Method, system, device and readable storage medium for calculating maximum pyrolysis peak temperature

Abstract

The present application relates to the technical field of well logging evaluation, and particularly relates to a highest pyrolysis peak temperature calculation method, system, device and readable storage medium. The method comprises the following steps: constructing a first acoustic wave porosity and density porosity difference calculation model according to acoustic travel time logging curves and lithology density logging curves; performing lithology correction on the first acoustic wave porosity and density porosity difference calculation model based on the actual lithology characteristics of the target rock layer to obtain a second acoustic wave porosity and density porosity difference calculation model; performing linear regression on the second acoustic wave porosity and density porosity difference calculation model to establish a highest pyrolysis peak temperature calculation model of the target rock layer. Through the logging continuous characterization of the highest pyrolysis peak temperature, the continuous calculation of the organic geochemical parameters is realized, the limitation that the maturity of the source rock can only be evaluated through the core experiment results is broken, and a technical weapon is provided for the evaluation of the source rock maturity.

Description

Technical Field

[0001] This invention relates to the field of well logging evaluation technology, and in particular to a method, system, device, and readable storage medium for calculating the highest pyrolysis peak temperature. Background Technology

[0002] In recent years, with the sustained and rapid development of the economy, my country's demand for oil and gas resources has gradually increased. How to improve the efficiency of oil and gas resources and promote the high-quality development of the company is a problem that must be solved in oil and gas exploration, evaluation, and development. For both conventional and unconventional oil and gas reservoirs, good source rocks are the foundation for oil and gas generation. Therefore, source rock evaluation plays an extremely important role in oil and gas exploration and development. Source rocks refer to source rocks in which the amount of hydrocarbons generated by organic matter has already met the adsorption needs of the source rock itself, leading to the outward release of hydrocarbons. Source rocks generally include oil source rocks, gas source rocks, and oil and gas source rocks.

[0003] The abundance, type, and maturity of organic matter determine the quality of source rocks. Maturity, in particular, indicates the degree of thermal evolution experienced by the source rock during its geological history and is a crucial indicator of its hydrocarbon-generating capacity. Many parameters reflect organic matter maturity, and different experimental methods provide various types of data from different perspectives. A commonly used parameter is vitrinite reflectance R. o The highest pyrolysis peak temperature T max And the H / C atomic ratio.

[0004] According to relevant existing technologies, such as the vitrinite reflectance R... o With the highest pyrolysis peak temperature T max Relationship—Taking the Middle and Deep Hydrocarbon Source Rocks of the Nanpu Depression as an Example, Pyrolysis T max Similar literature, such as "A New Application of Value in Predicting Oil Generation Threshold, Geothermal Temperature and Maturity" and "An Exploration of Vitrinite Reflectivity as a Maturity Indicator", and similar patents such as CN118225620A (A Method for Determining Source Rock Maturity Using Pyrolysis Parameters) and CN116026945A (A Method for Identifying Crude Oil Maturity), and based on existing rock pyrolysis analysis techniques such as Rock-Eval and high-temperature simulation experiments, it has been found that the current method of evaluating source rock maturity using core experimental results lacks a method for evaluating source rock maturity with continuous well logging characterization. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, device, and readable storage medium for calculating the highest pyrolysis peak temperature, utilizing the highest pyrolysis peak temperature T. max Continuous logging characterization is used to evaluate the maturity of source rocks.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for calculating the highest pyrolysis peak temperature includes:

[0008] Based on the sonic transit time logging curve and the lithological density logging curve, a calculation model for the difference between the first sonic porosity and the density porosity is constructed.

[0009] Based on the actual lithological characteristics of the target rock stratum, the first acoustic porosity and density porosity difference calculation model is lithologically corrected to obtain the second acoustic porosity and density porosity difference calculation model.

[0010] Linear regression was performed on the second acoustic porosity and density porosity difference calculation model to establish the highest pyrolysis peak temperature calculation model of the target rock layer.

[0011] Furthermore, the calculation model for the difference between sonic porosity and density porosity, constructed based on the sonic transit time logging curve and the lithology density logging curve, is as follows:

[0012] DPOR=(△t-△tma) / (△tf-△tma)-(ρma-ρb) / (ρma-ρf)

[0013] Wherein, DPOR is the difference between the first acoustic porosity and the density porosity;

[0014] △t is the sonic transit time logging value;

[0015] ρb is the lithological density logging value;

[0016] △tma represents the acoustic transit time of the rock skeleton;

[0017] △tf represents the acoustic transit time of formation fluids;

[0018] ρma is the density of the rock skeleton;

[0019] ρf is the density of the formation fluid.

[0020] Furthermore, based on the actual lithological characteristics of the target rock stratum, the first acoustic porosity and density porosity difference calculation model is lithologically corrected to obtain a second acoustic porosity and density porosity difference calculation model, including:

[0021] The lithological scanning results of the target rock layer were obtained. By converting various minerals into quartz and clay in equal volumes, the acoustic transit time reconstruction curve and the lithological density reconstruction curve were obtained.

[0022] The first acoustic porosity and density porosity difference calculation model is corrected using the acoustic time difference reconstruction curve and the lithological density reconstruction curve to construct a second acoustic porosity and density porosity difference calculation model.

[0023] Furthermore, the volumetric content of various minerals, including calcite, dolomite, potassium feldspar, and sodium feldspar, is converted into quartz content.

[0024] Furthermore, the lithological density reconstruction curve is as follows:

[0025] DENc=(VC+VD+VK+VN+VQ)*ρmaQ+VI*ρmaI+VKerogen*ρmaV

[0026] The acoustic time difference reconstruction curve is as follows:

[0027] ACc=(VC+VD+VK+VN+VQ)*△tfQ+VI*△tfI+VKerogen*△tfV

[0028] Where DENc is the reconstructed lithological density value;

[0029] ACc is the reconstructed time difference value of acoustic waves;

[0030] VC represents the calcite content;

[0031] VD represents the dolomite content;

[0032] VK represents the potassium feldspar content;

[0033] VN represents the albite content;

[0034] VQ represents the quartz content;

[0035] VI represents the clay content;

[0036] VKerogen is the content of kerogen;

[0037] ρmaQ is the density of the quartz framework;

[0038] ρmaI is the density of the clay skeleton;

[0039] ρmaV is the density of kerogen;

[0040] △tfQ represents the time difference of quartz acoustic waves;

[0041] △tfI represents the time difference of acoustic waves in clay.

[0042] △tfV represents the time difference of kerogen acoustic waves.

[0043] Furthermore, the calculation model for the difference between the second acoustic porosity and density porosity is as follows:

[0044] DPORc=(ACc-Δtma) / (Δtf-Δtma)-(ρma-DENc) / (ρma-ρf)

[0045] Wherein, DPORC is the difference between the second acoustic porosity and the density porosity.

[0046] Furthermore, the calculation model for the highest pyrolysis peak temperature is as follows:

[0047] T max =a + b × DPOrc

[0048] Among them, T max The highest pyrolysis peak temperature is denoted as , and a and b are constant coefficients.

[0049] The present invention also provides a system for calculating the highest pyrolysis peak temperature, comprising:

[0050] The first data processing module is used to construct a calculation model for the difference between the first acoustic porosity and density porosity based on the acoustic time-of-flight logging curve and the lithology density logging curve.

[0051] The second data processing module is used to perform lithological correction on the first acoustic porosity and density porosity difference calculation model based on the actual lithological characteristics of the target rock layer, so as to obtain the second acoustic porosity and density porosity difference calculation model.

[0052] The third data processing module is used to perform linear regression on the second acoustic porosity and density porosity difference calculation model to establish a calculation model for the highest pyrolysis peak temperature of the target rock layer.

[0053] Based on the same inventive concept, the present invention also provides an electronic device, including: a memory and a processor; the processor is used to read and execute a computer program stored in the memory to implement the aforementioned method for calculating the highest pyrolysis peak temperature.

[0054] Based on the same inventive concept, the present invention also provides a computer storage medium storing computer-executable instructions, which, when executed, implement the aforementioned method for calculating the highest pyrolysis peak temperature.

[0055] The technical effects and advantages of this invention are as follows:

[0056] (1) Based on well logging curves with good correlation and sensitivity to organic matter content, such as sonic transit time logging curves and lithological density logging curves, and combined with actual lithological characteristics, a well logging characterization model for geochemical parameters is established, providing a model for the highest pyrolysis peak temperature T. max The calculation method enables continuous calculation of organic geochemical parameters;

[0057] (2) By measuring the highest pyrolysis peak temperature T max The continuous logging characterization breaks through the limitation that the maturity of source rocks can only be evaluated through core test results, providing a powerful technical tool for evaluating the maturity of source rocks.

[0058] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 The highest pyrolysis peak temperature T provided in the embodiments of the present invention max Flowchart of the calculation method;

[0061] Figure 2 In the embodiments of the present invention, T max Relationship diagram with DPOR;

[0062] Figure 3 In the embodiments of the present invention, T max Relationship diagram with DPORC;

[0063] Figure 4 In the embodiments of the present invention, T max Calculation results diagram;

[0064] Figure 5 In the embodiments of the present invention, T max Calculated value and T max A comparison chart of measured values. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] The highest pyrolysis peak temperature T in rock pyrolysis analysis max It is one of the most important parameters for evaluating the organic matter maturity of source rocks. Its evaluation mechanism is that the kerogen in the source rock undergoes thermal degradation to generate oil and gas. The part with the worst thermal stability (lowest activation energy required) degrades first, while the remaining part requires a higher temperature to degrade, resulting in the peak temperature T of the pyrolysis hydrocarbon peak. max Increase, i.e., T maxThe value increases continuously with the increasing maturity of the source rock, therefore T max It can be used to determine the maturity of source rocks.

[0067] This invention discloses a maximum pyrolysis peak temperature T max Calculation methods, such as Figure 1 As shown, the method includes the following steps:

[0068] S1. Based on the sonic transit time logging curve and the lithology density logging curve, construct a calculation model for the difference between sonic porosity and density porosity.

[0069] S2. Based on the actual lithological characteristics of the target rock layer, the first acoustic porosity and density porosity difference calculation model is lithologically corrected to obtain the second acoustic porosity and density porosity difference calculation model.

[0070] S3. Perform linear regression on the second acoustic porosity and density porosity difference calculation model to establish the highest pyrolysis peak temperature calculation model of the target rock layer.

[0071] The organic matter content in source rocks indirectly reflects the response characteristics of different logging curves. Therefore, the differences in response on logging curves caused by the uneven distribution of organic matter content in source rocks can be used to achieve qualitative identification and quantitative evaluation of source rocks. Generally, the higher the organic matter content in source rocks, the more obvious the anomalies reflected in the logging curve response characteristics. Analyzing the anomalies on logging curves is the basis for identifying and evaluating source rocks. Since logging curves for deep resistivity, lithological density, uranium-free gamma, sonic transit time, and spontaneous potential all have a certain correlation with the total organic matter content in source rocks, multiple logging curves with good correlation or sensitivity to the organic matter content in the core are selected based on the actual conditions of the study area to construct a system for calculating the highest pyrolysis peak temperature T. max The multiple regression mathematical model.

[0072] According to step S1, the logging curves selected in this embodiment of the invention are sonic transit time logging curves and lithology density logging curves. A first calculation model for the difference between sonic porosity and density porosity is constructed based on the sonic transit time logging curves and lithology density logging curves. Generally speaking, the organic matter in organic-rich shale is mainly dispersed. For dispersed organic matter in the rock formation, the propagation time of the longitudinal wave in sonic logging is basically not controlled by the dispersed organic matter and its maturity. The logging response value of lithology density logging is the average value of the measurement range. The lithology density logging value is controlled by the organic matter content and maturity. Moreover, the higher the maturity of the organic matter, the lower the density value. Therefore, according to the first calculation model for the difference between sonic porosity and density porosity constructed based on the sonic transit time logging curves and lithology density logging curves, when the maturity of the organic matter in the target rock formation is higher and the lithology density logging value is lower, the values ​​of sonic porosity and density porosity are larger.

[0073] According to a preferred embodiment, the first sonic porosity and density porosity difference calculation model constructed based on the sonic transit time logging curve and the lithology density logging curve is as follows:

[0074] DPOR=(△t-△tma) / (△tf-△tma)-(ρma-ρb) / (ρma-ρf)

[0075] Wherein, DPOR is the difference between the first acoustic porosity and the density porosity, in decimal form;

[0076] △t is the sonic transit time logging value, in microseconds per foot (µs / ft);

[0077] ρb is the lithological density logging value, in grams per cubic centimeter (g / cm³). 3 );

[0078] △tma represents the time difference of sound waves through the rock skeleton, measured in microseconds per foot (µs / ft).

[0079] △tf represents the acoustic transit time of formation fluids, expressed in microseconds per foot (µs / ft).

[0080] ρma is the density of the rock skeleton, expressed in grams per cubic centimeter (g / cm³). 3 );

[0081] ρf is the density of the formation fluid, expressed in grams per cubic centimeter (g / cm³). 3 ).

[0082] In practical applications, comparing the core pyrolysis data with the first acoustic porosity and density porosity difference calculation model reveals consistent variation characteristics and a good correlation. However, for carbonaceous mudstone sections with high organic carbon (TOC), the acoustic logging and lithological density logging values ​​exhibit some distortion, failing to reflect the true porosity of the formation. Therefore, according to step S2, the first acoustic porosity and density porosity difference calculation model is lithologically corrected using the actual lithological characteristics of the target stratum to obtain the second acoustic porosity and density porosity difference calculation model.

[0083] Specifically, by analyzing the mineral composition of the target rock layer, and considering that multiple minerals can affect the accuracy of the sonic transit time logging curve and the lithological density logging curve, the lithological scanning results of the target rock layer are obtained. By converting multiple minerals into quartz and clay in equal volumes, the influence of other minerals on the sonic transit time logging value and the lithological density logging value is eliminated, and the sonic transit time reconstruction curve and the lithological density reconstruction curve are obtained. Then, the sonic transit time reconstruction curve and the lithological density reconstruction curve are used to correct the first sonic porosity and density porosity difference calculation model, and a second sonic porosity and density porosity difference calculation model is constructed.

[0084] According to a preferred embodiment of the present invention, the content of various minerals, including calcite, dolomite, potassium feldspar, and sodium feldspar, is converted into quartz content by volume.

[0085] Therefore, the lithological density reconstruction curve is:

[0086] DENc=(VC+VD+VK+VN+VQ)*ρmaQ+VI*ρmaI+VKerogen*ρmaV

[0087] The acoustic time difference reconstruction curve is as follows:

[0088] ACc=(VC+VD+VK+VN+VQ)*△tfQ+VI*△tfI+VKerogen*△tfV

[0089] Where DENc is the reconstructed lithological density value, in grams per cubic centimeter (g / cm³). 3 );

[0090] ACc is the acoustic time-of-flight reconstruction value, measured in grams per cubic centimeter (g / cm³). 3 );

[0091] VC represents the calcite content, expressed as a decimal.

[0092] VD represents the dolomite content, expressed as a decimal.

[0093] VK represents the potassium feldspar content, expressed as a decimal.

[0094] VN represents the albite content, expressed as a decimal.

[0095] VQ represents the quartz content, expressed as a decimal.

[0096] VI represents the clay content, expressed as a decimal.

[0097] VKerogen is the kerogen content, expressed as a decimal.

[0098] ρmaQ is the quartz framework density, expressed in grams per cubic centimeter (g / cm³).3 );

[0099] ρmaI is the density of the clay skeleton, expressed in grams per cubic centimeter (g / cm³). 3 );

[0100] ρmaV is the density of kerogen, expressed in grams per cubic centimeter (g / cm³). 3 );

[0101] △tfQ represents the time difference of quartz acoustic waves, measured in microseconds per foot (µs / ft).

[0102] △tfI represents the clay acoustic transit time, measured in microseconds per foot (µs / ft).

[0103] △tfV represents the time difference of kerogen acoustic waves, measured in microseconds per foot (µs / ft).

[0104] Therefore, the calculation model for the second acoustic porosity and density porosity difference constructed using the acoustic transit time reconstruction curve and the lithological density reconstruction curve is as follows:

[0105] DPORc=(ACc-Δtma) / (Δtf-Δtma)-(ρma-DENc) / (ρma-ρf)

[0106] Wherein, DPORC is the difference between the second acoustic porosity and the density porosity, which is the reconstructed value of the difference between the first acoustic porosity and the density porosity, and the unit of DPORC is a decimal.

[0107] In this embodiment of the invention, the content of various minerals, including calcite, dolomite, potassium feldspar, and sodium feldspar, is converted into quartz content by volume. This eliminates the influence of various minerals such as calcite, dolomite, potassium feldspar, and sodium feldspar in the target rock layer on the sonic transit time logging value and lithological density logging value, making the calculation results of the difference between sonic porosity and density porosity more accurate.

[0108] Further, in step S3, a linear regression analysis is performed on the calculation model of the difference between the second acoustic porosity and density porosity to establish the calculation model for the highest pyrolysis peak temperature as follows:

[0109] T max =a + b × DPOrc

[0110] Among them, T max The highest pyrolysis peak temperature of the target rock stratum, expressed in degrees Celsius (°C).

[0111] DPORc is the difference between the second acoustic porosity and the density porosity, expressed as a decimal.

[0112] a and b are constant coefficients, which are dimensionless.

[0113] In this embodiment of the invention, based on well logging curves with good correlation and sensitivity to organic matter content, such as sonic transit time logging curves and lithological density logging curves, and combined with actual lithological characteristics, a well logging characterization model for geochemical parameters is established, providing a model for the highest pyrolysis peak temperature T. max The calculation method enables continuous calculation of organic geochemical parameters. Furthermore, in this embodiment of the invention, the highest pyrolysis peak temperature T is used... max The continuous logging characterization breaks through the limitation that the maturity of source rocks can only be evaluated through core experimental results, and the calculated highest pyrolysis peak temperature T max The relative and absolute errors meet the requirements of source rock logging evaluation, providing a powerful technical tool for evaluating source rock maturity.

[0114] The following is an illustration through a specific example:

[0115] The technical solution of this invention is illustrated using the source rock of a well (Well Ji 174) in the Junggar Basin as an example. (See also...) Figures 2 to 5 , Figure 2 The highest pyrolysis peak temperature T max The relationship between DPOR and the difference between the first acoustic porosity and density porosity; Figure 3 The highest pyrolysis peak temperature T max The relationship between DPORC and the difference between second acoustic porosity and density porosity; Figure 4 The highest pyrolysis peak temperature T in this embodiment is max Calculation results diagram; Figure 5 In this embodiment, T max Calculated value and T max A comparison chart of measured values.

[0116] 1. Based on the logging response characteristics of source rocks, the acoustic transit time logging curve and lithological density logging curve, which are sensitive to the organic matter content of the core, are selected. The DPOR calculation model for the first acoustic porosity and density porosity difference is constructed as follows:

[0117] DPOR=(△t-△tma) / (△tf-△tma)-(ρma-ρb) / (ρma-ρf)

[0118] Wherein, △t is the sonic transit time log value, in microseconds per foot (µs / ft); △tma is the sonic transit time of the rock skeleton, in microseconds per foot (µs / ft); △tf is the sonic transit time of the formation fluid, in microseconds per foot (µs / ft); and ρb is the lithology density log value, in grams per cubic centimeter (g / cm³). 3 ); ρma is the density of the rock skeleton, in grams per cubic centimeter (g / cm³). 3 ); ρf represents the formation fluid density, in grams per cubic centimeter (g / cm³). 3).

[0119] Given that the acoustic transit time Δtma of the rock skeleton is 55 μS / ft, the acoustic transit time Δtf of the formation fluid is 189 μS / ft, and the density ρma of the rock skeleton is 2.65 g / cm³,... 3 The formation fluid density ρf is 1 g / cm³. 3 The calculation model for the difference between the first acoustic porosity and density porosity (DPOR) is as follows:

[0120] DPOR=(△t-55) / 134-(2.65-ρb) / 1.65

[0121] 2. Obtain the lithological scanning results of the target rock layer, and convert the contents of various minerals, including calcite, dolomite, potassium feldspar, and sodium feldspar, into quartz content by volume. Eliminate the influence of various minerals such as calcite, dolomite, potassium feldspar, and sodium feldspar in the target rock layer on the sonic transit time logging value and the lithological density logging value, and obtain the sonic transit time reconstruction curve and the lithological density reconstruction curve.

[0122] The lithological density reconstruction curve is as follows:

[0123] DENc=(VC+VD+VK+VN+VQ)*ρmaQ+VI*ρmaI+VKerogen*ρmaV

[0124] The acoustic time difference reconstruction curve is as follows:

[0125] ACc=(VC+VD+VK+VN+VQ)*△tfQ+VI*△tfI+VKerogen*△tfV

[0126] Where DENc is the reconstructed lithological density value, in grams per cubic centimeter (g / cm³). 3 ACc is the acoustic time difference reconstruction value, in grams per cubic centimeter (g / cm³). 3 ); VC is calcite content, in decimals; VD is dolomite content, in decimals; VK is potassium feldspar content, in decimals; VN is sodium feldspar content, in decimals; VQ is quartz content, in decimals; VI is clay content, in decimals; VKerogen is kerogen content, in decimals; ρmaQ is quartz framework density, in grams per cubic centimeter (g / cm³). 3 ); ρmaI is the density of the clay skeleton, in grams per cubic centimeter (g / cm³). 3 ); ρmaV is the density of kerogen, in grams per cubic centimeter (g / cm³). 3); △tfQ is the transit time of quartz sound waves, in microseconds per foot (µs / ft); △tfI is the transit time of clay sound waves, in microseconds per foot (µs / ft); △tfV is the transit time of kerogen sound waves, in microseconds per foot (µs / ft).

[0127] The density of the quartz framework, ρmaQ, is known to be 2.65 g / cm³. 3 The clay skeleton density ρmaI is 2.79 g / cm³. 3 The density of kerogen, ρmaV, is 1.4 g / cm³. 3 Therefore, the above lithological density reconstruction curve is as follows:

[0128] DENc=(VC+VD+VK+VN+VQ)*2.65+VI*2.79+VKerogen*1.4

[0129] Given that the transit time ΔtfQ for quartz is 55 μs / ft, for clay it is 90 μs / ft, and for kerogen it is 120 μs / ft, the reconstructed transit time curves for the above acoustic waves are:

[0130] ACc=(VC+VD+VK+VN+VQ)*55+VI*90+VKerogen*120

[0131] The DPOR calculation model is corrected using the lithological density reconstruction curve and the acoustic transit time reconstruction curve to construct a second acoustic porosity and density porosity difference (DPORc) calculation model:

[0132] DPORc=(ACc-55) / 134-(2.65-DENc) / 1.65

[0133] DPORc is the reconstructed value of DPOR, in decimal form.

[0134] 3. Based on the second acoustic porosity and density porosity difference DPORC calculation model, linear regression analysis is performed to establish the highest pyrolysis peak temperature T. max The calculation model is as follows:

[0135] T max =a + b × DPOrc

[0136] Among them, T max The highest pyrolysis peak temperature of the target rock formation is given in degrees Celsius (°C), and a and b are relevant constant coefficients. Based on the analysis of organic geochemical experimental data and well logging data, the value range of parameter a is 420–460, and the value range of parameter b is 390–410.

[0137] Preferably, a = 440, b = 400, therefore, the highest pyrolysis peak temperature T of the target rock stratum is... max The calculation model is as follows:

[0138] T max =440 + 400 × DPORC

[0139] In this embodiment of the invention, the highest pyrolysis peak temperature T is measured. max The continuous logging characterization breaks through the limitation that the maturity of source rocks can only be evaluated through core experimental results, and the calculated highest pyrolysis peak temperature T max The relative and absolute errors meet the requirements of source rock logging evaluation, providing a powerful technical tool for evaluating source rock maturity.

[0140] This invention also provides a maximum pyrolysis peak temperature T. max A computing system, the system comprising:

[0141] The first data processing module is used to construct a calculation model for the difference between the first acoustic porosity and density porosity based on the acoustic time-of-flight logging curve and the lithology density logging curve.

[0142] The second data processing module is used to perform lithological correction on the acoustic porosity and density porosity difference calculation model based on the actual lithological characteristics of the target rock layer, so as to obtain the second acoustic porosity and density porosity difference calculation model.

[0143] The third data processing module is used to perform linear regression on the second acoustic porosity and density porosity difference calculation model to establish the highest pyrolysis peak temperature T of the target rock layer. max Computational model.

[0144] In this embodiment of the invention, the first data processing module constructs a first calculation model for the difference between acoustic porosity and density porosity based on the acoustic transit time logging curve and the lithological density logging curve as follows:

[0145] DPOR=(△t-△tma) / (△tf-△tma)-(ρma-ρb) / (ρma-ρf)

[0146] Wherein, DPOR is the difference between the first sonic porosity and the density porosity; Δt is the sonic transit time logging value; Δtma is the sonic transit time of the rock skeleton; Δtf is the sonic transit time of the formation fluid; ρb is the lithological density logging value; ρma is the rock skeleton density; and ρf is the formation fluid density.

[0147] The second data processing module, based on the lithological scanning results of the target rock layer, converts the volumetric content of various minerals, including calcite, dolomite, potassium feldspar, and sodium feldspar, into quartz content, and obtains the lithological density reconstruction curve as follows:

[0148] DENc=(VC+VD+VK+VN+VQ)*ρmaQ+VI*ρmaI+VKerogen*ρmaV

[0149] The obtained acoustic time difference reconstruction curve is as follows:

[0150] ACc=(VC+VD+VK+VN+VQ)*△tfQ+VI*△tfI+VKerogen*△tfV

[0151] Wherein, DENc is the reconstructed lithological density value; ACc is the reconstructed sonic transit time value; VC is the calcite content; VD is the dolomite content; VK is the potassium feldspar content; VN is the sodium feldspar content; VQ is the quartz content; VI is the clay content; VKerogen is the kerogen content; ρmaQ is the quartz framework density value; ρmaI is the clay framework density; ρmaV is the kerogen density; ΔtfQ is the sonic transit time of quartz; ΔtfI is the sonic transit time of clay; and ΔtfV is the sonic transit time of kerogen.

[0152] The second data processing module also uses the acoustic transit time reconstruction curve and the lithological density reconstruction curve to correct the first acoustic porosity and density porosity difference calculation model, resulting in the second acoustic porosity and density porosity difference calculation model:

[0153] DPORc=(ACc-Δtma) / (Δtf-Δtma)-(ρma-DENc) / (ρma-ρf)

[0154] Wherein, DPORC is the difference between the second acoustic porosity and the density porosity.

[0155] Furthermore, the third data processing module performs linear regression analysis based on the second acoustic porosity and density porosity difference calculation model to establish the highest pyrolysis peak temperature T. max The calculation model is as follows:

[0156] T max =a + b × DPOrc

[0157] Among them, T max is the highest pyrolysis peak temperature of the target rock stratum; a and b are constant coefficients.

[0158] According to the embodiment of the present invention, the maximum pyrolysis peak temperature calculation system selects sonic transit time logging curves and lithological density logging curves, which have good correlation and sensitivity with organic matter content, and combines them with actual lithological characteristics to establish a logging characterization model of geochemical parameters, thereby providing a maximum pyrolysis peak temperature T. max The calculation method enables continuous calculation of organic geochemical parameters.

[0159] Regarding the system in the above embodiments, the specific manner in which each unit module performs operations has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0160] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, including: a memory and a processor, wherein the processor is configured to read and execute a computer program stored in the memory to achieve the aforementioned highest pyrolysis peak temperature T. max Calculation method.

[0161] Based on the same inventive concept, embodiments of the present invention also provide a computer storage medium storing computer-executable instructions, which, when executed, achieve the aforementioned highest pyrolysis peak temperature T. max Calculation method.

[0162] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0163] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, the functional modules in the various embodiments of this invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0164] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0165] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0166] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not described in detail in a particular embodiment can be found in the relevant descriptions of other embodiments. Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for calculating the highest pyrolysis peak temperature, characterized in that, The method includes: Based on the sonic transit time logging curve and the lithological density logging curve, a calculation model for the difference between the first sonic porosity and the density porosity is constructed. Based on the actual lithological characteristics of the target rock stratum, the first acoustic porosity and density porosity difference calculation model is lithologically corrected to obtain the second acoustic porosity and density porosity difference calculation model. Linear regression was performed on the second acoustic porosity and density porosity difference calculation model to establish the highest pyrolysis peak temperature calculation model of the target rock layer.

2. The method according to claim 1, characterized in that, The first calculation model for the difference between sonic porosity and density porosity, constructed based on sonic transit time logging curves and lithological density logging curves, is as follows: DPOR=(△t-△tma) / (△tf-△tma)-(ρma-ρb) / (ρma-ρf) Wherein, DPOR is the difference between the first acoustic porosity and the density porosity; △t is the sonic transit time logging value; ρb is the lithological density logging value; △tma represents the acoustic transit time of the rock skeleton; △tf represents the acoustic transit time of formation fluids; ρma is the density of the rock skeleton; ρf is the density of the formation fluid.

3. The method according to claim 2, characterized in that, The first acoustic porosity and density porosity difference calculation model is lithologically corrected based on the actual lithological characteristics of the target rock stratum to obtain a second acoustic porosity and density porosity difference calculation model, including: The lithological scanning results of the target rock layer were obtained. By converting various minerals into quartz and clay in equal volumes, the acoustic transit time reconstruction curve and the lithological density reconstruction curve were obtained. The first acoustic porosity and density porosity difference calculation model is corrected using the acoustic time difference reconstruction curve and the lithological density reconstruction curve to construct a second acoustic porosity and density porosity difference calculation model.

4. The method according to claim 3, characterized in that, The content of various minerals, including calcite, dolomite, potassium feldspar, and sodium feldspar, is converted into quartz content by volume.

5. The method according to claim 3 or 4, characterized in that, The lithological density reconstruction curve is as follows: DENc=(VC+VD+VK+VN+VQ)*ρmaQ+VI*ρmaI+VKerogen*ρmaV The acoustic time difference reconstruction curve is as follows: ACc=(VC+VD+VK+VN+VQ)*△tfQ+VI*△tfI+VKerogen*△tfV Where DENc is the reconstructed lithological density value; ACc is the reconstructed time difference value of acoustic waves; VC represents the calcite content; VD represents the dolomite content; VK represents the potassium feldspar content; VN represents the albite content; VQ represents the quartz content; VI represents the clay content; VKerogen is the content of kerogen; ρmaQ is the density of the quartz framework; ρmaI is the density of the clay skeleton; ρmaV is the density of kerogen; △tfQ represents the time difference of quartz acoustic waves; △tfI represents the time difference of acoustic waves in clay. △tfV represents the time difference of kerogen acoustic waves.

6. The method according to claim 5, characterized in that, The calculation model for the difference between the second acoustic porosity and density porosity is as follows: DPORc=(ACc-Δtma) / (Δtf-Δtma)-(ρma-DENc) / (ρma-ρf) Wherein, DPORC is the difference between the second acoustic porosity and the density porosity.

7. The method according to claim 6, characterized in that, The calculation model for the highest pyrolysis peak temperature is as follows: T max =a+b×DPORc Among them, T max The highest pyrolysis peak temperature is denoted as , and a and b are constant coefficients.

8. A system for calculating the highest pyrolysis peak temperature, characterized in that, The system includes: The first data processing module is used to construct a calculation model for the difference between the first acoustic porosity and density porosity based on the acoustic time-of-flight logging curve and the lithology density logging curve. The second data processing module is used to perform lithological correction on the first acoustic porosity and density porosity difference calculation model based on the actual lithological characteristics of the target rock layer, so as to obtain the second acoustic porosity and density porosity difference calculation model. The third data processing module is used to perform linear regression on the second acoustic porosity and density porosity difference calculation model to establish a calculation model for the highest pyrolysis peak temperature of the target rock layer.

9. An electronic device, characterized in that, include: Memory, processor; The processor is configured to read and execute the computer program stored in the memory to implement the method for calculating the highest pyrolysis peak temperature as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, implement the method for calculating the highest pyrolysis peak temperature as described in any one of claims 1-7.

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