Quantitative prediction method for supply capacity of sandstone and conglomerate provenance based on markov chain

Abstract

The present application provides a kind of based on Markov chain's sandstone and conglomerate source supply capacity quantitative prediction method, comprising: step 1, understand the basic geological profile of study area, carry out Cenozoic standard layer structural diagram interpretation;Step 2, determine the prediction object and initial state probability vector and time series vector, establish Markov prediction model;Step 3, apply statistical estimation method, establish one-step transition probability matrix;Step 4, realize multiple state probability prediction by one-step transition probability matrix;Step 5, based on source supply intensity factor K, establish the functional relationship between source supply intensity factor K and fan body advancing (retreating) distance.The sandstone and conglomerate source supply capacity quantitative prediction method based on Markov chain has high practical value, strong operability, high prediction result accuracy, and has significant progress compared with the traditional gentle slope zone sandstone and conglomerate source area source supply capacity quantitative prediction.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a quantitative prediction method for the source supply capacity of sandstone and conglomerate bodies based on Markov chains. Background Technology

[0002] With the continuous deepening of global oil and gas exploration and development, oil and gas exploration in sandstone and conglomerate bodies has received increasing attention both domestically and internationally, and oil and gas reservoirs in sandstone and conglomerate bodies are constantly being discovered in deep parts of basins. Currently, only a few scholars have conducted research on the quantitative supply capacity of sandstone and conglomerate bodies, and it is basically still at the qualitative to semi-quantitative stage.

[0003] Current technologies for quantifying the provenance supply capacity of conglomerate bodies can only perform qualitative to semi-quantitative studies, raising doubts about the regularity of geostatistical results and the reliability of provenance supply intensity. Furthermore, there is currently no mature method for determining the distribution range of sedimentary systems in areas with few or no wells, and the factor of poor seismic data response is not considered. The application of Markov chain model analysis methods in geology is limited to vertical lithology simulation and sedimentary sequences of sedimentary strata; there is no clear technique for quantitatively predicting the provenance supply capacity of conglomerate bodies based on Markov chains. Therefore, current technologies cannot solve the problem of determining the distribution range of sedimentary systems in areas with few or no wells and poor seismic data response.

[0004] A quantitative evaluation method for the source supply capacity of sandstone and conglomerate is provided in the journal *Petroleum and Natural Gas*, Volume 39, Issue 6, pages 1151-1163, retrieved from the CNKI database. This method is the closest existing technology to the present invention. The method involves: based on studies of conglomerate content, sand-to-soil ratio, lithology index, development degree of interlayers, mineral content, and intensity distribution, using a quantitative weighted average of parameters directly proportional to and inversely proportional to the source supply rate to calculate the source supply capacity, and using a source supply capacity index to characterize the source supply capacity. Although this method considers parameters with a good correlation to source supply, it has significant drawbacks: the statistical results of parameters from adjacent strata are independent or have no necessary connection, leading to doubts about the regularity of the statistical results and the reliability of the source supply intensity.

[0005] The quality of drilling and seismic data determines the accuracy of sedimentary system studies. In multi-well areas, using seismic attributes and single-well sedimentary facies to determine planar sedimentary facies provides high accuracy. However, in areas with poor seismic data response and few or no wells, there is still no mature method for studying the distribution range of sedimentary systems.

[0006] A technique for analyzing sedimentary systems in areas with few or no wells, presented in the journal *Marine Geology and Quaternary Geology*, Volume 32, Issue 1, pages 151-157 (accessed via CNKI database), describes the application of seismic multi-attribute analysis in this study. This technique involves semi-quantitatively determining the distribution characteristics of sedimentary systems in the study area based on seismic multi-attribute analysis combined with paleogeomorphological analysis. However, a drawback of this technique is that it does not consider the poor quality of seismic data.

[0007] With the advancement of science and technology and the continuous improvement of analytical techniques, the interdisciplinary integration has provided numerous conveniences and new methods for the study of sedimentary systems. For example, the Markov chain model analysis method, based on probabilistic models of random events, is increasingly widely used in geology and has achieved excellent results. However, these results are limited to vertical lithology simulation and sedimentary sequences of sedimentary strata. For instance, pages 21-30 of Volume 32, Issue 1 of the journal *Geology and Resources*, retrieved from the CNKI database, present a technique for identifying the sedimentary characteristics of the Elitu Formation in Zhenglan Banner, Inner Mongolia, using Markov chain analysis. This technique involves identifying the lithological characteristics of each layer of the Elitu Formation in the Elitu Pasture through field geological profile measurements, analyzing the sedimentary cycles of the strata below the profile using Markov chain analysis, and further analyzing the sedimentary environment by integrating basic geological data. However, this technique is limited to the sedimentary sequence of sedimentary strata and differs significantly from the present invention. Currently, researchers both domestically and internationally lack a clear technology for quantitatively predicting the source supply capacity of conglomerate bodies based on Markov chains, which brings many inconveniences to oil and gas exploration work.

[0008] Given the aforementioned problems, existing technologies cannot address the issue of the distribution range of sedimentary systems in areas with few or no wells and poor seismic data response. To address this, we have developed a quantitative prediction method for the source supply capacity of sandstone and conglomerate bodies based on Markov chains, filling the gap in the current lack of similar research in this field. Summary of the Invention

[0009] The purpose of this invention is to provide a quantitative prediction method for the provenance supply capacity of sandstone and conglomerate bodies based on Markov chains, which is of great significance for Cenozoic exploration.

[0010] The objective of this invention can be achieved through the following technical measures: a quantitative prediction method for the source supply capacity of conglomerate bodies based on Markov chains, which includes:

[0011] Step 1: Understand the basic geological conditions of the study area and interpret the structural diagram of the standard Cenozoic strata.

[0012] Step 2: Determine the prediction object, initial state probability vector, and time series vector, and establish a Markov prediction model;

[0013] Step 3: Apply statistical estimation methods to establish a one-step transition probability matrix;

[0014] Step 4: Apply the one-step transition probability matrix to achieve multiple state probability predictions;

[0015] Step 5: Predict the distribution range of the sedimentary system based on the source supply intensity factor and the mathematical relationship between the advance or retreat of the fan.

[0016] The objective of this invention can also be achieved through the following technical measures:

[0017] In step 1, data acquisition and preparation: understand the basic geological conditions of the study area, collect relevant data on tectonic evolution, become familiar with the tectonic movements and stress directions experienced by the study area, understand the basic geological framework and tectonic stress field of the study area, ensure the rationality of the fault interpretation results, and conduct detailed structural interpretation of the standard layers based on the well logging and logging data of the drilled wells.

[0018] In step 1, based on the logging curves such as sonic transit time and density, a synthetic record is created to calibrate the formation and interpret the formation and faults. During the interpretation process, it is necessary to check whether the interpreted layers and faults are closed.

[0019] In step 1, combining well and seismic data, a paleogeological map of the Tertiary strata in the study area was drawn to understand the bedrock composition and the differences in the distribution of the source system in the source area.

[0020] In step 1, based on the seismic interpretation results, a three-dimensional color map of the top surface of the pre-Tertiary bedrock in the study area is drawn as the basis for paleogeomorphological analysis. Based on the three-dimensional color map of the top surface of the pre-Tertiary bedrock, the source area and sedimentary area are macroscopically distinguished.

[0021] In step 2, the formula for the Markov chain prediction model is:

[0022] μ n+1 =μ n P,---(1)

[0023] (1) In the formula, μ n Let P be the probability vector of the state of the object at time n, and let P be the one-step transition matrix. n+1 Let μ2 be the probability vector of the state of the object at time n+1, i.e., the prediction result; since μ2 = μ1P, we can derive the following:

[0024] μ n+1 =μ1P n+1 -----(2)

[0025] (2) In the formula, μ1 is the initial state vector of the predicted object, and P is the one-step transition probability matrix.

[0026] In step 2, based on the collected thin section data of rock samples from all core wells in the study area, the initial depositional period is used as the time vector of the prediction object of the Markov model, and the weighted average of the percentage contents of quartz, feldspar, igneous rocks, metamorphic rocks, and sedimentary rock fragments is used as the initial state probability vector of the prediction object, i.e., μ1 = [A% B% C% D% E%]; where A% is the weighted average percentage contents of quartz; B% is the weighted average percentage contents of feldspar; C% is the weighted average percentage contents of igneous rock fragments; D% is the weighted average percentage contents of igneous rock fragments; and E% is the weighted average percentage contents of sedimentary rock fragments.

[0027] In step 2, the method for calculating the weighted average is to multiply each data point by its corresponding weight, then add all the products together, and finally divide by the sum of all weights. The specific calculation formula is as follows: Weighted average = Σ(data_i × weight_i) / Σ weight_i, where Σ represents summation, data_i represents the i-th data point, and weight_i represents the weight of the i-th data point.

[0028] In step 3, assume the object to be predicted has Xi (i = 1, 2, ..., n) states, the total number of known states Xi is ai, and the number of transitions from state Xi to Xj through investigation is a. ij , Then the transition frequency from state Xi to Xj is According to probability theory, when the theoretical distribution of state probabilities is unknown, if the sample size is large enough, the theoretical distribution of states can be approximated by the sample distribution. Therefore, for unknown transition probabilities, the transition frequency can be used to approximate the transition probability. Thus, the estimated value of the transition probability from state Xi to Xj is... Thus, by arranging the probabilities of transitions between each state in a table, we obtain an estimate of the one-step transition probability matrix:

[0029]

[0030] The I-th row is the state Xi transition probability vector; each row has the characteristics of satisfying the transition probability, namely: (1) the row sum is 1, and (2) the elements are non-negative.

[0031] In step 4, the Markov chain prediction model refers to establishing the initial transition probability matrix by using the transition probabilities between states in the past time series; to obtain t n The probability of being in each state at time t0 can be determined by knowing the state at time t0 and using the CK equation through n state transitions; thus, prediction can be made using equation (1) or (2); to predict t n+1 The percentage content of quartz, feldspar, igneous rocks, metamorphic rocks, and sedimentary rock fragments of a given period needs to be calculated from the one-step transition matrix to obtain the n-step transition probability matrix, i.e., P(n) = Pn Thus, we can derive μ4 = μ3P = μ1P 3 The percentage content of quartz, feldspar, igneous rock, metamorphic rock, and sedimentary rock fragments in period t4 can be directly calculated.

[0032] In step 5, based on the variation patterns of rock fragments and quartz grains in sandstone at different periods, the provenance can be traced and the differences in different provenance systems can be determined. The main types of rock fragments in sandstone are igneous rocks, metamorphic rocks, and sedimentary rocks. Carbonate rocks are the main type of sedimentary rock fragments, with the parent rocks mainly being Paleozoic marine and marine-continental transitional sediments. Volcanic clastic rock fragments indicate that the parent rocks are Mesozoic terrestrial volcanic clastic sediments. Metamorphic rock fragments indicate that the parent rocks are Archean metamorphic basement rocks. Quartz grains indicate that the parent rocks are Archean metamorphic basement rocks or intrusive rocks. Through the above analysis, based on the variation patterns of rock fragments and quartz grains in sandstone at different periods, different provenance systems can be traced and the differences in different provenance systems can be determined.

[0033] In step 5, the quantitative prediction results of the source supply capacity of the conglomerate body were analyzed: Quartz has very stable physical and chemical properties, and a high quartz content indicates a far distance from the source area; Feldspar has poor stability, with a lower content further away from the source area and a higher content closer to the source area; The ratio of the percentage content of stable mineral quartz to unstable minerals feldspar and rock fragments is called the source supply capacity index, i.e., the source supply capacity index K: the ratio of the probability of stable mineral quartz to unstable mineral feldspar, i.e., K = Pquartz / Pfeldspar, where Pquartz is the percentage content of quartz and Pfeldspar is the percentage content of feldspar; This index can reflect the strength of the source supply capacity; if the source supply capacity index is small, it reflects a strong source supply capacity; if the source supply capacity index is large, it reflects a weak source supply capacity.

[0034] In step 5, for the sandstone and conglomerate mass of the gentle slope zone, when the material supply index K is in the range of 1.1-2.3, the material supply capacity is relatively strong; when the material supply index K is in the range of 2.4-8, the material supply capacity is relatively weak.

[0035] The objective of this invention can also be achieved through the following technical measures: a quantitative prediction system for the source supply capacity of sand and conglomerate bodies based on Markov chains. This system uses a quantitative prediction method for the source supply capacity of sand and conglomerate bodies based on Markov chains to predict the source supply capacity based on Markov models and the mathematical relationship between fan advance or retreat, and to predict the distribution range of sedimentary systems.

[0036] This invention presents a quantitative prediction method for the provenance supply capacity of conglomerate bodies based on Markov chains. The method involves sedimentary facies analysis, firstly to understand the basic geological conditions of the study area; secondly, a Markov chain model prediction method is proposed; thirdly, the provenance supply capacity of the conglomerate source area is quantitatively predicted, and the prediction results are analyzed; finally, based on the mathematical relationship between the provenance supply intensity factor K and the fan advance (retreat), the mathematical relationship between fan advance (retreat) is predicted, solving the problem of the distribution range of sedimentary systems in areas with few or no wells and poor seismic data response. Compared with the analysis results of actual drilling data, the prediction results have higher accuracy. This method has high practical value and strong operability.

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

[0038] 1. Taking quartz, feldspar, igneous rocks, metamorphic rocks and sedimentary rock fragments as research objects, a Markov prediction model was established with the weighted average of rock fragment percentage content as the initial state probability vector and the initial deposition period as the initial time vector. This model has high practical value.

[0039] 2. By applying statistical estimation methods, the transition probability is approximated by the transition frequency for unknown transition probabilities, and a reliable one-step transition probability matrix is ​​quickly established.

[0040] 3. By applying a one-step transition probability matrix to calculate multiple state probabilities, and then using an n-step transition probability matrix, the percentage content of rock fragments at any given time can be directly obtained. This enables quantitative prediction of the source supply capacity of sandstone and conglomerate masses, and the prediction results are unaffected by the quality of seismic data.

[0041] 4. A mathematical relationship was established between the source supply intensity factor K and the fan advance (retreat), solving the problem of the distribution range of sedimentary systems in areas with few or no wells and poor seismic data response. Attached Figure Description

[0042] Figure 1 This is a flowchart of a specific embodiment 1 of the method for quantitative prediction of the source supply capacity of sandstone and conglomerate bodies based on Markov chains according to the present invention;

[0043] Figure 2 This is a cross-sectional view of the north-south trending sedimentary interconnected well Chen 42-Ken 118 in specific embodiment 2 of the present invention;

[0044] Figure 3 This is a plan view of the sedimentary facies of the middle section of the northern slope of the Chenjiazhuang Uplift, specifically in Embodiment 2 of the present invention, namely, the upper and lower sedimentary facies of the Sha-3-12.

[0045] Figure 4 This is a graph showing the functional relationship between the material supply intensity factor K and the fan body advance (retreat) in a specific embodiment 2 of the present invention. Detailed Implementation

[0046] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, and / or combinations thereof.

[0048] The quantitative prediction method for the source supply capacity of sandstone and conglomerate masses based on Markov chains of the present invention includes the following steps:

[0049] Step 1: Understand the basic geological conditions of the study area and interpret the structural diagram of the standard Cenozoic strata.

[0050] Step 2: Determine the prediction object, initial state probability vector, and time series vector, and establish a Markov prediction model;

[0051] Step 3: Apply statistical estimation methods to establish a one-step transition probability matrix;

[0052] Step 4: Apply the one-step transition probability matrix to achieve multiple state probability predictions;

[0053] Step 5: Predict the distribution range of the sedimentary system based on the source supply intensity factor and the mathematical relationship between the advance or retreat of the fan.

[0054] This invention identifies the prediction object, initial state probability, and time series vector, establishing a Markov prediction model. Statistical estimation methods are applied to establish a one-step transition probability matrix, achieving the most crucial step in Markov model prediction. Using the one-step transition probability matrix, multiple state probability matrices are calculated, enabling quantitative prediction of the sediment supply capacity of conglomerate bodies. Based on the sediment supply intensity factor K, a mathematical relationship is established between K and sector advance (retreat), resolving the issue of the distribution range of sedimentary systems in areas with few or no wells and poor seismic data response.

[0055] The following are several specific embodiments of the application of the present invention.

[0056] Example 1

[0057] In a specific embodiment of the present invention, the method for quantitatively predicting the source supply capacity of conglomerate bodies based on Markov chains includes:

[0058] Step 1: Data Acquisition and Preparation. Understand the basic geological conditions of the study area; collect relevant data on tectonic evolution, understand the tectonic movements and stress directions experienced by the study area, and ensure the rationality of the tectonic interpretation results. Understand the basic geological conditions, tectonic movements, and regional stress directions of the study area, and conduct detailed tectonic interpretation of standard layers based on well logging and well logging data from drilled wells.

[0059] Based on well logging curves such as sonic transit time and density, synthetic records are created to calibrate the formation, and formation and fault interpretation is performed. During the interpretation process, it is necessary to check whether the interpreted layers and faults are closed.

[0060] By combining well and seismic data, a paleogeological map of the Tertiary strata in the study area was drawn to understand the bedrock composition and the differences in the distribution of the provenance system in the source areas.

[0061] Based on the seismic interpretation results, a three-dimensional color map of the top surface of the pre-Tertiary bedrock in the study area was drawn as a basis for paleogeomorphological analysis. Based on the three-dimensional color map of the top surface of the pre-Tertiary bedrock, the source area and sedimentary area were distinguished macroscopically.

[0062] Step 2: Determine the prediction object, initial state probability vector, and time series vector, and establish a Markov prediction model.

[0063] The formula for the Markov chain prediction model is:

[0064] μ n+1 =μ n P,---(1).

[0065] (1) In the formula, μ n Let P be the probability vector of the state of the object at time n, and let P be the one-step transition matrix. n+1 Let μ2 be the probability vector of the state of the object at time n+1, i.e., the prediction result. Since μ2 = μ1P, we can derive the following:

[0066] μ n+1 =μ1P n+1 -----(2).

[0067] (2) In the formula, μ1 is the initial state vector of the predicted object, and P is the one-step transition probability matrix.

[0068] Based on the collected thin section data of rock samples from all core wells in the study area, the initial depositional period was used as the time vector of the prediction object in the Markov model, and the weighted average of the percentage contents of quartz, feldspar, igneous rocks, metamorphic rocks, and sedimentary rock fragments was used as the initial state probability vector of the prediction object, i.e., μ1 = [A% B% C% D% E%]. Where A% is the weighted average percentage content of quartz; B% is the weighted average percentage content of feldspar; C% is the weighted average percentage content of igneous rock fragments; D% is the weighted average percentage content of igneous rock fragments; and E% is the weighted average percentage content of sedimentary rock fragments.

[0069] The method for calculating the weighted average is to multiply each data point by its corresponding weight, then sum all the products, and finally divide by the sum of all weights. The specific calculation formula is as follows: Weighted average = Σ(data_i × weight_i) / Σweight_i, where Σ represents summation, data_i represents the i-th data point, and weight_i represents the weight of the i-th data point.

[0070] Step 3: Apply statistical estimation methods to establish a one-step transition probability matrix.

[0071] Suppose the object to be predicted has Xi (i = 1, 2, ..., n) states, and the total number of known states Xi is ai. The number of transitions from state Xi to Xj through investigation is a. ij , (i,j=1,2,…,n), then the transition frequency from state Xi to Xj is As we know from probability theory, when the theoretical distribution of state probabilities is unknown, if the sample size is large enough, the theoretical distribution of states can be approximated by the sample distribution. Therefore, for unknown transition probabilities, the transition frequency can be used to approximate the transition probability. Thus, the estimated value of the transition probability from state Xi to Xj is... In this way, by arranging the probabilities of the transitions between each state into a table, we obtain an estimate of the one-step transition probability matrix.

[0072]

[0073] The I-th row is the state Xi transition probability vector. Each row has the characteristics of satisfying the transition probability. That is: (1) the row sum is 1. (2) the elements are non-negative.

[0074] Step 4: Apply the one-step transition probability matrix to achieve multiple state probability predictions.

[0075] Markov chain prediction models refer to establishing an initial transition probability matrix by using the transition probabilities between states in a past time series. To obtain t... nThe probability of being in each state at time t0 can be determined by knowing the state at time t0 and using the CK equation through n state transitions. This allows prediction using equation (1) or (2). Generally, to predict t... n+1 The percentage content of quartz, feldspar, igneous rocks, metamorphic rocks, and sedimentary rock fragments of a given period needs to be calculated from the one-step transition matrix to obtain the n-step transition probability matrix, i.e., P(n) = P n Thus, we can derive μ4 = μ3P = μ1P 3 The percentage content of quartz, feldspar, igneous rock, metamorphic rock, and sedimentary rock fragments in period t4 can be directly calculated.

[0076] Step 5: Predict the distribution range of the sedimentary system based on the mathematical relationship between the source supply intensity factor and the fan advance (retreat).

[0077] Based on the variation patterns of rock fragment and quartz grain content in sandstone from different periods, the provenance can be traced and the differences in variations between different provenance systems can be determined. The main types of rock fragments in sandstone are igneous rocks, metamorphic rocks, and sedimentary rocks. Carbonate rocks are the main type of sedimentary rock fragments, with their parent rocks primarily being Paleozoic marine and marine-continental transitional sediments. Volcanic clastic rock fragments indicate Mesozoic terrestrial volcanic clastic sediments as parent rocks. Metamorphic rock fragments indicate Archean metamorphic basement rocks. Quartz grains indicate Archean metamorphic basement rocks or intrusive rocks. Through the above analysis, the variation patterns of rock fragment and quartz grain content in sandstone from different periods can be used to trace different provenance systems and determine the differences in variations between different provenance systems.

[0078] Quantitative prediction results of the source supply capacity of sandstone and conglomerate bodies were analyzed. Quartz exhibits highly stable physical and chemical properties; high quartz content indicates a distant source area. Feldspar is less stable; its content decreases with increasing distance from the source area. The ratio of the percentage content of stable mineral quartz to unstable minerals feldspar and rock fragments is called the source supply capacity index, which reflects the strength of the source supply capacity. A small source supply capacity index indicates a strong source supply capacity, while a large index indicates a weak source supply capacity.

[0079] For conglomerate masses on gentle slopes, a source supply intensity factor K of 1.1–2.3 indicates a strong source supply capacity, while a source supply intensity factor K of 2.4–8 indicates a weak source supply capacity. Statistical results show that the source supply intensity factor K and the advance or retreat distance of the fan body satisfy y = 6130x. -0.847Taking the Kenxie 125 well as an example, the Sha-3 Lower 13 sandstone group is a deltaic plain subfacies with a sediment supply intensity factor of 1.71 and a fan advance distance of approximately 3500m. It transitions to a delta front subfacies in the Sha-3 Lower 12 sandstone group, with a sediment supply intensity factor of 2.33 and a fan advance distance of approximately 3200m. Therefore, in the Sha-3 Lower 12 sandstone group, the fan actually retreated by 300m. Figure 2 , 3 4).

[0080] Example 2

[0081] In a specific embodiment 2 of the present invention, the Sanhecun Depression is located on the southern slope of the Zhanhua Depression in the Jiyang Depression. After years of comprehensive oil and gas exploration, abundant oil and gas resources have been discovered in the lower section of the Neogene Guantao Formation and the Paleogene Shahejie Formation in the Sanhecun Depression of the Zhanhua Depression. The lower section of the Shahejie Formation (Shahejie 3) is an important reservoir with great exploration potential. There are no reports on the provenance system of the lower section of the Shahejie Formation in the Sanhecun Depression, both domestically and internationally. With technological innovation, provenance analysis has gradually shifted from initial qualitative research to quantitative research. In recent years, the Markov chain model, a probabilistic model based on random events, has been increasingly widely used in geology and has achieved excellent results. Therefore, it is necessary to conduct in-depth research on the quantitative prediction of the provenance supply capacity of the sandstone and conglomerate body in the gentle slope zone of the lower section of the Shahejie Formation in the Sanhecun Depression based on the Markov chain model.

[0082] like Figure 1 This is a flowchart illustrating the quantitative prediction of the source supply capacity of sandstone and conglomerate bodies based on Markov chains, as presented in this invention.

[0083] In this embodiment, the quantitative prediction of the source supply capacity of sandstone and conglomerate bodies based on Markov chains of the present invention includes the following steps:

[0084] In step 1, data acquisition and preparation

[0085] Understanding the basic geological conditions of the study area, collecting relevant data on tectonic evolution, and becoming familiar with the tectonic movements and stress directions experienced by the study area are the main purposes of this step. The goal is to gain an understanding of the basic geological framework and tectonic stress field of the study area, and to ensure the rationality of the fault interpretation results.

[0086] Chen Shuping (2021) argues that during the deposition of the Kongdian Formation to the fourth member of the Shahejie Formation, the Jiyang Depression was in a tensional-creeping environment with multi-directional fault activity (including NW, NEC, NE, and EST). During the deposition of the third member of the Shahejie Formation, right-lateral strike-slip activity began, and the NEC-trending right-lateral torsional-tensional stress intensified, resulting in a NEC-trending overall structural pattern in the Sanhecun Depression. Right-lateral strike-slip activity occurred again during the deposition of the first member of the Shahejie Formation to the Dongying Formation. Since the Guantao Formation, the depression has been in a period of stagnation and is still under strike-slip stress, with the principal stress direction being near EST.

[0087] In step 1, the structural diagram of the standard Cenozoic stratigraphic layer is interpreted. Based on the logging data from the drilled wells, the strata and faults are interpreted according to the conventional stratigraphic interpretation process. This step is the basis of this invention.

[0088] The specific interpretation steps are as follows: import AC (acoustic transit time), Den (density), and other curves into the seismic interpretation software—synthetic record calibration of strata—strata and fault interpretation. During the interpretation process, it is necessary to check whether the interpreted horizons and faults are closed.

[0089] In step 1, a combined well-seismic and seismic study was conducted to draw a paleogeological map of the Tertiary strata on the northern slope of the Chenjiazhuang Uplift, in order to understand the bedrock composition and the differences in the distribution of the source system in the source area.

[0090] Based on the standard stratigraphic layers interpreted from the seismic data and the information from wells that have encountered the basement, a paleogeological map of the Tertiary strata on the northern slope of the Chenjiazhuang Uplift was drawn. Located between the Zhanhua and Dongying Depressions, the northern slope of the Chenjiazhuang Uplift serves as a source area for the Sanhecun Depression. Its bedrock, composed of Archean, Paleozoic, and Mesozoic strata, forms a sloping basement that slopes from south to north, with the top surface generally trending downwards from south to north and from west to east. A fault gully develops between the eastern and western uplifts, dividing the Chenjiazhuang Uplift into its western and eastern segments. Due to differences in bedrock composition and planar distribution, the two major source systems of the western and eastern segments of the Chenjiazhuang Uplift exhibit significant differences. Specifically, the bedrock in the southwestern source area of ​​the western segment of the Chenjiazhuang Uplift has relatively weak resistance to degradation, exhibiting a peneplain-like landform; while the bedrock in the southern source area of ​​the eastern segment of the Chenjiazhuang Uplift has strong resistance to degradation and erosion, exhibiting a gully-ridge landform.

[0091] In step 1, based on the seismic interpretation results, three-dimensional color images of the top surface of the Tertiary bedrock before the Chenjiazhuang Uplift and the top surface of the Tertiary bedrock before the Sanhecun Depression are drawn as the basis for paleogeomorphological analysis, and the source area and sedimentary area are distinguished macroscopically.

[0092] Using seismic interpretation data from the Kenxi area, a three-dimensional color map of the top surface of the pre-Tertiary bedrock in the Sanhecun Depression was created using Petrel software, serving as the basis for paleogeomorphological analysis. The three-dimensional color map clearly identifies the positive paleogeomorphic units (paleo-uplifts, paleo-bulges), negative paleogeomorphic units (gullies, channels), and sedimentary zones in the Sanhecun Depression. The southern uplift on the northern slope of the Chenjiazhuang Uplift serves as the source area, while the negative paleogeomorphic units act as channels for sediment transport, bridging the source and sedimentary areas.

[0093] In step 2, the prediction object, initial state probability vector, and time series vector are determined, and a Markov prediction model is established.

[0094] μn+1=μnP,---(1). In equation (1), μn is the state probability vector of the predicted object at time n, P is the one-step transition matrix, and μn+1 is the state probability vector of the predicted object at time n+1, i.e., the prediction result. Since μ2=μ1P, it is derived that: μn+1=μ1Pn+1-----(2). In equation (2), μ1 is the initial state vector of the predicted object, and P is the one-step transition probability matrix.

[0095] In step 2, μ1 is used to determine the initial state vector and time series vector of the object to be predicted. This step is the core of the Markov chain model prediction method.

[0096] Let N be the set of positive integers, and the time series be Sand Group 13, Lower Sand Group 12, Upper Sand Group 12, Sand Group 9-11, ... For a given depositional period, there are 5 possible objects to be predicted, with a state space I = {1, 2, 3, 4, 5}. Let Xn = 1, Xn = 2, Xn = 3, Xn = 4, Xn = 5 represent the weighted average percentage content of quartz, feldspar, igneous rocks, metamorphic rocks, and sedimentary rocks in the nth depositional period, respectively.

[0097] Based on the research results of the Sha-3 Lower Series in the Zhanhua Depression, and combined with regional seismic and rock electrical characteristics, the Sha-3 Lower Series is divided into five sand groups: 9, 10, 11, 12, and 13. Sand group 12 is further divided into two sand groups: Upper 12 and Lower 12.

[0098] Thin section data were collected from 16 core wells in the Kenxi area of ​​the Sha-3 formation, totaling 120 sampling points. Thin section data were collected from 5 wells during the depositional period of the 13th Sandstone Formation, totaling 40 sampling points; from 5 wells during the depositional period of the 12th Lower Sandstone Formation, totaling 28 sampling points; from 2 wells during the depositional period of the 12th Upper Sandstone Formation, totaling 8 sampling points; from 1 well during the depositional period of the 10th Sandstone Formation, totaling 2 sampling points; and from 1 well during the depositional period of the 8th Sandstone Formation, totaling 23 sampling points.

[0099] The sedimentary sequence is (13 Sand Formation – Lower 12 Sand Formation – Upper 12 Sand Formation – (9-11 Sand Formation)). The percentage content of quartz, feldspar, igneous rocks, metamorphic rocks, and sedimentary rocks in each period is statistically analyzed. A weighted average is then calculated for the percentage content of quartz, feldspar, igneous rocks, metamorphic rocks, and sedimentary rocks at each sampling point for each period. The weighted average is calculated by multiplying each data point by its corresponding weight, summing all products, and finally dividing by the sum of all weights. The specific formula is as follows: Weighted average = Σ(data i × weight i) / Σweight i, where Σ represents summation, data i represents the i-th data point, and weight i represents the weight of the i-th data point.

[0100] During the Sha-3 Lower 13 Sand Formation period, rock thin section data were collected from 5 wells, with a total of 40 sampling points (Table 1).

[0101] Table 1. Statistical table of thin section data of the Sha-3-13 sandstone group in the Sanhecun depression.

[0102]

[0103] Using the Sha-3 Lower 13 Sandstone Formation period as the initial time vector of the Markov model, and the weighted average of the percentage contents of rock fragments (quartz, feldspar, igneous rocks, metamorphic rocks, and sedimentary rocks) as the initial state probability vector, i.e., μ1 = [A% B% C% D% E%]. Where A% is the weighted average percentage content of quartz; B% is the weighted average percentage content of feldspar; C% is the weighted average percentage content of igneous rock fragments; D% is the weighted average percentage content of igneous rock fragments; and E% is the weighted average percentage content of sedimentary rock fragments (hereinafter referred to as A, B, C, D, and E, respectively). The calculated initial state vector for the Sha-3 Lower 13 Sandstone Formation period is μ1 = [29% 17% 11% 3% 40%] (Table 2).

[0104] Table 2 Weighted average percentage content of rock fragments in the Sanhecun depression, Sand Group 3-13.

[0105]

[0106] In step 3, statistical estimation methods are applied to establish the one-step transition probability matrix. This step is crucial for the Markov chain model prediction method.

[0107] The percentage content of quartz, feldspar, igneous rocks, metamorphic rocks, and sedimentary rocks at 120 sampling points from the depositional period of Sandy Formation 13 was investigated, yielding the following data: Of the original 100 sampling points A, 90 remain; of the remaining 10, 2, 2, 3, and 3 have shifted to B, C, D, and E, respectively. Of the original 100 sampling points B, 80 remain; of the remaining 20, 10, 2, 3, and 5 have shifted to A, C, D, and E, respectively. Of the original 100 sampling points C, 80 remain; of the remaining 20, 12, 1, 3, and 4 have shifted to A, B, D, and E, respectively. Of the original 100 sampling points D, 70 remain; of the remaining 30, 15, 2, 5, and 8 have shifted to A, B, C, and E, respectively. Of the original 100 sampling points in E, 70 sampling points remain, and of the remaining 30, 10, 2, 3, and 15 have been redirected to A, B, C, and D, respectively.

[0108] Statistically analyzing the above data, the one-step transition probability matrix is ​​as follows:

[0109]

[0110] The process proceeds to step 4.

[0111] Step 4: Apply the one-step transition probability matrix to achieve multiple state probability predictions.

[0112] In step 4, the percentage content of quartz and rock fragments during the depositional period of the Lower Sand Formation 12 is predicted.

[0113] Given the initial state vector μ1 = [0.29 0.17 0.11 0.03 0.40]

[0114]

[0115] = [0.28 0.12 0.10 0.15 0.35], meaning that after one transition, the probabilities of A, B, C, D, and E will become 0.28, 0.12, 0.10, 0.15, and 0.35, respectively.

[0116] In step 4, the probability of the percentage content of quartz and rock fragments during the deposition period of the Upper Sand Formation 12 is predicted.

[0117] The probability vector of the upper sand group 12 is μ3=μ2P=μ1P2, which is calculated as follows:

[0118]

[0119]

[0120] That is, after two transitions, the probabilities of A, B, C, D, and E will become 0.60, 0.073, 0.059, 0.16, and 0.108, respectively.

[0121] In step 4, the probability of the percentage content of quartz and rock fragments during the deposition period of Sand Group 10 is predicted.

[0122] The transition probability of sand group 10 is μ4=μ3P=μ1P3, calculated as follows:

[0123]

[0124] That is, after three transitions, the probabilities of A, B, C, D, and E will become 0.50, 0.31, 0.05, 0.075, and 0.065, respectively.

[0125] In step 4, the probability of the percentage content of quartz and rock fragments during the deposition period of Sand Group 8 is predicted.

[0126] The transition probability of sand group 8 is μ5=μ4P=μ1P4, calculated as follows:

[0127]

[0128] The prediction results (Table 3) show that if the probabilities of A, B, C, D, and E change according to the above-mentioned pattern, the percentage of sedimentary rocks and igneous rocks in the middle 8th sandstone group of Sha-3 gradually decreases, while the percentage of metamorphic rocks gradually increases. This indicates that the early stage of Sha-3 was dominated by Paleozoic marine sediments, and in the late stage it gradually changed to Archean metamorphic rock basement superimposed with Paleozoic marine sediments.

[0129] Table 3. Prediction Results of Markov Chain Model

[0130] Layering quartz(%) Feldspar (%) Igneous rocks (%) Metamorphic rocks (%) Sedimentary rocks (%) Material supply index 13 29 17 11 3 40 1.71 12th 28 12 10 15 35 2.33 12 60 7.3 5.9 16 10.8 8.22 10 50 31 5 7.5 3.5 1.61 8 36.7 31.5 4 23.3 3 1.17

[0131] Step 5: Predict the distribution range of the sedimentary system based on the source supply intensity factor K.

[0132] Material supply intensity factor K: The ratio of the probability of stable mineral quartz to unstable mineral feldspar. That is, K = Pquartz / Pfeldspar. This index reflects the strength of material supply capacity; a smaller index indicates a stronger supply capacity, while a larger index indicates a weaker supply capacity. Where Pquartz is the percentage content of quartz, and Pfeldspar is the percentage content of feldspar.

[0133] The intensity of sediment supply gradually increases from the 13th sand group to the upper 12th sand group, indicating that the sediment supply capacity gradually weakens. The intensity of sediment supply suddenly decreases in the 10th sand group below the third sand group, indicating that the sediment supply capacity was strengthened during the 10th sand group period.

[0134] Table 4. Statistical table of weighted average percentage content of rock cuttings

[0135]

[0136] Comparing the provenance supply intensity obtained by weighting the percentage content of rock fragments from actual statistics (Table 4) with the provenance supply intensity predicted by the Markov chain model (Table 3), the predicted provenance supply intensity of the lower 12, upper 12, 10, and 8 sandstone groups are 2.33, 8.22, 1.61, and 1.77, respectively. Compared with the actual data, the relative errors are 4%, 11%, 15%, and 3%, respectively.

[0137] Table 5. Relative Errors Between Predicted and Actual Data for Rock Cuttings Percentage Content

[0138] Layering quartz(%) Feldspar (%) Igneous rocks (%) Metamorphic rocks (%) Sedimentary rocks (%) 12th 3 7.6 3.9 13.3 1.1 12 16 6.8 12 6.2 3.5 10 4 3.2 3.8 8.5 15.7 8 4.6 2.8 5 3.2 21.3

[0139] The relative errors of the rock fragment percentages obtained by weighted average of the actual rock fragment percentages (Table 4) and the rock fragment percentages predicted by the Markov chain model (Table 5) were compared with the actual data. The relative errors of the predicted rock fragment percentages for the lower 12, upper 12, 10, and 8 sandstone groups were between 1.1% and 21.3%.

[0140] For conglomerate masses on gentle slopes, a sediment supply intensity K of 1.1-2.3 indicates a relatively strong sediment supply capacity, while a K of 2.4-8 indicates a relatively weak sediment supply capacity. Statistical results show that the sediment supply intensity factor K and the advance or retreat distance of the fan body satisfy the equation y = 6130x - 0.847 ( Figure 4 ), where y is the fan advance or retreat distance, and K is the source supply intensity factor. On the sedimentary facies plan of the upper and lower 12 of the Sha-3-12 formation in the middle section of the northern slope of the Chenjiazhuang Uplift, taking the Kenxie 125 well as an example, the Sha-3-13 sand group is a deltaic plain subfacies with a source supply intensity factor of 1.71 and a fan advance distance of about 3500m. It transitions to the delta front subfacies of the lower 12 of the Sha-3-12 formation with a source supply intensity factor of 2.33 and a fan advance distance of about 3200m. Therefore, in the lower 12 of the Sha-3-12 sand group, the fan actually retreated by 300m. Figure 2 , 3 4).

[0141] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is 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.

[0142] Except for the technical features described in the specification, all other technologies are known to those skilled in the art.

Claims

1. A quantitative prediction method for the source supply capacity of sandstone and conglomerate masses based on Markov chains, characterized in that, The quantitative prediction method for the provenance supply capacity of conglomerate bodies based on Markov chains includes: Step 1: Understand the basic geological conditions of the study area and interpret the structural diagram of the standard Cenozoic strata. Step 2: Determine the prediction object, initial state probability vector, and time series vector, and establish a Markov prediction model; Step 3: Apply statistical estimation methods to establish a one-step transition probability matrix; Step 4: Apply the one-step transition probability matrix to achieve multiple state probability predictions; Step 5: Based on the source supply intensity factor and the mathematical relationship between fan advance or retreat, predict the distribution range of the sedimentary system.

2. The method for quantitatively predicting the source supply capacity of sandstone and conglomerate bodies based on Markov chains according to claim 1, characterized in that, In step 1, we will understand the basic geological conditions of the study area, collect relevant data on tectonic evolution, familiarize ourselves with the tectonic movements and stress directions experienced by the study area, gain an understanding of the basic geological framework and tectonic stress field of the study area, ensure the rationality of the fault interpretation results, and conduct a detailed structural interpretation of the standard layer based on the well logging and logging data of the drilled wells.

3. The method for quantitatively predicting the source supply capacity of sandstone and conglomerate bodies based on Markov chains according to claim 2, characterized in that, In step 1, based on the logging curves such as sonic transit time and density, a synthetic record is created to calibrate the formation and interpret the formation and faults. During the interpretation process, it is necessary to check whether the interpreted layers and faults are closed.

4. The method for quantitatively predicting the source supply capacity of sandstone and conglomerate masses based on Markov chains according to claim 3, characterized in that, In step 1, combining well and seismic data, a paleogeological map of the Tertiary strata in the study area was drawn to understand the bedrock composition and the differences in the distribution of the source system in the source area.

5. The method for quantitatively predicting the source supply capacity of sandstone and conglomerate bodies based on Markov chains according to claim 4, characterized in that, In step 1, based on the seismic interpretation results, a three-dimensional color map of the top surface of the pre-Tertiary bedrock in the study area is drawn as the basis for paleogeomorphological analysis. Based on the three-dimensional color map of the top surface of the pre-Tertiary bedrock, the source area and sedimentary area are macroscopically distinguished.

6. The method for quantitatively predicting the source supply capacity of sandstone and conglomerate masses based on Markov chains according to claim 1, characterized in that, In step 2, the formula for the Markov chain prediction model is: m n+1 =μ n P,---(1) (1) In the formula, μ n Let P be the probability vector of the state of the object at time n, and let P be the one-step transition matrix. n+1 Let μ2 be the probability vector of the state of the object at time n+1, i.e., the prediction result; since μ2 = μ1P, we can derive the following: μ n+1 =μ1P n+1 -----(2) (2) In the formula, μ1 is the initial state vector of the predicted object, and P is the one-step transition probability matrix.

7. The method for quantitatively predicting the source supply capacity of sandstone and conglomerate masses based on Markov chains according to claim 6, characterized in that, In step 2, based on the collected thin section data of rock samples from all core wells in the study area, the initial depositional period is used as the time vector of the prediction object of the Markov model, and the weighted average of the percentage contents of quartz, feldspar, igneous rocks, metamorphic rocks, and sedimentary rock fragments is used as the initial state probability vector of the prediction object, i.e., μ1 = [A% B% C% D% E%]; where A% is the weighted average percentage contents of quartz; B% is the weighted average percentage contents of feldspar; C% is the weighted average percentage contents of igneous rock fragments; D% is the weighted average percentage contents of igneous rock fragments; and E% is the weighted average percentage contents of sedimentary rock fragments.

8. The method for quantitatively predicting the source supply capacity of sandstone and conglomerate masses based on Markov chains according to claim 7, characterized in that, In step 2, the method for calculating the weighted average is to multiply each data point by its corresponding weight, then add all the products together, and finally divide by the sum of all weights. The specific calculation formula is as follows: Weighted average = Σ(data_i × weight_i) / Σ weight_i, where Σ represents summation, data_i represents the i-th data point, and weight_i represents the weight of the i-th data point.

9. The method for quantitatively predicting the source supply capacity of sandstone and conglomerate masses based on Markov chains according to claim 8, characterized in that, In step 3, assume the object to be predicted has Xi (i = 1, 2, ..., n) states, the total number of known states Xi is ai, and the number of transitions from state Xi to Xj through investigation is a. ij , Then the transition frequency from state Xi to Xj is According to probability theory, when the theoretical distribution of state probabilities is unknown, if the sample size is large enough, the theoretical distribution of states can be approximated by the sample distribution. Therefore, for unknown transition probabilities, the transition frequency can be used to approximate the transition probability. Thus, the estimated value of the transition probability from state Xi to Xj is... Thus, by arranging the probabilities of transitions between each state in a table, we obtain an estimate of the one-step transition probability matrix: The I-th row is the state Xi transition probability vector; each row has the characteristics of satisfying the transition probability, namely: (1) the row sum is 1, and (2) the elements are non-negative.

10. The method for quantitatively predicting the source supply capacity of sandstone and conglomerate masses based on Markov chains according to claim 9, characterized in that, In step 4, the Markov chain prediction model refers to establishing the initial transition probability matrix by using the transition probabilities between states in the past time series; to obtain t n The probability of being in each state at time t0 can be determined by knowing the state at time t0 and using the CK equation through n state transitions; thus, prediction can be made using equation (1) or (2); to predict t n+1 The percentage content of quartz, feldspar, igneous rocks, metamorphic rocks, and sedimentary rock fragments of a given period needs to be calculated from the one-step transition matrix to obtain the n-step transition probability matrix, i.e., P(n) = P n Thus, we can derive μ4 = μ3P = μ1P 3 The percentage content of quartz, feldspar, igneous rock, metamorphic rock, and sedimentary rock fragments in period t4 can be directly calculated.

11. The method for quantitatively predicting the source supply capacity of sandstone and conglomerate bodies based on Markov chains according to claim 1, characterized in that, In step 5, based on the variation patterns of rock fragments and quartz grains in sandstone at different periods, the provenance can be traced and the differences in different provenance systems can be determined. The main types of rock fragments in sandstone are igneous rocks, metamorphic rocks, and sedimentary rocks. Carbonate rocks are the main type of sedimentary rock fragments, with the parent rocks mainly being Paleozoic marine and marine-continental transitional sediments. Volcanic clastic rock fragments indicate that the parent rocks are Mesozoic terrestrial volcanic clastic sediments. Metamorphic rock fragments indicate that the parent rocks are Archean metamorphic basement rocks. Quartz grains indicate that the parent rocks are Archean metamorphic basement rocks or intrusive rocks. Through the above analysis, based on the variation patterns of rock fragments and quartz grains in sandstone at different periods, different provenance systems can be traced and the differences in different provenance systems can be determined.

12. The method for quantitatively predicting the source supply capacity of sandstone and conglomerate bodies based on Markov chains according to claim 11, characterized in that, In step 5, the quantitative prediction results of the source supply capacity of the conglomerate body were analyzed: Quartz has very stable physical and chemical properties, and a high quartz content indicates a far distance from the source area; Feldspar has poor stability, with a lower content further away from the source area and a higher content closer to the source area; The ratio of the percentage content of stable mineral quartz to unstable minerals feldspar and rock fragments is called the source supply capacity index, i.e., the source supply capacity index K: the ratio of the probability of stable mineral quartz to unstable mineral feldspar, i.e., K = Pquartz / Pfeldspar, where Pquartz is the percentage content of quartz and Pfeldspar is the percentage content of feldspar; This index can reflect the strength of the source supply capacity; if the source supply capacity index is small, it reflects a strong source supply capacity; if the source supply capacity index is large, it reflects a weak source supply capacity.

13. The method for quantitatively predicting the source supply capacity of sandstone and conglomerate masses based on Markov chains according to claim 12, characterized in that, In step 5, for the sandstone and conglomerate mass of the gentle slope zone, when the material supply index K is in the range of 1.1-2.3, the material supply capacity is relatively strong; when the material supply index K is in the range of 2.4-8, the material supply capacity is relatively weak.

14. A quantitative prediction system for the source supply capacity of sandstone and conglomerate masses based on Markov chains, characterized in that, The Markov chain-based quantitative prediction system for the source supply capacity of sandstone and conglomerate bodies uses the Markov chain-based quantitative prediction method for the source supply capacity of sandstone and conglomerate bodies as described in any one of claims 1-13. It predicts the source supply capacity based on the Markov model and the mathematical relationship between fan advance or retreat to predict the distribution range of the sedimentary system.

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