Clastic rock reservoir porosity prediction method, and apparatus

Abstract

The present application relates to the technical field of oil and gas exploration. Disclosed are a clastic rock reservoir porosity prediction method, and an apparatus. The method comprises: acquiring temperature evolution data and pressure evolution data of a layer to be predicted; according to the temperature evolution data, establishing a first function model of the temperature of said layer evolving over the burial time; according to the pressure evolution data, establishing a second function model of the pressure of said layer evolving over the burial time; according to the first function model, determining a time-temperature index; according to the second function model, determining a time-pressure index, the time-pressure index reflecting the rule of fluid overpressure changing over time; inputting the time-temperature index, the time-pressure index and an original porosity before burial of said layer into a pre-established prediction model, so as to output a current porosity of said layer, the prediction model reflecting the association relationship between the time-temperature index, the time-pressure index, the original porosity and the current porosity. The present application can improve the precision of porosity prediction for clastic rock reservoirs.

Description

Clastic rock reservoir porosity prediction method and device

[0001] Related applications

[0002] This application claims priority to Chinese patent application No. 202311330397.6 filed on October 13, 2023, and cites the contents disclosed in the above patent application as part of this application. Technical Field

[0003] The present application relates to the technical field of oil and gas exploration, and in particular to a method and device for predicting the porosity of clastic rock reservoirs. Background Art

[0004] This section is intended to provide a background or context to the embodiments of the present application that are recited in the claims. No admission is made that the description herein is prior art by virtue of its inclusion in this section.

[0005] Clastic reservoirs are usually composed of many clastic particles, and pores develop in the unfilled parts between the clastic particles. The porosity of a clastic reservoir is the ratio of the total volume of pores in the clastic reservoir to the total volume of the clastic reservoir.

[0006] There are two types of porosity prediction methods commonly used in the existing technology for deep clastic reservoirs. The first type is to establish a porosity-depth trend line and function based on the current porosity and current depth data of existing samples in a certain study area, thereby predicting the reservoir porosity at a certain depth. This is because the depth of the reservoir sample is the easiest to obtain and can indirectly reflect the strength of the compaction effect (i.e., the pore preservation effect). However, the disadvantage of this method is that the burial mode, temperature, and pressure environment experienced by clastic reservoirs of similar depths today may be extremely different, resulting in significant differences in the amount of compaction pore reduction (i.e., the amount of pore preservation), resulting in large differences in current porosity. The porosity-depth function is usually poorly correlated, and the error in predicting porosity using depth is also large. The second approach involves first simulating the reservoir's thermal evolution history based on the temperature field and geological stratification data from existing wells in the study area, calculating reservoir thermal maturity data, and establishing a reservoir thermal maturity-porosity trend line and function. Then, based on the temperature field and geological stratification data of the target layer, the thermal maturity value of the target reservoir at a specific depth is calculated, and the porosity is predicted using the established function. The disadvantage of this method is that it only considers the differences in compaction porosity reduction caused by different reservoir thermal evolution histories, which is a relatively single factor, resulting in low porosity prediction accuracy for deep clastic reservoirs.

[0007] Summary of the Invention

[0008] The present invention provides a porosity prediction method for clastic reservoirs, which is used to improve the porosity prediction accuracy of clastic reservoirs. The method includes:

[0009] Obtain geological data of the layer to be predicted in the clastic reservoir; the geological data includes temperature evolution data, pressure evolution data, and original porosity before burial;

[0010] According to the temperature evolution data of the layer to be predicted, a first function model of the evolution of the temperature of the layer to be predicted with the burial time of the layer to be predicted is established;

[0011] According to the pressure evolution data of the layer to be predicted, a second function model of the evolution of the pressure of the layer to be predicted with the burial time of the layer to be predicted is established;

[0012] Determine a time-temperature index (TTI) according to the first function model;

[0013] Based on the second function model, the Time-Pressure Index (TPI) is determined. The TPI reflects the temporal variation of the fluid overpressure value (overload pressure generated by the fluid in the rock) in the layer to be predicted.

[0014] The TTI, time-pressure index, and original porosity before burial of the layer to be predicted are input into a pre-established prediction model, and the current porosity of the layer to be predicted is output. The prediction model reflects the correlation between the TTI, time-pressure index, original porosity before burial and current porosity of the clastic reservoir.

[0015] In one embodiment, the second function model includes a function model of the evolution of the fluid pressure of the layer to be predicted with the burial time, and a function model of the evolution of the hydrostatic pressure of the layer to be predicted with the burial time;

[0016] According to the second function model, the time pressure index is determined, including:

[0017] The time pressure index is determined according to the function model of the fluid pressure of the layer to be predicted evolving with the burial time and the function model of the hydrostatic pressure of the layer to be predicted evolving with the burial time.

[0018] In one embodiment, the function model of the evolution of the fluid pressure of the layer to be predicted with the burial time includes: a function curve of the evolution of the fluid pressure of the layer to be predicted with the burial time;

[0019] The function model of the hydrostatic pressure of the layer to be predicted evolving with burial time includes: a function curve of the hydrostatic pressure of the layer to be predicted evolving with burial time;

[0020] According to the function model of the fluid pressure of the layer to be predicted evolving with the burial time and the function model of the hydrostatic pressure of the layer to be predicted evolving with the burial time, the time pressure index is determined, including:

[0021] According to the function curve of the fluid pressure of the layer to be predicted changing with the burial time and the function curve of the hydrostatic pressure of the layer to be predicted changing with the burial time, integral calculation is performed along the time direction to obtain the integral value corresponding to the fluid pressure and the integral value corresponding to the hydrostatic pressure;

[0022] The time pressure index is obtained according to the integral value corresponding to the fluid pressure and the integral value corresponding to the hydrostatic pressure.

[0023] In one embodiment, the prediction model is obtained in advance as follows:

[0024] Pre-establish the TTI, time pressure index, and fitting formulas of the original porosity before burial and the current porosity of multiple clastic reservoirs;

[0025] The fitting formula was verified and calculated using the historical TTI, time pressure index, original porosity before burial and actual porosity of the clastic reservoir to obtain multiple porosity calculation values.

[0026] The fitting formula corresponding to the minimum difference between the calculated porosity and the actual porosity is used as the prediction model.

[0027] In one embodiment, the prediction model is expressed as follows:

[0028] Where, Φ 预测 is the current porosity of the layer to be predicted, Φ0 is the original porosity of the layer to be predicted before burial, TTI is the time-temperature index value, TPI is the time-pressure index value, k, m, n are fitting coefficients, and e is the natural logarithm.

[0029] In one embodiment, the temperature evolution data includes one or any combination of the following:

[0030] Current formation temperature data, historical geothermal gradient data, historical heat flow value data, and thermal conductivity data of different formations.

[0031] In one embodiment, the pressure evolution data includes one or any combination of the following:

[0032] Current formation pressure test data, historical fluid inclusion pressure data, and current layer pressure data interpreted by geophysical methods.

[0033] In one embodiment, determining the TTI according to the first function model includes:

[0034] The TTI was determined using the Lopatin method according to the first function model.

[0035] The present application also provides a clastic reservoir porosity prediction device for improving the porosity prediction accuracy of clastic reservoirs. The device includes:

[0036] The data acquisition module is used to obtain geological data of the layer to be predicted in the clastic reservoir; the geological data includes temperature evolution data, pressure evolution data, and original porosity before burial;

[0037] A thermal evolution history establishment module is used to establish a first function model of the evolution of the temperature of the layer to be predicted with the burial time based on the temperature evolution data of the layer to be predicted;

[0038] A pressure evolution history establishment module is used to establish a second function model of the evolution of the pressure of the layer to be predicted with the burial time based on the pressure evolution data of the layer to be predicted;

[0039] A TTI calculation module, configured to determine a TTI according to a first function model;

[0040] A time pressure index calculation module is used to determine the time pressure index based on the second function model; the time pressure index reflects the law of change of the fluid overpressure value (overload pressure generated by the fluid in the rock) in the layer to be predicted over time;

[0041] The result output module is used to input the TTI, time-pressure index, and original porosity before burial of the layer to be predicted into a pre-established prediction model, and output the current porosity of the layer to be predicted. The prediction model reflects the correlation between the TTI, time-pressure index, original porosity before burial and current porosity of the clastic reservoir.

[0042] An embodiment of the present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned clastic reservoir porosity prediction method is implemented.

[0043] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned clastic reservoir porosity prediction method is implemented.

[0044] An embodiment of the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned clastic reservoir porosity prediction method.

[0045] In an embodiment of the present application, geological data of the layer to be predicted in the clastic rock reservoir are obtained, a first function model of the evolution of the temperature of the layer to be predicted with the burial time and a second function model of the evolution of the pressure of the layer to be predicted with the burial time are established, and TTI and time-pressure index are determined respectively. The time-pressure index reflects the law of change of the fluid overpressure value in the layer to be predicted with the burial time. Finally, the porosity of the layer to be predicted is determined using TTI, time-pressure index and the original porosity of the layer to be predicted before burial. The embodiment of the present application focuses on the porosity of clastic reservoirs. On the one hand, the environmental temperature factor is taken into account, and the effect of environmental temperature on porosity is characterized by TTI. The higher the environmental temperature of the reservoir, the stronger the compaction effect, which ultimately affects the reservoir porosity. On the other hand, the pressure factor is taken into account, and a time-pressure index is proposed to characterize the effect of pressure on porosity. The time-pressure index is determined according to a second function model of the evolution of the pressure of the layer to be predicted with the burial time, reflecting the law of the change of fluid overpressure with the burial time. The fluid overpressure will inevitably affect the stress between the reservoir clastic particles, thereby affecting the porosity. Finally, the TTI, time-pressure index, original porosity before burial and pre-established prediction model of the layer to be predicted are used to output the current porosity of the layer to be predicted. The embodiment of the present application predicts porosity by the underground temperature environment of the layer to be predicted and the pressure on the clastic particles. That is, on the basis of considering thermal evolution, the influence of pressure evolution on porosity is further considered, thereby improving the prediction accuracy of the porosity of clastic reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0047] FIG1 is a schematic flow chart of a clastic reservoir porosity prediction method according to an embodiment of the present application;

[0048] FIG2 is a schematic diagram of a first function model of the evolution of the temperature of the layer to be predicted with the burial time in an embodiment of the present application;

[0049] FIG3 is a schematic diagram of a second function model of the evolution of the pressure of the layer to be predicted with the burial time in an embodiment of the present application;

[0050] FIG4 is a schematic diagram of the evolution history of excess pressure in a typical well reservoir in an embodiment of the present application;

[0051] FIG5 is a specific embodiment of the porosity prediction method of clastic reservoirs in the embodiment of the present application;

[0052] FIG6 is a specific example of the evolution of the temperature of the layer to be predicted with the burial time in the embodiment of the present application;

[0053] FIG7 is a schematic diagram of a clastic reservoir porosity prediction device in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the embodiments of the present application are further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but are not intended to limit the present application.

[0055] First, the technical terms involved in the embodiments of the present application are explained.

[0056] Fluid pressure, also known as formation pressure, refers to the pressure of fluid in the pores of a formation. The standard unit of formation pressure is Pa. MPa is commonly used in geological research.

[0057] Hydrostatic Pressure: refers to the formation pressure when the formation fluid is connected to the surface water system.

[0058] Lopatin's Method: Modern petroleum genesis research has demonstrated that temperature and time are the primary influencing factors in the hydrocarbon evolution of organic matter. Specifically, temperature and hydrocarbon generation are exponentially related, while time and hydrocarbon generation are positively linear. Based on this understanding, Lopatin proposed a simple method between 1969 and 1971 to quantitatively calculate organic matter maturity based on time and temperature, known as the Time-Temperature Index (TTI). This method, supplemented and developed by Whipple (1980), has been widely used in oil and gas exploration.

[0059] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0060] The applicant discovered that existing techniques for predicting the porosity of clastic reservoirs have low accuracy. Therefore, the applicant proposed a porosity prediction method for clastic reservoirs. Based on the predicted thermal and pressure evolution histories of the layer to be predicted, the applicant calculated the time-temperature index and time-pressure index of the layer to be predicted. Furthermore, a porosity prediction model for deep and ultra-deep reservoirs was established. The model quantitatively evaluated the impact of different thermal and pressure evolution histories on the amount of compaction porosity reduction, enabling accurate prediction of the porosity of clastic reservoirs at a specific depth.

[0061] FIG1 is a flow chart of a method for predicting porosity of a clastic reservoir in an embodiment of the present application. As shown in FIG1 , the method includes:

[0062] Step 101: Acquire geological data of a layer to be predicted in a clastic reservoir; the geological data includes temperature evolution data, pressure evolution data, and original porosity before burial;

[0063] Step 102: establishing a first function model of the evolution of the temperature of the layer to be predicted with the burial time based on the temperature evolution data of the layer to be predicted;

[0064] Step 103: establishing a second function model of the evolution of the pressure of the layer to be predicted with burial time based on the pressure evolution data of the layer to be predicted;

[0065] Step 104: Determine TTI according to the first function model;

[0066] Step 105: Determine a time pressure index based on the second function model; the time pressure index reflects the law of change of the fluid overpressure value in the layer to be predicted over time;

[0067] Step 106: The TTI, time-pressure index, and original porosity before burial of the layer to be predicted are input into a pre-established prediction model, and the current porosity of the layer to be predicted is output. The prediction model reflects the correlation between the TTI, time-pressure index, original porosity before burial, and current porosity of the clastic reservoir.

[0068] As can be seen from the process shown in Figure 1, in the embodiment of the present application, geological data of the layer to be predicted in the clastic rock reservoir are obtained, a first function model of the evolution of the temperature of the layer to be predicted with the burial time and a second function model of the evolution of the pressure of the layer to be predicted with the burial time are established, and the TTI and time-pressure index are determined respectively. The time-pressure index reflects the law of change of the fluid overpressure value in the layer to be predicted with the burial time. Finally, the porosity of the layer to be predicted before burial is determined using the original porosity, TTI and time-pressure index of the layer to be predicted. The embodiment of the present application focuses on the porosity of clastic reservoirs. On the one hand, the environmental temperature factor is taken into account, and the effect of environmental temperature on porosity is characterized by TTI. The higher the environmental temperature of the reservoir, the stronger the compaction effect, which ultimately affects the reservoir porosity. On the other hand, the pressure factor is taken into account, and a time-pressure index is proposed to characterize the effect of pressure on porosity. The time-pressure index is determined according to a second function model of the evolution of the pressure of the layer to be predicted with the burial time, reflecting the law of fluid overpressure change with time. Fluid overpressure will inevitably affect the stress between the reservoir debris particles, thereby affecting the porosity. Finally, the original porosity of the layer to be predicted before burial, TTI, time-pressure index and a pre-established prediction model are used to output the current porosity of the layer to be predicted. The embodiment of the present application predicts porosity by the underground temperature environment of the layer to be predicted and the pressure on the debris particles. That is, on the basis of considering thermal evolution, the influence of pressure evolution on porosity is further considered, thereby improving the prediction accuracy of the porosity of the clastic reservoir.

[0069] The porosity prediction method for clastic reservoirs in the embodiments of the present application is explained in detail below.

[0070] In the embodiments of the present application, it is taken into account that clastic reservoirs are usually composed of many clastic particles, and pores develop in the unfilled parts between the clastic particles. The porosity of the clastic reservoir is the ratio of the total volume of pores in the clastic reservoir to the total volume of the reservoir. During the burial process, the clastic reservoir is affected by the compaction caused by the overburden load or tectonic stress. The arrangement of particles will gradually become tighter, the pore volume will shrink, and the porosity will gradually decrease. The main controlling factors for the reduction of porosity are the underground temperature environment of the reservoir and the stress exerted on the clastic particles of the reservoir. On the one hand, the depth of the reservoir will gradually increase during the underground burial process. The higher the depth, the higher the underground temperature. The higher the ambient temperature of the reservoir, the stronger the compaction effect, which affects the final porosity of the reservoir. On the other hand, reservoir pores are typically filled with fluids, such as groundwater or oil and gas. Under a constant total confining pressure, higher reservoir fluid pressure reduces the stress between particles. Therefore, the reservoir fluid pressure environment also affects the intensity of compaction, and thus the final porosity. The total confining pressure is equal to the sum of the reservoir fluid pressure and the stress between particles. Therefore, the effects of temperature and pressure on reservoir porosity are simultaneously considered in the examples of this application.

[0071] In specific implementation, in step 101, geological data of the layer to be predicted in the clastic reservoir is obtained, and the geological data includes temperature evolution data, pressure evolution data, and original porosity before burial.

[0072] For example, collect and organize relevant geological data of the layer to be predicted, including but not limited to burial history background data, temperature evolution data, and pressure evolution data.

[0073] The burial history background data includes one of the following or any combination: geological stratification and thickness data of each stratum of the target well point, tectonic evolution history data of the target well point, stratum erosion volume, tectonic uplift volume data, lithology data of each stratum of the target well point, total organic carbon (TOC) content data, etc.

[0074] The original porosity of the layer to be predicted before burial can be determined based on the lithologic data of the layer to be predicted at the target well point, such as the original grain size, sorting, rigid particle content and other information of the reservoir.

[0075] Temperature evolution data include one or any combination of the following: current formation temperature data of the target well point or the same working area, adjacent wells, (paleo)historical geothermal gradient data, (paleo)historical heat flow value data, thermal conductivity data of different formations, etc.

[0076] Pressure evolution data include one or any combination of the following: current formation pressure test data of the same study area or adjacent wells, (paleo)historical pressure data of fluid inclusions, current layered pressure data interpreted by geophysical methods, etc.

[0077] Afterwards, in steps 102 and 103 , a first function model for the evolution of the temperature of the layer to be predicted with the burial time and a second function model for the evolution of the pressure of the layer to be predicted with the burial time are established respectively.

[0078] During implementation, the reservoir burial history is restored and reconstructed based on the acquired data. The reservoir burial history is a function of the evolution of the depth, temperature, and formation pressure of the layer to be predicted over time. See Figures 2 and 3. Figure 2 is a schematic diagram of a first functional model for the evolution of the temperature of the layer to be predicted over time, and Figure 3 is a schematic diagram of a second functional model for the evolution of the pressure of the layer to be predicted over time, in accordance with an embodiment of the present application. Restoration and reconstruction of the burial history can be performed manually or using existing geological software.

[0079] In step 104, a TTI is determined according to the first function model.

[0080] For example, referring to the first function model in FIG2 , TTI is determined. In one embodiment, the Lopatin method (Lopatin, 1971) is used to determine TTI based on the first function model. The specific calculation formula is as follows:

[0081] In the formula, r is the temperature factor, which is generally 2. The exponent n is related to the temperature range of the layer to be predicted. It takes 100-110℃ as the base and takes 0. The high temperature range increases by 1 and the low temperature range decreases by 1. For example, 90-100℃ takes -1, 100-110℃ takes 0, 110-110℃ takes 1, 110-120℃ takes 2, and so on. i Represents the duration of the layer to be predicted in this temperature range, n min is the initial moment in the temperature range of the layer to be predicted, n max is the end time of the temperature range of the layer to be predicted. i ×r n By accumulating, the total TTI value of the layer to be predicted can be obtained.

[0082] In step 105, a time pressure index is determined according to the second function model; the time pressure index reflects the law of change of the fluid overpressure value in the layer to be predicted with time.

[0083] Considering that the reservoir pores are usually filled with fluids, such as groundwater or oil and gas, under the condition of a certain total confining pressure, if the fluid pressure in the reservoir is higher (i.e., fluid overpressure), the stress between the particles will be reduced accordingly. Therefore, the fluid pressure environment of the reservoir will also affect the strength of the compaction effect, and thus affect the size of the final porosity. Therefore, in order to quantitatively characterize the effect of fluid overpressure on porosity, the concept of time pressure index is proposed in the embodiment of the present application. The purpose of using this time pressure index to analyze the effect of fluid overpressure on porosity is that the size and formation time of fluid overpressure will affect the final preservation effect of the primary pores of clastic rock. If the fluid overpressure is formed very late, the clastic rock reservoir pores have been compacted to death. Since compaction is an irreversible physical process, even if a strong fluid overpressure environment appears later, the porosity will not increase. Therefore, the embodiment of the present application establishes this time pressure index that combines the overpressure intensity and the overpressure formation time to analyze the effect of overpressure on porosity change.

[0084] In specific implementation, the second function model of the evolution of the pressure of the layer to be predicted with the burial time is used to determine the law of the fluid overpressure changing with time, and the time pressure index is calculated by integral calculation.

[0085] In order to improve the accuracy of porosity prediction of clastic reservoirs, in one embodiment, the pressures considered in this solution include fluid pressure and hydrostatic pressure, and the second function model includes a function model of the evolution of the fluid pressure of the layer to be predicted with burial time and a function model of the evolution of the hydrostatic pressure of the layer to be predicted with burial time;

[0086] Determining the time pressure index according to the second function model may include:

[0087] The time pressure index is determined according to the function model of the fluid pressure of the layer to be predicted evolving with the burial time and the function model of the hydrostatic pressure of the layer to be predicted evolving with the burial time.

[0088] In this example, functional models of the evolution of fluid pressure and hydrostatic pressure with time are established respectively, and the fluid overpressure is determined using the fluid pressure and hydrostatic pressure. Thus, the time pressure index is determined based on the functional models of the evolution of the fluid pressure of the layer to be predicted with burial time and the evolution of the hydrostatic pressure of the layer to be predicted with burial time.

[0089] In order to further improve the accuracy of clastic reservoir porosity prediction, in one embodiment, the function model of the evolution of the fluid pressure of the layer to be predicted with the burial time includes: a function curve of the change of the fluid pressure of the layer to be predicted with the burial time;

[0090] The function model of the hydrostatic pressure of the layer to be predicted evolving with burial time includes: a function curve of the hydrostatic pressure of the layer to be predicted evolving with burial time;

[0091] According to the function model of the fluid pressure of the layer to be predicted evolving with the burial time and the function model of the hydrostatic pressure of the layer to be predicted evolving with the burial time, the time pressure index is determined, including:

[0092] According to the function curve of the fluid pressure of the layer to be predicted changing with the burial time and the function curve of the hydrostatic pressure of the layer to be predicted changing with the burial time, integral calculation is performed along the time direction to obtain the integral value corresponding to the fluid pressure and the integral value corresponding to the hydrostatic pressure;

[0093] The time pressure index is obtained according to the integral value corresponding to the fluid pressure and the integral value corresponding to the hydrostatic pressure.

[0094] In specific implementation, referring to FIG2 , the time-pressure index TPI (Time-Pressure Index, TPI) can be calculated according to the following formula:

[0095] Where ΔP is the reservoir's excess pressure (which can reach fluid overpressure), which is the fluid pressure minus the hydrostatic pressure; ΔT is the duration that the reservoir under investigation experiences this excess pressure. Obviously, if the reservoir remains under hydrostatic pressure, ΔP is always zero, and TPI is also zero, indicating that the reservoir is unaffected by fluid overpressure.

[0096] Among them, the fluid pressure can be recovered by basin simulation software. Hydrostatic pressure = formation water density (ρ) × gravitational acceleration (g) × formation depth (h). The calculation of TPI is the integration of ΔP over time, or it can be understood as the difference between the two curves in Figure 4 after taking the definite integral. Since the formation pressure calculated by basin simulation software is usually discrete data points, T min and T max The ΔT calculation and the final TPI accumulation calculation can also be performed according to the time assignment corresponding to each calculated pressure data point.

[0097] See FIG4 , which is a schematic diagram of the evolution history of excess pressure in a typical well reservoir in an embodiment of the present application, illustrating how excess pressure changes with burial time.

[0098] Finally, the TTI, time-pressure index, and original porosity before burial of the layer to be predicted are input into the pre-established prediction model, and the current porosity of the layer to be predicted is output. The prediction model reflects the correlation between the TTI, time-pressure index, original porosity before burial and current porosity of the clastic reservoir.

[0099] In the embodiment of the present application, a prediction model is pre-established. The prediction model can be obtained in advance as follows:

[0100] Pre-establish the TTI, time pressure index, and fitting formulas of the original porosity before burial and the current porosity of multiple clastic reservoirs;

[0101] The fitting formula was verified and calculated using the historical TTI, time pressure index, original porosity before burial and actual porosity of the clastic reservoir to obtain multiple porosity calculation values.

[0102] The fitting formula corresponding to the minimum difference between the calculated porosity and the actual porosity is used as the prediction model.

[0103] During implementation, a large number of historical wellbore TTI and TPI data from actual clastic formations, as well as original and actual porosity data from pre-burial periods, were used for fitting and verification. After comparing the performance of multiple fitting formulas (e.g., linear, polynomial, and least-squares), the best fitting formula (that is, the one with the smallest difference between the calculated porosity and the actual porosity) was selected as the prediction model. Actual porosity can be calculated using other more accurate existing methods and data.

[0104] In one embodiment, the prediction model is expressed as follows:

[0105] It can also be obtained through mathematical transformation:

[0106] Where, Φ 预测 is the current porosity of the layer to be predicted, Φ0 is the original porosity of the layer to be predicted before burial, TTI is the time-temperature index value, TPI is the time-pressure index value, e is the natural logarithm, k, m, and n are fitting coefficients obtained by the above fitting process.

[0107] In one embodiment, k=-2, m=1, and n=1.

[0108] Figure 5 is a specific example of the evolution of the temperature of the layer to be predicted with the burial time in the embodiment of the present application. As shown in Figure 5, the porosity prediction method of the clastic reservoir in the embodiment of the present application is summarized. The reservoir burial history is restored and reconstructed, and the reservoir thermal evolution history and reservoir pressure evolution history are respectively calculated. The TTI value and TPI value are calculated respectively. Finally, the porosity quantitative prediction is achieved according to the formula in Figure 5. It can be seen that in Figure 5, Φ T&P is the current porosity of the layer to be predicted, k = -2, m = 0, n = 0. It should be noted that those skilled in the art may obtain different fitting coefficients according to the methods in the embodiments of the present application, for different tools, and using different data.

[0109] Figure 6 is a specific example of the evolution of the temperature of the layer to be predicted with the burial time in the embodiment of the present application. The horizontal axis in Figure 6 is the fitting coefficient in the prediction model obtained by combining the reservoir temperature history and pressure history, and the vertical axis is the porosity. The dotted line is the predicted porosity using the method of the embodiment of the present application, and the circular mark is the measured porosity. It can be seen that the porosity predicted in the implementation of the present application is basically consistent with the measured porosity.

[0110] The embodiment of the present application proposes a porosity prediction method for clastic rock reservoirs for ultra-deep clastic rocks. The basin simulation method is used, combined with the thermal evolution history and pressure evolution history of the reservoir, to establish a characteristic function for porosity prediction. No logging or core data is required, and the method is convenient and low-cost.

[0111] In the experimental work area, the method of the embodiment of the present application is used to predict the porosity of clastic rocks before drilling. Compared with the traditional method of predicting porosity based on depth history or relying solely on temperature history, the prediction accuracy can be improved by up to 50%, greatly improving the efficiency and quality of zone selection and reducing exploration risks and exploration investment costs.

[0112] The present application also provides a device for predicting the porosity of a clastic reservoir, as described in the following embodiments. Since the principles of the device are similar to those of the clastic reservoir porosity prediction method, the implementation of the device can refer to the implementation of the clastic reservoir porosity prediction method, and the repeated parts will not be repeated.

[0113] FIG7 is a schematic diagram of a clastic reservoir porosity prediction device according to an embodiment of the present application. As shown in FIG7 , the device includes:

[0114] The data acquisition module 701 is used to acquire geological data of the layer to be predicted in the clastic reservoir; the geological data includes temperature evolution data, pressure evolution data, and original porosity before burial;

[0115] The thermal evolution history establishment module 702 is used to establish a first function model of the evolution of the temperature of the layer to be predicted with the burial time based on the temperature evolution data of the layer to be predicted;

[0116] The pressure evolution history establishment module 703 is used to establish a second function model of the evolution of the pressure of the layer to be predicted with the burial time based on the pressure evolution data of the layer to be predicted;

[0117] A TTI calculation module 704 is configured to determine a TTI based on a first function model;

[0118] The time pressure index calculation module 705 is used to determine the time pressure index according to the second function model; the time pressure index reflects the law of the change of the fluid overpressure value in the layer to be predicted over time;

[0119] The result output module 706 is used to input the TTI, time-pressure index, and original porosity before burial of the layer to be predicted into a pre-established prediction model, and output the current porosity of the layer to be predicted. The prediction model reflects the correlation between the TTI, time-pressure index, original porosity before burial and current porosity of the clastic reservoir.

[0120] In one embodiment, the second function model includes a function model of the evolution of the fluid pressure of the layer to be predicted with the burial time, and a function model of the evolution of the hydrostatic pressure of the layer to be predicted with the burial time;

[0121] The time pressure index calculation module 705 is specifically used to:

[0122] The time pressure index is determined according to the function model of the fluid pressure of the layer to be predicted evolving with the burial time and the function model of the hydrostatic pressure of the layer to be predicted evolving with the burial time.

[0123] In one embodiment,

[0124] The function model of the evolution of the fluid pressure of the layer to be predicted with the burial time includes: a function curve of the fluid pressure of the layer to be predicted with the burial time;

[0125] The function model of the hydrostatic pressure of the layer to be predicted evolving with burial time includes: a function curve of the hydrostatic pressure of the layer to be predicted evolving with burial time;

[0126] The time pressure index calculation module 705 is specifically used to:

[0127] According to the function curve of the fluid pressure of the layer to be predicted changing with the burial time and the function curve of the hydrostatic pressure of the layer to be predicted changing with the burial time, integral calculation is performed along the time direction to obtain the integral value corresponding to the fluid pressure and the integral value corresponding to the hydrostatic pressure;

[0128] The time pressure index is obtained according to the integral value corresponding to the fluid pressure and the integral value corresponding to the hydrostatic pressure.

[0129] In one embodiment, the prediction model is obtained in advance as follows:

[0130] Pre-establish the TTI, time pressure index, and fitting formulas of the original porosity before burial and the current porosity of multiple clastic reservoirs;

[0131] The fitting formula was verified and calculated using the historical TTI, time pressure index, original porosity before burial and actual porosity of the clastic reservoir to obtain multiple porosity calculation values.

[0132] The fitting formula corresponding to the minimum difference between the calculated porosity and the actual porosity is used as the prediction model.

[0133] In one embodiment, the prediction model is expressed as follows:

[0134] Where, Φ 预测 is the current porosity of the layer to be predicted, Φ0 is the original porosity of the layer to be predicted before burial, TTI is the time-temperature index value, TPI is the time-pressure index value, and k, m, and n are fitting coefficients.

[0135] In one embodiment, the temperature evolution data includes one or any combination of the following:

[0136] Current formation temperature data, historical geothermal gradient data, historical heat flow value data, and thermal conductivity data of different formations.

[0137] In one embodiment, the pressure evolution data includes one or any combination of the following:

[0138] Current formation pressure test data, historical fluid inclusion pressure data, and current layer pressure data interpreted by geophysical methods.

[0139] In one embodiment, the TTI calculation module 704 is specifically configured to:

[0140] The TTI was determined using the Lopatin method according to the first function model.

[0141] An embodiment of the present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned clastic reservoir porosity prediction method is implemented.

[0142] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned clastic reservoir porosity prediction method is implemented.

[0143] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above-mentioned clastic reservoir porosity prediction method is implemented.

[0144] In an embodiment of the present application, geological data of the layer to be predicted in the clastic rock reservoir are obtained, a first function model of the evolution of the temperature of the layer to be predicted with the burial time and a second function model of the evolution of the pressure of the layer to be predicted with the burial time are established, and the TTI and time-pressure index are determined respectively. The time-pressure index reflects the law of change of the fluid overpressure value in the layer to be predicted with the burial time. Finally, the porosity of the layer to be predicted before burial is determined using the original porosity, TTI and time-pressure index of the layer to be predicted. The embodiment of the present application focuses on the porosity of clastic reservoirs. On the one hand, the environmental temperature factor is taken into account, and the effect of environmental temperature on porosity is characterized by TTI. The higher the environmental temperature of the reservoir, the stronger the compaction effect, which ultimately affects the reservoir porosity. On the other hand, the pressure factor is taken into account, and a time-pressure index is proposed to characterize the effect of pressure on porosity. The time-pressure index is determined based on a second function model of the evolution of the pressure of the layer to be predicted with the burial time, reflecting the law of the change of fluid overpressure with the burial time. The fluid overpressure will inevitably affect the stress between the reservoir clastic particles, thereby affecting the porosity. Finally, the original porosity of the layer to be predicted before burial, TTI, time-pressure index and a pre-established prediction model are used to output the current porosity of the layer to be predicted. The embodiment of the present application predicts porosity by the underground temperature environment of the layer to be predicted and the pressure on the clastic particles. That is, on the basis of considering thermal evolution, the influence of pressure evolution on porosity is further considered, thereby improving the prediction accuracy of the porosity of the clastic reservoir.

[0145] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0146] The present application is described with reference to the flow chart and / or block diagram of the method, device (system), and computer program product according to the embodiment of the present application. It should be understood that each flow process and / or box in the flow chart and / or block diagram and the combination of the flow process and / or box in the flow chart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processing machine or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one flow chart flow or multiple flows and / or one box or multiple boxes of the block diagram.

[0147] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0149] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for predicting the porosity of a clastic reservoir, characterized in that: include: Acquire geological data of the layer to be predicted in the clastic rock reservoir; the geological data include temperature evolution data, pressure evolution data, and original porosity before burial; According to the temperature evolution data of the layer to be predicted, a first function model of the evolution of the temperature of the layer to be predicted with the burial time is established; According to the pressure evolution data of the layer to be predicted, a second function model of the evolution of the pressure of the layer to be predicted with the burial time is established; According to the first function model, determining a time temperature index; According to the second function model, a time pressure index is determined; the time pressure index reflects the law of the change of the fluid overpressure value in the layer to be predicted with time; The time-temperature index, time-pressure index, and original porosity before burial of the layer to be predicted are input into a pre-established prediction model, and the present porosity of the layer to be predicted is output. The prediction model reflects the correlation between the time-temperature index, time-pressure index, original porosity before burial and present porosity of the clastic reservoir.

2. The method according to claim 1, characterized in that The second function model includes a function model of the evolution of the fluid pressure of the layer to be predicted with the burial time and a function model of the evolution of the hydrostatic pressure of the layer to be predicted with the burial time; According to the second function model, the time pressure index is determined, including: The time pressure index is determined according to the function model of the fluid pressure of the layer to be predicted evolving with the burial time and the function model of the hydrostatic pressure of the layer to be predicted evolving with the burial time.

3. The method according to claim 2, characterized in that The function model of the evolution of the fluid pressure of the layer to be predicted with the burial time includes: a function curve of the change of the fluid pressure of the layer to be predicted with the burial time; The function model of the evolution of the hydrostatic pressure of the layer to be predicted with the burial time includes: a function curve of the hydrostatic pressure of the layer to be predicted with the burial time; According to the function model of the evolution of fluid pressure of the layer to be predicted with burial time and the function model of the evolution of hydrostatic pressure of the layer to be predicted with burial time, the time pressure index is determined, including: According to the function curve of the fluid pressure of the layer to be predicted changing with the burial time and the function curve of the hydrostatic pressure of the layer to be predicted changing with the burial time, integral calculation is performed along the time direction to obtain the integral value corresponding to the fluid pressure and the integral value corresponding to the hydrostatic pressure; The time pressure index is obtained according to the integral value corresponding to the fluid pressure and the integral value corresponding to the hydrostatic pressure.

4. The method according to claim 1, characterized in that The prediction model is obtained in advance as follows: Establish in advance the fitting formulas of time-temperature index, time-pressure index, original porosity before burial and current porosity of multiple clastic reservoirs; The fitting formula is verified and calculated by using the historical time-temperature index, time-pressure index, original porosity before burial and actual porosity of the clastic reservoir to obtain multiple porosity calculation values; The fitting formula corresponding to the minimum difference between the calculated porosity and the actual porosity is used as the prediction model.

5. The method according to claim 4, characterized in that The prediction model is expressed as follows: In the formula, Φ 预测 is the current porosity of the layer to be predicted, Φ0 is the original porosity of the layer to be predicted before burial, TTI is the time-temperature index value, TPI is the time-pressure index value, and k, m, and n are fitting coefficients.

6. The method according to claim 1, characterized in that The temperature evolution data includes one or any combination of the following: Current formation temperature data, historical geothermal gradient data, historical heat flow value data, and thermal conductivity data of different formations.

7. The method according to claim 1, characterized in that The pressure evolution data includes one or any combination of the following: Current formation pressure test data, historical fluid inclusion pressure data, and current stratification pressure data interpreted by geophysical methods.

8. The method according to claim 1, characterized in that According to the first function model, a time temperature index is determined, including: The time-temperature index was determined using the Lopatin method according to the first function model.

9. A clastic reservoir porosity prediction device, characterized in that: include: A data acquisition module is used to acquire geological data of the layer to be predicted in the clastic reservoir; the geological data includes temperature evolution data, pressure evolution data, and original porosity before burial; A thermal evolution history establishment module is used to establish a first function model of the evolution of the temperature of the layer to be predicted with the burial time according to the temperature evolution data of the layer to be predicted; A pressure evolution history establishment module is used to establish a second function model of the evolution of the pressure of the layer to be predicted with the burial time according to the pressure evolution data of the layer to be predicted; A TTI calculation module, used to determine a time temperature index according to a first function model; A time pressure index calculation module is used to determine the time pressure index according to the second function model; the time pressure index reflects the law of the change of the fluid overpressure value in the layer to be predicted with time; The result output module is used to input the time-temperature index, time-pressure index, and original porosity before burial of the layer to be predicted into a pre-established prediction model, and output the current porosity of the layer to be predicted. The prediction model reflects the correlation between the TTI, time-pressure index, original porosity before burial and current porosity of the clastic reservoir.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

12. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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