, a shale reservoir pore pressure prediction device is provided, comprising:

[0041] The acquisition unit is used to acquire well logging curves in the study area and determine organic carbon content, total porosity, clay content, and water saturation based on the well logging curves.

[0042] The correction unit is used to establish a longitudinal wave time difference correction model based on the theoretical value of longitudinal wave time difference of quartz, as well as the organic carbon content, total porosity, clay content and water saturation, and to obtain the corrected longitudinal wave time difference using the correction model.

[0043] An effective vertical stress determination unit is used to determine the effective vertical stress of the measured section of the target well based on the logging curve.

[0044] The pore pressure determination unit is used to establish a regression model between the corrected longitudinal wave time difference and the effective vertical stress, and to determine the pore pressure based on the regression model.

[0045] According to one aspect of the present invention, an electronic device is provided, comprising:

[0046] processor;

[0047] Memory used to store processor-executable instructions;

[0048] The processor is configured to execute the above-described shale reservoir pore pressure prediction method.

[0049] According to one aspect of the present invention, a computer-readable storage medium is provided having computer program instructions stored thereon, which, when executed by a processor, implement the above-described shale reservoir pore pressure prediction method.

[0050] The present invention has at least the following beneficial effects:

[0051] This invention proposes a method, device, equipment, and medium for predicting pore pressure in shale reservoirs. By establishing a P-wave time difference correction model to correct the P-wave time difference, and using the corrected P-wave time difference, the pore pressure is determined using the effective vertical stress principle, which improves the prediction accuracy and ensures the reliability of subsequent reservoir logging evaluation. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present invention and, together with the specification, serve to explain the technical solutions of the present invention.

[0053] Figure 1 A flowchart illustrating a shale reservoir pore pressure prediction method according to an embodiment of the present invention is shown;

[0054] Figure 2 This diagram shows the P-wave time difference correction results of the target well in the Qingshankou Formation in the Gulong area according to an embodiment of the present invention.

[0055] Figure 3 This diagram illustrates the overburden pressure curve of the target well according to an embodiment of the present invention.

[0056] Figure 4 This diagram illustrates the relationship between the pre-correction P-wave time difference and the effective vertical stress according to an embodiment of the present invention.

[0057] Figure 5 The diagram showing the relationship between the corrected P-wave time difference and the effective vertical stress according to an embodiment of the present invention is illustrated.

[0058] Figure 6 This figure shows a comparison of the pore pressure prediction results of the Qingshankou Formation in Well XX in the Gulong area using the method according to an embodiment of the present invention and the Bowers method.

[0059] Figure 7 A comparison diagram of predicted formation pore pressure and measured formation pore pressure before and after correction is shown in the embodiment of the present invention for well XX of the Qingshankou Formation in the Gulong area.

[0060] Figure 8 This diagram shows a plane contour map of the predicted pressure coefficient of the Q9 segment of the Qingshankou Formation in the Gulong area, according to an embodiment of the present invention.

[0061] Figure 9 The diagram shows the relationship between daily oil production and pressure coefficient of the Q9 oil reservoir in the Qingshankou Formation of the Gulong area, according to an embodiment of the present invention. Detailed Implementation

[0062] Various exemplary embodiments, features, and aspects of the present invention will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0063] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0064] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0065] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can be practiced without certain specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order to highlight the spirit of the invention.

[0066] Figure 1 A flowchart illustrating a shale reservoir pore pressure prediction method according to an embodiment of the present invention is shown; Figure 2 This diagram shows the results of P-wave time difference correction in the Qingshankou Formation of Well XX in the Gulong area according to an embodiment of the present invention. Figure 3 This diagram illustrates the overlying formation pressure curve of well XX according to an embodiment of the present invention. Figure 4 This diagram illustrates the relationship between the pre-correction P-wave time difference and the effective vertical stress according to an embodiment of the present invention. Figure 5 The diagram showing the relationship between the corrected P-wave time difference and the effective vertical stress according to an embodiment of the present invention is illustrated. Figure 6 This figure shows a comparison of the pore pressure prediction results of the Qingshankou Formation in Well XX in the Gulong area using the method according to an embodiment of the present invention and the Bowers method. Figure 7 A comparison diagram of predicted formation pore pressure and measured formation pore pressure before and after correction is shown in the embodiment of the present invention for well XX of the Qingshankou Formation in the Gulong area. Figure 8 This diagram shows a plane contour map of the predicted pressure coefficient of the Q9 segment of the Qingshankou Formation in the Gulong area, according to an embodiment of the present invention. Figure 9 This diagram illustrates the relationship between daily oil production and pressure coefficient in the Q9 oil reservoir of the Qingshankou Formation in the Gulong area, according to an embodiment of the present invention. Figure 1-9As shown, a method for predicting pore pressure in shale reservoirs includes: Step S01: Obtaining well logging curves for the study area, and determining the organic carbon content, total porosity, clay content, and water saturation based on the well logging curves; Step S02: Establishing a P-wave transit time correction model based on the theoretical value of P-wave transit time of quartz and the organic carbon content, total porosity, clay content, and water saturation, and obtaining the corrected P-wave transit time using the correction model; Step S03: Determining the effective vertical stress of the measured section of the target well based on the well logging curves; Step S04: Establishing a regression model between the corrected P-wave transit time and the effective vertical stress, and determining the pore pressure based on the regression model.

[0067] The shale reservoir pore pressure prediction method provided by this invention specifically includes the following steps:

[0068] Step S01: Obtain the logging curve of the target well, and determine the organic carbon content, total porosity, clay content and water saturation based on the logging curve.

[0069] In this embodiment of the invention, when calculating formation pore pressure using P-wave transit time, in addition to being related to formation pressure, factors such as organic carbon, porosity, lithology, and fluid composition also have a significant impact on P-wave transit time. Therefore, when establishing a pore pressure calculation model, it is necessary to first correct the P-wave transit time, and then, based on the effective stress principle, use the corrected P-wave transit time to establish a formation pore pressure prediction model.

[0070] Taking the Qingshankou Formation in the Gulong area of ​​the study area as an example, a calculation model for organic carbon, porosity, clay content, and water saturation of the Qingshankou Formation in the Gulong area was established using well logging data. Based on this model, the organic carbon, porosity, clay content, and water saturation of wells with measured pressure were calculated.

[0071] In this invention, the method for determining the organic carbon content based on the logging curve includes: determining the organic carbon content using formula (1) based on the sonic transit time and deep lateral resistivity curve in the logging curve;

[0072] TOC = a × DT + b × log(R) LLD )-c (1);

[0073] In the formula: TOC is the organic carbon content, %; DT is the acoustic transit time, us·ft -1 ;R LLD ρ is the deep lateral resistivity, in Ω·m; a, b, and c are coefficients.

[0074] In this embodiment of the invention, based on the above formula (1), using the well logging curves of the study area, namely the sonic transit time and the deep lateral resistivity curve, a mathematical statistical regression method is used to obtain the values ​​of coefficients a, b, and c of formula (1), thereby establishing a calculation model for the organic carbon content of the study area. The organic carbon content of the study area is calculated based on this organic carbon content calculation model.

[0075] Taking the Qingshankou Formation in the Gulong area of ​​the study area as an example, based on the logging response characteristics of source rocks in the Gulong area, the acoustic transit time and deep lateral resistivity curves were selected. Using the core analysis data of 87 layers from 6 wells, a logging calculation model for the organic carbon content (TOC) of the Qingshankou Formation oil layer was established, as shown in Equation (1-1).

[0076] TOC = 0.05 × DT + 3.78 × log(R) LLD -6.47 (1-1).

[0077] In this invention, the method for determining total porosity based on the logging curve includes: determining the total porosity using formula (2) based on the compensated neutron curve, lithological density curve and sonic transit time curve in the logging curve;

[0078] Φ 总 =a×DT+b×CNL-c×DEN+d (2);

[0079] Where: Φ 总 Total porosity, %; DT is acoustic transit time, µs·ft. -1 CNL represents compensated neutrons, f; DEN represents lithological density, g / cm³. 3 a, b, c, and d are coefficients.

[0080] In this embodiment of the invention, based on the above formula (2), using the well logging curves of the study area, namely the compensated neutron curve, lithological density curve, and sonic transit time curve, a mathematical statistical regression method is used to obtain the values ​​of coefficients a, b, c, and d in formula (2), thereby establishing a total porosity calculation model for the study area. Based on this total porosity calculation model, the total porosity value of the study area is calculated.

[0081] Taking the Qingshankou Formation in the Gulong area of ​​the study area as an example, the porosity data obtained from the analysis (experiment) of 98 core layers from 7 wells in the Qingshankou Formation oil layer in the Gulong area were used to select the three porosity curves, namely the compensated neutron curve, the lithological density curve and the sonic transit time curve. The conventional logging calculation model for the total porosity of the Qingshankou Formation was determined by using the mathematical statistical regression method, as shown in Equation (2-1).

[0082] Φ 总 =0.11×DT+9.73×CNL-4.67×DEN+5.05(2-1).

[0083] In this invention, the method for determining clay content based on the well logging curve includes: acquiring elemental well logging data and XRD whole-rock analysis experimental data of the study area; determining the conversion relationship between mineral content and elemental content based on the elemental well logging data and XRD whole-rock analysis experimental data, and determining the clay content based on the conversion relationship; if elemental well logging data cannot be obtained, then establishing a clay content regression calculation model using conventional well logging curves based on the XRD whole-rock analysis experimental data, and determining the clay content based on the model.

[0084] In this embodiment of the invention, the mud content in the study area is calculated using XRD whole-rock analysis experimental data (mineral composition data). Based on the Herron model principle, the equation is solved using an optimization method to obtain the conversion relationship between mineral content and element content in this area.

[0085] Elemental logging can accurately calculate the elemental dry weight of a formation. Therefore, by applying well-calibrated mineral content (mineral composition data) obtained from XRD experimental data, the elemental content (elemental dry weight) obtained from elemental logging can be calibrated to determine the conversion relationship between mineral content and elemental content. The mineral composition obtained from elemental logging in the study area can be finely processed and interpreted to obtain the mud content (clay content) parameter.

[0086] For wells without elemental logging data, based on the clay content in the mineral components obtained from XRD whole-rock analysis experiments, a regression calculation model for shale content is established using conventional logging curves to obtain the shale content of the entire oil reservoir.

[0087] In this invention, the method for determining water saturation based on the well logging curve includes: determining water saturation based on the well logging curve using a saturation model, wherein the saturation model is: Archie saturation model, or dual water saturation model, or Simondu saturation model, or Indonesian saturation model.

[0088] In this embodiment of the invention, the Archie saturation model is primarily used to calculate water saturation in sandstone reservoirs. Other models, such as the dual-water, Simondus, and Indonesian saturation models, consider the influence of clay content on resistivity and are designed for shale or silty mudstone reservoirs with high clay content. Therefore, a suitable saturation model can be selected to calculate water saturation based on the reservoir type in the study area.

[0089] In this invention, water saturation is determined based on the well logging curve using the Simon's saturation model described in the formula; wherein, the Simon's saturation model includes:

[0090]

[0091] In the formula: S wFor water saturation, f; R w R is the resistivity of formation water, in Ω·m; t R represents the resistivity of the original formation, in Ω·m. cl V represents the resistivity of clay, in Ω·m. cl denoted as clay content (f); Φ as effective porosity (f); m as cementation index; n as saturation index; and a and b as coefficients.

[0092] In this embodiment of the invention, for mudstone shale reservoirs or muddy siltstone reservoirs, the Simon saturation model is preferred for calculating water saturation.

[0093] Geological studies indicate that the Gulong Shale in the study area is mainly composed of clayey felsic shale, which has a higher clay mineral content, higher feldspar content, and lower carbonate content compared to other shale oil and gas fields. Thin section analysis shows that the target layer in the study area has a disordered accumulation of clay minerals and felsic minerals, belonging to typical dispersed clay sediments. The preferred model is the saturation model of argillaceous sandstone suitable for dispersed clay, proposed by Simandoux in 1963, which takes into account the conductivity of clay, i.e., formula (3).

[0094] In equation (3), m and n are key parameters for saturation calculation. Taking the Qingshankou Formation in the Gulong area of ​​the study area as an example, based on geological background data, R is selected. w =0.2Ω·m, R cl =2.0Ω·m. Based on rock electrical experiments and digital core simulation, the rock electrical parameters a=1, m=1.38, b=1, n=1.42 were determined and substituted into equation (1) to calculate the water saturation of the shale reservoir in the Gulong area.

[0095] Step S02: Based on the theoretical value of the longitudinal wave time difference of quartz and the organic carbon content, total porosity, clay content and water saturation, establish a longitudinal wave time difference correction model, and use the correction model to obtain the corrected longitudinal wave time difference.

[0096] In an embodiment of the invention, the longitudinal wave time difference correction model includes:

[0097]

[0098] Where: Δt c The P-wave time difference after multi-parameter correction is expressed in μs·ft. -1 Δt is the measured P-wave time difference, us·ft -1 TOC represents total organic carbon content, in g / g; ρ b Lithological density, g / cm³ 3 Φ represents total porosity, v / v; V sh S represents the clay content, v / v; w ν represents water saturation, v / v.

[0099] In this embodiment of the invention, the theoretical value of the longitudinal wave time difference of quartz, 55.5 μs / ft, is used as the benchmark to establish a correction model, namely formula (4). The organic carbon, total porosity, clay content and water saturation that have a significant impact on the longitudinal wave time difference are calculated in step S01. The longitudinal wave time difference is corrected according to the volume model (correction model) to obtain the corrected longitudinal wave time difference.

[0100] Taking the Qingshankou Formation in the Gulong area of ​​the study area as an example, the established calibration model is shown in Equation (4-1).

[0101]

[0102] The corrected P-wave time difference of the target well Qingshankou Formation in the Gulong area of ​​the study region was calculated using equation (4-1), as shown in the attached figure. Figure 2 As shown. Figure 2 The fifth, sixth, seventh, and ninth steps are the total porosity, water saturation, organic carbon content, and lithology results calculated in step S01. The last step is the P-wave transit time after multi-parameter correction according to the method of the present invention and the measured P-wave transit time.

[0103] Step S03: Determine the effective vertical stress of the measured section of the target well based on the logging curve.

[0104] In this invention, the method for determining the effective vertical stress of the measured section of the target well based on the logging curve includes: determining the overlying formation pressure based on the logging curve; and determining the effective vertical stress using the effective vertical stress equation based on the overlying formation pressure.

[0105] In this invention, the method for determining the overlying formation pressure based on the well logging curve includes: determining the overlying formation pressure using formula (5) based on the density curve in the well logging curve;

[0106]

[0107] Where: σ v ρ is the overlying formation pressure, MPa; D is the vertical depth, m; z The density at depth z is kg / m³. 3 .

[0108] In this embodiment of the invention, density curves are used to calculate the overlying formation pressure of wells with measured pore pressure in the study area. For the density-free section of the target well, it needs to be completed. The specific method is as follows: based on the vertical depth, the density trend of the density-free logging section is extrapolated using the three-point extrapolation method to determine the trend line. Combined with the existing measured density curve trend of the target well, the density trend line curve of the density-free section is determined.

[0109] The overburden pressure (overburden stress) of the target well is calculated by integrating the completed density curve, i.e., by formula (5). The calculation results are as follows: Figure 3 As shown.

[0110] Figure 3 The fifth channel is the density curve for well sections without measured density, determined by the three-point extrapolation method. The equivalent mud density in the fourth channel is obtained by converting the overlying formation pressure into an equivalent mud density. Generally, the equivalent mud density of sandstone and mudstone formations is between 2.3 and 2.65. The equivalent mud density can be used to adjust and correct the density trend determined by the three-point extrapolation method, thereby obtaining a more accurate density trend curve.

[0111] In this invention, the effective vertical stress equation includes:

[0112] σ ve =σ v -P p (6);

[0113] Where: σ ve For effective vertical stress, MPa; σ v The overlying formation pressure is expressed in MPa; P p ρ represents the formation pore pressure, in MPa.

[0114] In this embodiment of the invention, based on the overlying formation pressure, the average effective vertical stress of the target well pressure measurement section (pore pressure test section) is calculated using the Terzaghi effective vertical stress equation, i.e., equation (6). That is, the average effective vertical stress is the difference between the overlying formation pressure and the average pore pressure of the measurement section.

[0115] Step S04: Establish a regression model of the corrected longitudinal wave time difference and effective vertical stress, and determine the pore pressure based on the regression model.

[0116] In this embodiment of the invention, the effective vertical stress obtained in step S03 is used to establish a regression model of the longitudinal wave time difference and the effective vertical stress after correction in step S02, as shown in equation (7), which can calculate the effective vertical stress of the formation (section without measured data) in the target well.

[0117] σ ve =a×Δt c +b (7);

[0118] Where: Δt c denoted as Us·ft⁻¹, representing the P-wave time difference after multi-parameter correction; a and b are coefficients.

[0119] Using the obtained effective vertical stress regression model, i.e., equation (7), the pore pressure is calculated by back-calculation based on the Terzaghi effective stress equation.

[0120] Taking the Qingshankou Formation in the Gulong area of ​​the study area as an example, the regression model of the corrected P-wave time difference and effective vertical stress is shown in Equation (7-1).

[0121] σ ve = -0.3927 × Δt c +49.934(7-1);

[0122] Based on the Terzaghi effective stress principle and the effective vertical stress regression model, the pore pressure is calculated. That is, by combining equations (6) and (7), the pore pressure calculation formula of the formation in the study area is obtained, as shown in equation (8).

[0123] P p =σ v -σ ve =σ v +0.3927×Δt c -49.394 (8).

[0124] like Figure 4 The figure shown is a graph depicting the relationship between the P-wave travel time and effective vertical stress of the Qingshankou Formation in the Gulong area of ​​the study region before correction, with a correlation coefficient of 0.36. Figure 5 The diagram shown is a graph depicting the relationship between the corrected longitudinal wave time difference and the effective vertical stress, obtained after performing steps S01 to S04 according to the multi-parameter correction method of the present invention. Figure 5 The results show that the correlation coefficient between the corrected P-wave time difference and the effective vertical stress increased from 0.36 to 0.74, which is a significant improvement compared to before the correction.

[0125] Figure 6 A comparison chart showing the formation pore pressure predicted by the method of this invention for the Qingshankou Formation in the Gulong area of ​​the study area, and the formation pore pressure predicted by the existing Bowers method. Figure 7 This image shows a comparison of predicted and measured formation pore pressures before and after acoustic correction of the target well in the Qingshankou Formation in the Gulong area of ​​the study region. Figure 6 and 7 The results show that the formation pressure of the Qingshankou Formation in the Gulong area ranges from 23.9 to 37.6 MPa, with a pressure coefficient of 1.19 to 1.58, classifying it as an overpressured formation. Currently, the pore pressure of the Qingshankou Formation in Gulong is calculated directly using the measured P-wave transit time and the Bowers model. However, the average absolute error of the calculated result is 2.65 MPa, which is relatively large and cannot meet the needs of reservoir logging evaluation. The method of this invention reduces the average absolute error of the pore pressure calculation from 2.65 MPa using the Bowers method to 1.43 MPa, significantly improving the calculation accuracy.

[0126] Appendix Figure 8To utilize the pore pressure calculation model obtained in this invention, the pressure coefficient of the Q9 section of the vertical well in the Qingshankou Formation of the Gulong area of ​​the study area was calculated. Then, the average pressure coefficient of the Q9 section of each well was read, and a plane pressure coefficient prediction contour map was made.

[0127] Figure 9 The graph shows the relationship between the daily oil production of the Q9 oil-bearing formation in the Qingshankou Formation of the Gulong area of ​​the study region and the pore pressure coefficient predicted by the method of this invention. A higher pore pressure coefficient corresponds to a higher daily oil production, and the two are positively correlated. Therefore, by obtaining accurate pore pressure data, accurate prediction and evaluation of oil reservoir production can be achieved.

[0128] The method of this invention was experimentally applied in the Qingshankou Formation of the Gulong area, achieving excellent results. Combined with... Figures 1-9 The effects of this invention can be summarized in at least the following points: 1. The pore pressure predicted using the multi-parameter (organic carbon content, total porosity, clay content, water saturation) acoustic transit time correction method of this invention has an average absolute error reduced from 2.65 MPa (Bowers method) to 1.43 MPa (with appendix). Figure 6 Appendix Figure 7 1. The prediction accuracy has been greatly improved; 2. The pore pressure of 38 wells in the Gulong area was calculated using the multi-parameter sonic transit time correction method, and the planar distribution of the pressure coefficient of the Q9 oil layer group was predicted. This can provide technical guidance for the prediction of favorable shale oil areas in the Gulong area and the well location deployment of the Q9 oil layer group (see appendix). Figure 8 3. Based on the predicted pressure coefficient of the Q9 oil reservoir group in the horizontal wells, the correlation between the horizontal well productivity and the pressure coefficient was analyzed. It was found that the higher the pressure coefficient in the Gulong area, the higher the horizontal well productivity. Therefore, the next step in well placement should prioritize wells located at positions with higher pressure coefficients. (See attached diagram) Figure 9 ).

[0129] It is understood that the various method embodiments mentioned above in this invention can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this invention will not elaborate further.

[0130] The execution entity for the shale reservoir pore pressure prediction method can be a shale reservoir pore pressure prediction device. For example, the shale reservoir pore pressure prediction method can be executed by a terminal device, server, or other processing device. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. In some possible implementations, the shale reservoir pore pressure prediction method can be implemented by a processor calling computer-readable instructions stored in memory.

[0131] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0132] This invention also proposes a shale reservoir pore pressure prediction device, comprising: an acquisition unit for acquiring well logging curves of the study area and determining organic carbon content, total porosity, clay content, and water saturation based on the well logging curves; a correction unit for establishing a P-wave transit time correction model based on the theoretical value of P-wave transit time of quartz and the organic carbon content, total porosity, clay content, and water saturation, and obtaining the corrected P-wave transit time using the correction model; an effective vertical stress determination unit for determining the effective vertical stress of the measured section of the target well based on the well logging curves; and a pore pressure determination unit for establishing a regression model between the corrected P-wave transit time and the effective vertical stress, and determining the pore pressure based on the regression model.

[0133] In some embodiments, the functions or modules and units included in the apparatus provided by the present invention can be used to execute the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0134] This invention also proposes a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the aforementioned shale reservoir pore pressure prediction method. The computer-readable storage medium can be a non-volatile computer-readable storage medium.

[0135] This invention also proposes an electronic device, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the aforementioned shale reservoir pore pressure prediction method. The electronic device may be provided as a terminal, a server, or other form of device.

[0136] This invention improves the correlation between P-wave transit time and effective vertical stress by using a volumetric model to correct for organic carbon, porosity, lithology, and fluid composition. It also improves the accuracy of pore pressure prediction by establishing a pore pressure prediction model based on the effective stress principle.

[0137] The pore pressure calculation method of this invention can be extended to other unconventional oil and gas reservoirs, demonstrating broad applicability. This invention provides technical support for improving rock mechanics calculations, sweet spot prediction, and well location deployment in shale reservoirs, and has significant economic and social benefits.

[0138] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting pore pressure in shale reservoirs, characterized in that, include: Obtain well logging curves for the study area, and determine organic carbon content, total porosity, clay content, and water saturation based on the well logging curves. Based on the theoretical value of P-wave transit time of quartz and the organic carbon content, total porosity, clay content and water saturation, a P-wave transit time correction model is established, and the corrected P-wave transit time is obtained using the correction model. Based on the logging curves, determine the effective vertical stress of the measured section of the target well; A regression model is established between the corrected longitudinal wave time difference and the effective vertical stress. Based on the regression model, the pore pressure is determined.

2. The method for predicting pore pressure in shale reservoirs according to claim 1, characterized in that, The method for determining the organic carbon content based on the well logging curve includes: Based on the sonic transit time and deep lateral resistivity curve in the well logging curve, the organic carbon content is determined using formula (1). TOC=a×DT+b×log(R LLD )-c (1); In the formula: TOC is the organic carbon content, %; DT is the acoustic transit time, us·ft -1 ;R LLD ρ is the deep lateral resistivity, in Ω·m; a, b, and c are coefficients.

3. The method for predicting pore pressure in shale reservoirs according to claim 1, characterized in that, The method for determining total porosity based on the well logging curve includes: Based on the compensated neutron curve, lithological density curve and sonic transit time curve in the well logging curve, the total porosity is determined using formula (2). F 总 =a×DT+b×CNL-c×DEN+d (2); Where: Φ 总 Total porosity, %; DT is acoustic transit time, µs·ft. -1 CNL represents compensated neutrons, f; DEN represents lithological density, g / cm³. 3 a, b, c, and d are coefficients.

4. The method for predicting pore pressure in shale reservoirs according to claim 1, characterized in that, The method for determining clay content based on the well logging curve includes: Acquire elemental logging data and XRD whole-rock analysis experimental data for the study area; Based on elemental logging data and XRD whole-rock analysis experimental data, the conversion relationship between mineral content and elemental content was determined, and the clay content was determined based on the conversion relationship. If elemental logging data cannot be obtained, a clay content regression calculation model is established based on the XRD whole-rock analysis experimental data and conventional logging curves, and the clay content is determined according to the model.

5. The method for predicting pore pressure in shale reservoirs according to claim 1, characterized in that, The method for determining water saturation based on the well logging curve includes: Based on the well logging curves, the water saturation is determined using a saturation model, wherein the saturation model is: Archie saturation model, or double water saturation model, or Simondu saturation model, or Indonesian saturation model.

6. The method for predicting pore pressure in shale reservoirs according to claim 5, characterized in that: Based on the well logging curves, the water saturation is determined using the Simon saturation model described in the formula. The Simon saturation model includes: In the formula: S w For water saturation, f; R w R is the resistivity of formation water, in Ω·m; t R represents the resistivity of the original formation, in Ω·m. cl V represents the resistivity of clay, in Ω·m. cl denoted as clay content (f); Φ as effective porosity (f); m as cementation index; n as saturation index; and a and b as coefficients.

7. The method for predicting pore pressure in shale reservoirs according to claim 1, characterized in that, The P-wave time difference correction model includes: Where: Δt c The P-wave time difference after multi-parameter correction is expressed in μs·ft. -1 Δt is the measured P-wave time difference, us·ft -1 TOC represents total organic carbon content, in g / g; ρ b Lithological density, g / cm³ 3 Φ represents total porosity, v / v; V sh S represents the clay content, v / v; w ν represents water saturation, v / v.

8. The method for predicting pore pressure in shale reservoirs according to any one of claims 1-7, characterized in that, The method for determining the effective vertical stress of the measured section of the target well based on the logging curve includes: The overlying formation pressure is determined based on the well logging curves. Based on the overlying stratum pressure, the effective vertical stress is determined using the effective vertical stress equation.

9. The method for predicting pore pressure in shale reservoirs according to claim 8, characterized in that, The method for determining the overlying formation pressure based on the well logging curve includes: Based on the density curve in the well logging curve, the overlying formation pressure is determined using formula (5); Where: σ v ρ is the overlying formation pressure, MPa; D is the vertical depth, m; z The density at depth z is kg / m³. 3 .

10. The method for predicting pore pressure in shale reservoirs according to claim 8, characterized in that, The effective vertical stress equation includes: s ve =s v -P p (6); Where: σ ve For effective vertical stress, MPa; σ v The overlying formation pressure is expressed in MPa; P p ρ represents the formation pore pressure, in MPa.

11. A device for predicting pore pressure in shale reservoirs, characterized in that, include: The acquisition unit is used to acquire well logging curves in the study area and determine organic carbon content, total porosity, clay content, and water saturation based on the well logging curves. The correction unit is used to establish a longitudinal wave time difference correction model based on the theoretical value of longitudinal wave time difference of quartz, as well as the organic carbon content, total porosity, clay content and water saturation, and to obtain the corrected longitudinal wave time difference using the correction model. An effective vertical stress determination unit is used to determine the effective vertical stress of the measured section of the target well based on the logging curve. The pore pressure determination unit is used to establish a regression model between the corrected longitudinal wave time difference and the effective vertical stress, and to determine the pore pressure based on the regression model.

12. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 10.

13. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 10.

Images