Clastic rock reservoir porosity prediction method and device

Abstract

The invention discloses a clastic rock reservoir porosity prediction method and device, and relates to the technical field of petroleum and natural gas exploration, and the method comprises the steps: obtaining temperature evolution data and pressure evolution data of a to-be-predicted layer; according to the temperature evolution data, establishing a first function model of the temperature of the to-be-predicted layer evolved along with the burying time; according to the pressure evolution data, establishing a second function model of the pressure of the to-be-predicted layer evolved along with the burying time; determining a time temperature index TTI according to the first function model; determining a time pressure index according to the second function model; the time pressure index reflects the rule that the fluid overpressure changes along with time; the TTI, the time pressure index and the original porosity before burying of the to-be-predicted layer are input into a pre-established prediction model, the current porosity of the to-be-predicted layer is output, and the prediction model reflects the incidence relation among the TTI, the time pressure index, the original porosity and the current porosity. According to the method, the prediction precision of the porosity of the clastic rock reservoir can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas exploration, and in particular to a method and a device for predicting the porosity of a clastic rock reservoir. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] 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 clastic reservoir.

[0004] There are two types of methods commonly used in the prior art for predicting the porosity of 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 most easily obtained parameter that 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, and the porosity-depth function is usually poorly correlated, so the error in predicting porosity using depth is also large. The second type is to first simulate the thermal evolution history of the reservoir based on the temperature field and geological stratification data of the existing wells in the study area, calculate the thermal maturity data of the reservoir, establish the reservoir thermal maturity-porosity trend line and function, and then calculate the thermal maturity value of the target reservoir at a certain depth based on the temperature field and geological stratification data of the target layer, and use the established function to predict the porosity. The disadvantage of this method is that it only considers the difference in compaction reduction caused by different thermal evolution histories of the reservoir, and the factors considered are relatively single, resulting in low porosity prediction accuracy for deep clastic reservoirs. Summary of the invention

[0005] An embodiment of the present invention provides a clastic reservoir porosity prediction method for improving the porosity prediction accuracy of the clastic reservoir. The method includes:

[0006] 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;

[0007] 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;

[0008] 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;

[0009] According to the first function model, a time-temperature index TTI (Time-Temperature Index, TTI) is determined;

[0010] According to the second function model, a time-pressure index (TPI) is determined; the time-pressure index reflects the law of the change of the fluid overpressure in the layer to be predicted with time;

[0011] 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.

[0012] 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;

[0013] According to the second function model, a time pressure index is determined, including:

[0014] 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.

[0015] 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;

[0016] 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;

[0017] 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:

[0018] 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;

[0019] 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.

[0020] In one embodiment, the prediction model is obtained in advance as follows:

[0021] Establish in advance the fitting formulas of TTI, time pressure index, original porosity before burial and current porosity of multiple clastic reservoirs;

[0022] The fitting formula is verified and calculated by using the historical TTI, time pressure index, original porosity before burial and actual porosity of the clastic reservoir to obtain multiple porosity calculation values;

[0023] The fitting formula corresponding to the minimum difference between the calculated porosity and the actual porosity is used as the prediction model.

[0024] In one embodiment, the prediction model is expressed as follows:

[0025]

[0026] In the formula, Φ 预测 is the current porosity of the layer to be predicted, Φ 0 is the original porosity of the predicted layer before burial, TTI is the time-temperature index value, TPI is the time-pressure index value, and k, m, and n are fitting coefficients.

[0027] In one embodiment, the temperature evolution data includes one or any combination of the following:

[0028] Current formation temperature data, historical geothermal gradient data, historical heat flow value data, and thermal conductivity data of different formations.

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

[0030] Current formation pressure test data, historical fluid inclusion pressure data, and current stratification pressure data interpreted by geophysical methods.

[0031] In one embodiment, determining the TTI according to the first function model includes:

[0032] The TTI was determined using the Lopatin method according to the first function model.

[0033] The embodiment of the present invention further provides a clastic reservoir porosity prediction device for improving the porosity prediction accuracy of the clastic reservoir, the device comprising:

[0034] A data acquisition module is used to acquire geological data of the layer to be predicted in the clastic rock reservoir; the geological data includes temperature evolution data, pressure evolution data, and original porosity before burial;

[0035] 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;

[0036] 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;

[0037] A TTI calculation module, used to determine TTI according to the first function model;

[0038] 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 in the layer to be predicted with time;

[0039] 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.

[0040] An embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned clastic reservoir porosity prediction method when executing the computer program.

[0041] An embodiment of the present invention 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.

[0042] An embodiment of the present invention 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.

[0043] In an embodiment of the present invention, geological data of the layer to be predicted in the clastic rock reservoir is 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 fluid overpressure 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 invention takes into account the porosity of the clastic reservoir. On the one hand, the environmental temperature factor is considered, and the influence of the environmental temperature on the 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 considered, and the time pressure index is proposed to characterize the influence of pressure on the porosity. The time pressure index is determined according to the 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 invention predicts the 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0045] Figure 1 A schematic diagram of a process for predicting the porosity of a clastic reservoir in an embodiment of the present invention;

[0046] Figure 2 It 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 invention;

[0047] Figure 3 It 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 invention;

[0048] Figure 4 Schematic diagram of the evolution history of excess pressure in a typical well reservoir in an embodiment of the present invention;

[0049] Figure 5 This is a specific embodiment of the clastic reservoir porosity prediction method in the embodiment of the present invention;

[0050] Figure 6 This is a specific embodiment of the temperature evolution of the layer to be predicted with the burial time in the embodiment of the present invention;

[0051] Figure 7 Schematic diagram of a clastic reservoir porosity prediction device in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0053] First, technical terms involved in the embodiments of the present invention are explained.

[0054] Fluid Pressure, also known as Formation Pressure, refers to the pressure of the fluid in the pores of the formation. The standard unit of formation pressure is Pa. MPa is usually used in geological research.

[0055] Hydrostatic Pressure: refers to the formation pressure when the formation fluid is connected to the surface water system.

[0056] Lopatin method: Modern petroleum genesis research has proved that temperature and time are the main influencing factors in the process of organic matter hydrocarbon evolution. To be precise, temperature and hydrocarbon generation are exponentially related, and time and hydrocarbon generation are positively linearly related. Based on the above ideas, Lopatin proposed a simple method in 1969-1971 to quantitatively calculate the maturity of organic matter based on time and temperature, namely the time-temperature index (TTI for short). This method was supplemented and developed by Whippleis (1980) and has been widely used in oil and gas exploration.

[0057] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations.

[0058] The applicant found that the porosity prediction accuracy of clastic reservoirs in the prior art is low. To this end, the applicant proposed a porosity prediction method for clastic reservoirs. On the basis of predicting the thermal evolution history and pressure evolution history of the layer to be predicted, the time-temperature index and time-pressure index of the layer to be predicted are calculated, and a porosity prediction model for deep reservoirs and ultra-deep reservoirs is established to quantitatively evaluate the effects of different thermal histories and pressure evolution histories on the compaction porosity reduction, so that the porosity of clastic reservoirs at a certain depth can be accurately predicted.

[0059] Figure 1FIG. 1 is a schematic diagram of a process for predicting the porosity of a clastic reservoir in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0060] Step 101, obtaining 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;

[0061] Step 102: establishing 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;

[0062] Step 103: establishing 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;

[0063] Step 104: Determine TTI according to the first function model;

[0064] Step 105: Determine a time pressure index according to the second function model; the time pressure index reflects the law of the change of fluid overpressure in the layer to be predicted with time;

[0065] Step 106: 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.

[0066] from Figure 1It can be seen from the process shown that in the embodiment of the present invention, geological data of the layer to be predicted in the clastic 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 fluid overpressure in the layer to be predicted with the burial time. Finally, the porosity of the layer to be predicted is determined using the original porosity of the layer to be predicted before burial, TTI and time-pressure index. The embodiment of the present invention takes into account the porosity of the clastic reservoir. On the one hand, the environmental temperature factor is considered, and the influence of the environmental temperature on the 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 considered, and the time pressure index is proposed to characterize the influence of pressure on the porosity. The time pressure index is determined according to the 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 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 the pre-established prediction model are used to output the current porosity of the layer to be predicted. The embodiment of the present invention predicts the 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.

[0067] The porosity prediction method of clastic reservoir in the embodiment of the present invention is explained in detail below.

[0068] In the embodiments of the present invention, it is considered that the clastic reservoir is usually composed of many clastic particles, and pores are developed in the unfilled parts between the clastic particles. The porosity of the clastic reservoir is the proportion 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, and the particle arrangement 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 magnitude of the reservoir clastic particles. On the one hand, the depth of the reservoir usually increases gradually during the underground burial process. The higher the depth, the higher the underground temperature. The higher the environmental temperature of the reservoir, the stronger the compaction effect is usually, which affects the final porosity of the reservoir. On the other hand, 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, 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 then affect the size of the final porosity, where the total confining pressure is equal to the sum of the fluid pressure in the reservoir and the stress between the particles. Therefore, in the embodiments of the present invention, the effects of temperature and pressure on the reservoir porosity are considered at the same time.

[0069] During 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.

[0070] 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.

[0071] The burial history background data include 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.

[0072] Among them, the original porosity of the layer to be predicted before burial can be determined based on the lithological data of the layer to be predicted at the target well point, such as information such as the original grain size, sorting, and rigid particle content of the reservoir.

[0073] 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.

[0074] Pressure evolution data include one or any combination of the following: current formation pressure test data of the same study area or adjacent wells, fluid inclusion (paleo)historical pressure data, current stratified pressure data interpreted by geophysical methods, etc.

[0075] 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.

[0076] 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 the burial time. Figure 2 , Figure 3 , Figure 2 Schematic diagram of the 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 invention, Figure 3 The second function model schematic diagram of the evolution of the pressure of the layer to be predicted with the burial time in the embodiment of the present invention is shown in FIG. The restoration and reconstruction of the burial history can be done manually or by using existing geological software.

[0077] In step 104, TTI is determined according to the first function model.

[0078] For example, refer to Figure 2The first function model in is used to determine TTI. In one embodiment, the Lopatin method (Lopatin, 1971) is used to determine TTI according to the first function model. The specific calculation formula is as follows:

[0079]

[0080] In the formula, r is the temperature factor, which is generally 2. The index 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 time in the temperature range of the layer to be predicted, n max is the end time of the temperature interval of the layer to be predicted. i × n By accumulating, the total TTI value of the layer to be predicted can be obtained.

[0081] In step 105, a time pressure index is determined according to the second function model; the time pressure index reflects the law of the change of the fluid overpressure in the layer to be predicted with time.

[0082] 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 then 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 invention. 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 rocks. If the fluid overpressure is formed very late, the pores of the clastic reservoir have been compacted. 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 invention establishes this time pressure index that combines the overpressure intensity and the overpressure formation time to analyze the effect of overpressure on porosity change.

[0083] 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 change of fluid overpressure with time, and the time pressure index is calculated by integral calculation.

[0084] In order to improve the accuracy of clastic reservoir porosity prediction, in one embodiment, the pressure considered in this solution includes 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 the burial time and a function model of the evolution of the hydrostatic pressure of the layer to be predicted with the burial time;

[0085] According to the second function model, determining the time pressure index may include:

[0086] 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.

[0087] In this example, function 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 function model of the evolution of the fluid pressure of the layer to be predicted with burial time and the function model of the evolution of the hydrostatic pressure of the layer to be predicted with burial time.

[0088] 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;

[0089] 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;

[0090] 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:

[0091] 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;

[0092] 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.

[0093] When implementing it, refer to Figure 2 , the time pressure index TPI (Time-Pressure Index, TPI) can be calculated according to the following formula:

[0094]

[0095] In the formula, ΔP is the excess pressure of the reservoir (which can reach fluid overpressure), that is, fluid pressure minus hydrostatic pressure; ΔT is the duration of the predicted layer under this excess pressure. Obviously, if the reservoir is always under hydrostatic pressure, ΔP is always 0, and TPI is also 0, that is, the reservoir is not affected by fluid overpressure.

[0096] Among them, the fluid pressure can be recovered by basin simulation software. Hydrostatic pressure = formation water density (ρ) × gravity acceleration (g) × formation depth (h). The calculation of TPI is the integral of ΔP over time, or it can be understood as Figure 4 The difference between the two curves after definite integration. 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 also Figure 4 , Figure 4 Schematic diagram of the evolution history of excess pressure in a typical well reservoir in an embodiment of the present invention, showing the law of variation of excess pressure 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 invention, a prediction model is pre-established. The prediction model can be obtained in advance in the following manner:

[0100] Establish in advance the fitting formulas of TTI, time pressure index, original porosity before burial and current porosity of multiple clastic reservoirs;

[0101] The fitting formula is verified and calculated by 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 clastic rock formations' actual drilling TTI, TPI, original porosity data before burial, and actual porosity data are used for fitting and verification. After comparing the fitting effects of multiple fitting formulas (e.g., linear fitting, polynomial fitting, least squares fitting), the best fitting effect is selected, that is, the fitting formula corresponding to the smallest difference between the porosity calculated value and the actual porosity is selected as the prediction model. Among them, the actual porosity can be calculated using other existing more accurate methods and data.

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

[0105]

[0106] It can also be obtained through mathematical transformation:

[0107]

[0108] In the formula, Φ 预测 is the current porosity of the layer to be predicted, Φ 0 is the original porosity of the predicted layer before burial, TTI is the time-temperature index value, TPI is the time-pressure index value, k, m, and n are fitting coefficients obtained by the above fitting process.

[0109] In one embodiment, k=-2, m=1, n=1.

[0110] Figure 5 is a specific embodiment of the temperature evolution of the layer to be predicted with the burial time in the embodiment of the present invention, such as Figure 5 As shown in the figure, the porosity prediction method of clastic reservoir in the embodiment of the present invention is summarized, the reservoir burial history is restored and reconstructed, the reservoir thermal evolution history and reservoir pressure evolution history are respectively performed, the TTI value and TPI value are respectively calculated, and finally based on Figure 5 The formula in can realize the quantitative prediction of porosity. It can be seen that Figure 5 In, 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 for different tools and using different data according to the method in the embodiment of the present invention.

[0111] Figure 6 is a specific embodiment of the temperature evolution of the layer to be predicted with the burial time in the embodiment of the present invention, Figure 6 The horizontal axis 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 invention, and the circular mark is the measured porosity. It can be seen that the porosity predicted in the implementation of the present invention is basically consistent with the measured porosity.

[0112] The embodiment of the present invention 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 to predict the porosity. No logging or core data is required, and the method is convenient and low-cost.

[0113] In the experimental work area, the method of the embodiment of the present invention 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%, which greatly improves the efficiency and quality of zone optimization and reduces exploration risks and exploration investment costs.

[0114] The present invention also provides a clastic reservoir porosity prediction device, as described in the following embodiments. Since the principle of the device to solve the problem is similar to 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.

[0115] Figure 7 FIG. 1 is a schematic diagram of a clastic reservoir porosity prediction device according to an embodiment of the present invention. Figure 7 As shown, the device comprises:

[0116] 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;

[0117] 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 according to the temperature evolution data of the layer to be predicted;

[0118] 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 according to the pressure evolution data of the layer to be predicted;

[0119] A TTI calculation module 704 is used to determine the TTI according to the first function model;

[0120] 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 in the layer to be predicted with time;

[0121] 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.

[0122] 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;

[0123] The time pressure index calculation module 705 is specifically used for:

[0124] 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.

[0125] In one embodiment,

[0126] 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;

[0127] 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;

[0128] The time pressure index calculation module 705 is specifically used for:

[0129] 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;

[0130] 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.

[0131] In one embodiment, the prediction model is obtained in advance as follows:

[0132] Establish in advance the fitting formulas of TTI, time pressure index, original porosity before burial and current porosity of multiple clastic reservoirs;

[0133] The fitting formula is verified and calculated by using the historical TTI, time pressure index, original porosity before burial and actual porosity of the clastic reservoir to obtain multiple porosity calculation values;

[0134] The fitting formula corresponding to the minimum difference between the calculated porosity and the actual porosity is used as the prediction model.

[0135] In one embodiment, the prediction model is expressed as follows:

[0136]

[0137] In the formula, Φ 预测 is the current porosity of the layer to be predicted, Φ 0is the original porosity of the predicted layer before burial, TTI is the time-temperature index value, TPI is the time-pressure index value, and k, m, and n are fitting coefficients.

[0138] In one embodiment, the temperature evolution data includes one or any combination of the following:

[0139] Current formation temperature data, historical geothermal gradient data, historical heat flow value data, and thermal conductivity data of different formations.

[0140] In one embodiment, the pressure evolution data includes one or any combination of the following:

[0141] Current formation pressure test data, historical fluid inclusion pressure data, and current stratification pressure data interpreted by geophysical methods.

[0142] In one embodiment, the TTI calculation module 704 is specifically used to:

[0143] The TTI is determined based on the first function model using the Lopatin method.

[0144] An embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned clastic reservoir porosity prediction method when executing the computer program.

[0145] An embodiment of the present invention 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.

[0146] An embodiment of the present invention 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.

[0147] In an embodiment of the present invention, geological data of the layer to be predicted in the clastic rock reservoir is 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 fluid overpressure in the layer to be predicted with the burial time. Finally, the porosity of the layer to be predicted is determined using the original porosity of the layer to be predicted before burial, TTI and time-pressure index. The embodiment of the present invention takes into account the porosity of the clastic reservoir. On the one hand, the environmental temperature factor is considered, and the influence of the environmental temperature on the 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 considered, and the time pressure index is proposed to characterize the influence of pressure on the porosity. The time pressure index is determined according to the 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 the pre-established prediction model are used to output the current porosity of the layer to be predicted. The embodiment of the present invention predicts the 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.

[0148] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0149] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented 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 processor, 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 generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0150] These computer program instructions may also be stored in a computer-readable memory capable of directing 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 an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0151] 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, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0152] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

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, a time temperature index TTI is determined; 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 in the layer to be predicted with time; 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.

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 TTI, 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 TTI, 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: Wherein, Φprediction 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, determining TTI includes: The TTI 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 TTI according to the 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 in the layer to be predicted with time; 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.

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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