A method and system for predicting the success rate of oil and gas exploration based on classified wildcat well statistics

Abstract

The application provides a kind of oil and gas exploration success rate prediction method and system based on classified exploration well statistics, the method comprises the following steps: quantitative evaluation of oil and gas accumulation geological conditions and draw evaluation map;According to the evaluation map, the comprehensive evaluation value of geology is calculated, and is drawn into the comprehensive evaluation map of geology;The comprehensive evaluation value of geology is extracted from the comprehensive evaluation map of geology;According to the comprehensive evaluation value of geology, the exploration success rate is calculated to realize prediction.The application combines the quantitative evaluation results of two-dimensional plane range of oil and gas accumulation with the drilling results of exploration well, quantitatively predicts the exploration risk, realizes quantitative prediction, avoids exploration risk, improves exploration benefit, and provides decision basis for exploration deployment.

Description

Technical Field

[0001] This invention belongs to the field of geological exploration technology, and specifically relates to a method and system for predicting the success rate of oil and gas exploration based on the statistics of classified exploration wells. Background Technology

[0002] Evaluate and predict the success rate of exploration targets, select favorable areas, avoid drilling risks, and provide a basis for decision-making in oil and gas drilling deployment.

[0003] Exploration risk prediction and assessment methods can be summarized into three main categories. The first category, represented by White (1988, 1993), is the genetic method. This method overlays geological control factors that govern hydrocarbon accumulation, such as source, reservoir, caprock, migration, trap, and preservation conditions, onto a geological map to describe favorable areas and geological risks in the spatial distribution of oil and gas resources. This is a conventional method for geological risk assessment. The second category is multivariate statistical methods, such as Kaufman's (1992) point process model, Pan's (1997) geostatistical method, and Harff et al.'s (1992) multivariate statistical method. This type of method uses statistical models to predict the spatial distribution of oil and gas resources based on information about the spatial distribution characteristics of oil and gas wells and non-oil and gas wells revealed by exploration wells. This method emphasizes information from individual wells but neglects other geological information. The third category is comprehensive methods, such as object-oriented stochastic simulation (Gao et al., 2000; Chen et al., 2000) and geological model-based stochastic simulation methods (Chen et al., 2006; Guo et al., 2009). For example, Guo et al. (2006) used an information integration method to integrate geological, geophysical, and exploration engineering information to reflect the characteristics of the spatial distribution of oil and gas resources, and used maps to represent the distribution of oil and gas resources, making the prediction results more intuitive. Hu et al. (2007) proposed a method to predict the spatial distribution of oil and gas resources using multivariate statistics and information processing technology, that is, to integrate information using Mahalanobis distance discrimination, calculate the probability of oil and gas in known samples using Bayes' formula, and establish a template for the probability of oil and gas in different Mahalanobis distance values, and then use the template to predict the spatial distribution of oil and gas resources. Zhu et al. (2018) discussed the evaluation units, variables, and models related to geological risk and favorable assessment, and proposed a data-driven model using Logistic regression. This model integrates existing geoscience information and current exploration results to obtain a quantitative logistic regression relationship between oil and gas occurrence and key geological factors, and is used to predict the probability of oil and gas occurrence.

[0004] However, the above three types of risk prediction and assessment methods have the following shortcomings: The first type, the overlay mapping method based on causal mechanisms, is a qualitative or semi-quantitative assessment; the second type, the prediction method based on statistical analysis, is a purely statistical method; and the third type, the stochastic simulation and information integration method, is a statistical method that combines geological understanding. The first type of method lacks quantitative models or uses relatively simple models. The latter two types of methods are characterized by mature statistical algorithms and have a wide range of applications, but they lack statistical experience data and models based on specific regional geological conditions and drilling results, resulting in poor targeting.

[0005] In summary, existing technologies cannot effectively combine quantitative evaluation results with well drilling results to avoid exploration risks and improve exploration efficiency. Summary of the Invention

[0006] To address the above problems, this invention provides a method and system for predicting oil and gas exploration success rates based on classification well statistics, employing the following technical solution:

[0007] A method for predicting oil and gas exploration success rate based on classification well statistics includes the following steps:

[0008] Quantitatively evaluate the geological conditions for hydrocarbon accumulation and draw evaluation maps;

[0009] Calculate the comprehensive geological evaluation value based on the evaluation map and draw a comprehensive geological evaluation map;

[0010] Extract the comprehensive geological evaluation value of the exploration well from the aforementioned comprehensive geological evaluation map;

[0011] The exploration success rate is calculated based on the comprehensive geological evaluation value of the exploration well, thus enabling prediction.

[0012] Preferably, the geological conditions for hydrocarbon accumulation include single conditions, which are any one or more of hydrocarbon generation conditions, hydrocarbon supply conditions, reservoir conditions, trap conditions, caprock conditions, or preservation conditions.

[0013] Preferably, the quantitative evaluation of oil and gas reservoir geological conditions and the drawing of evaluation maps include: analyzing and quantifying each of the individual conditions to obtain the evaluation value of each individual condition, and drawing the evaluation map of each individual condition based on the evaluation value, wherein the evaluation value is 0-1, including 0 and 1.

[0014] Preferably, the step of calculating the comprehensive geological evaluation value based on the evaluation map includes: multiplying the evaluation values ​​of a single condition at the same coordinate point on the evaluation map to obtain the comprehensive geological evaluation value, wherein the comprehensive geological evaluation value takes the value of 0-1, including 0 and 1.

[0015] Preferably, the calculation of the exploration success rate based on the comprehensive geological evaluation value of the exploration well includes:

[0016] The geological comprehensive evaluation values ​​were then logarithmically converted.

[0017] The number of exploration wells was counted in intervals based on the converted comprehensive geological evaluation values.

[0018] The exploration success rate is calculated based on the number of exploration wells.

[0019] Preferably, the formula for logarithmically converting the comprehensive geological evaluation value is as follows:

[0020] Z t =ln(Z) o +a)+b

[0021] Among them, Z o This is the comprehensive geological evaluation value; Z t Here are the converted comprehensive geological evaluation values; a is the offset value, 0 ≤ a ≤ 1; b is the system translation value, 8 <b<15。

[0022] Preferably, the step of dividing the converted geological comprehensive evaluation value into intervals to count the number of exploration wells includes: determining the maximum and minimum values ​​of the converted geological comprehensive evaluation value, dividing different intervals according to the maximum and minimum values, and counting the number of oil and gas exploration wells and the number of non-oil and gas exploration wells in each interval.

[0023] Preferably, the formula for calculating the exploration success rate based on the number of exploration wells is:

[0024] P(j) = 100 × n(j) / [n(j) + m(j)]

[0025] Where j is the interval number; P(j) is the exploration success rate of the j-th interval; n(j) is the number of exploration wells of the oil and gas well type in the j-th interval; and m(j) is the number of exploration wells of the oil and gas well type in the j-th interval.

[0026] Preferably, it also includes

[0027] Based on the calculated exploration success rate, an exploration success rate map is drawn for visual prediction.

[0028] Preferably, the value of 'a' is 0, and the value of 'b' is 10.

[0029] A system for predicting oil and gas exploration success rate based on classification well statistics, including

[0030] The quantitative module is used to quantitatively evaluate the geological conditions for hydrocarbon accumulation and generate evaluation maps.

[0031] The calculation module is used to calculate the comprehensive geological evaluation value based on the evaluation map and draw it into a comprehensive geological evaluation map;

[0032] The extraction module is used to extract the comprehensive geological evaluation value of the exploration well from the comprehensive geological evaluation map;

[0033] The prediction module is used to calculate the exploration success rate based on the comprehensive geological evaluation value of the exploration well, thereby achieving prediction.

[0034] Preferably, the oil and gas accumulation geological conditions include several single conditions, and the single conditions are any one or more of the following: hydrocarbon generation conditions, hydrocarbon supply conditions, reservoir conditions, trap conditions, caprock conditions, or preservation conditions.

[0035] The quantitative module, used to quantitatively evaluate the geological conditions for hydrocarbon accumulation and draw evaluation maps, includes: analyzing and quantifying each of the individual conditions to obtain the evaluation value of each individual condition, and drawing the evaluation map of each individual condition based on the evaluation value, wherein the evaluation value is 0-1, including 0 and 1.

[0036] Preferably, the calculation module is used to calculate the comprehensive geological evaluation value based on the evaluation map by multiplying the evaluation values ​​of a single condition at the same coordinate point on the evaluation map to obtain the comprehensive geological evaluation value, wherein the comprehensive geological evaluation value takes the value of 0-1, including 0 and 1.

[0037] Preferably, the prediction module includes

[0038] A conversion unit is used to perform a logarithmic conversion on the comprehensive geological evaluation value;

[0039] The partition unit is used to divide the converted comprehensive geological evaluation value into intervals to count the number of exploration wells.

[0040] A calculation unit is used to calculate the exploration success rate based on the number of exploration wells.

[0041] Preferably, the conversion unit uses the following formula to perform a logarithmic conversion on the comprehensive geological evaluation value:

[0042] Z t =ln(Z) o +a)+b

[0043] Among them, Z o This is the comprehensive geological evaluation value; Z t Here are the converted comprehensive geological evaluation values; a is the offset value, 0 ≤ a ≤ 1; b is the system translation value, 8 <b<15。

[0044] Preferably, the partitioning unit, used to count the number of exploration wells in intervals of the converted comprehensive geological evaluation value, includes: determining the maximum and minimum values ​​of the converted comprehensive geological evaluation value, dividing different intervals according to the maximum and minimum values, and counting the number of oil and gas exploration wells and the number of non-oil and gas exploration wells in each interval.

[0045] Preferably, the calculation unit uses the following formula to calculate the exploration success rate based on the number of exploration wells:

[0046] P(j) = 100 × n(j) / [n(j) + m(j)]

[0047] Where j is the interval number; P(j) is the exploration success rate of the j-th interval, %; n(j) is the number of exploration wells of the oil and gas well type in the j-th interval; m(j) is the number of exploration wells of the oil and gas well type in the j-th interval.

[0048] Preferably, the prediction module further includes a visualization unit for drawing an exploration success rate map based on the calculated exploration success rate, and performing visual prediction.

[0049] This invention combines the quantitative evaluation results of hydrocarbon accumulation in a two-dimensional plane with the results of exploration well drilling to quantitatively predict exploration risks, achieve quantitative prediction, avoid exploration risks, improve exploration efficiency, and provide a basis for exploration deployment decisions.

[0050] 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

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart of an oil and gas exploration success rate prediction method based on classification well statistics according to an embodiment of the present invention is shown.

[0053] Figure 2 A quantitative evaluation diagram of the Sangonghe Formation reservoir according to an embodiment of the present invention is shown;

[0054] Figure 3 A quantitative evaluation diagram of the Sangonghe Formation traps according to an embodiment of the present invention is shown;

[0055] Figure 4 A quantitative evaluation diagram of hydrocarbon supply conditions in the Sangonghe Formation according to an embodiment of the present invention is shown;

[0056] Figure 5 A quantitative evaluation diagram of the capping layer and preservation conditions according to an embodiment of the present invention is shown;

[0057] Figure 6 A comprehensive geological evaluation map of the Sangonghe Formation according to an embodiment of the present invention is shown;

[0058] Figure 7 Different Zs are shown according to embodiments of the present invention. t The number of oil and gas wells and the number of non-oil and gas wells corresponding to the interval;

[0059] Figure 8 The success rate of exploration of the Sangonghe Formation according to an embodiment of the present invention and Z are shown. t Relationship diagram;

[0060] Figure 9 The following is an evaluation result of the exploration success rate of the Sangonghe Formation according to an embodiment of the present invention;

[0061] Figure 10 A sedimentary microfacies diagram of the Sangonghe Formation according to an embodiment of the present invention is shown. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0063] The Sangonghe Formation study area is located in the heart of the Junggar Basin, spanning 160 km east to west and 170 km north to south, covering an area of ​​approximately 2.7 × 10⁻⁶ km². 4 km 2 The study area includes the West Depression of Well 1, the Mosuowan Uplift, and the Luxi Uplift. The target stratum is the Jurassic Sangonghe Formation (J1s), the source rock is the Lower Permian Wuerhe Formation (P2w), and the caprock is Jurassic shale. The study area slopes from south to north, with finer facies in the south and coarser facies in the north. Oil and gas migrate from bottom to top and from south to north, forming fault-nose, fault-block, and lithological stratigraphic reservoirs. By the end of 2019, more than 203 exploration and appraisal wells had encountered the Sangonghe Formation and completed oil testing. Some oil and gas fields have been discovered in the Mosuowan Uplift, Mobei, Shixi, Shinan, and Xiayan areas.

[0064] A method for predicting oil and gas exploration success rate based on classification well statistics, exemplarily selecting the Sangonghe Formation in the central Junggar Basin as the target, includes the following steps:

[0065] S101: Quantitatively evaluate the geological conditions for hydrocarbon accumulation and draw an evaluation map: For example, four geological conditions are used, namely reservoir conditions, trap conditions, hydrocarbon supply conditions, caprock and preservation conditions (hydrocarbon generation conditions also include hydrocarbon generation conditions):

[0066] (1) Reservoir conditions

[0067] Based on sedimentary microfacies, reservoir conditions are quantitatively evaluated, ranked in descending order of quality as follows: underwater distributary channels, sheet sands, beaches and sandy debris flows, interdistributary bays, and littoral lacustrines. The corresponding evaluation values ​​are shown in Table 1. Within the same sedimentary microfacies, the evaluation value for each point is a random sample from the minimum to the maximum value of that microfacies. Taking underwater distributary channels as an example, the sampling range is from 0.7 to 0.9, and the random sample result is any real number between 0.7 and 0.9, inclusive. Based on the above method, the sedimentary microfacies map is converted into a quantitative reservoir evaluation map (e.g., ...) according to the evaluation values. Figure 2 As shown, quantitative reservoir evaluation is achieved by using sedimentary microfacies diagrams (such as...). Figure 10 The sedimentary microfacies diagram shown is transformed according to the random values ​​of each microfacies in Table 1 (sampled from the minimum and maximum values). Figure 2 . Figure 2 Different colors correspond to different quantitative evaluation values ​​of reservoir conditions, with larger values ​​indicating better reservoir conditions. Figure 2 (Note Figure 2 In the diagram: ● indicates an oil and gas well; ○ indicates a non-oil and gas well. In the diagram, labels such as "Shinan 2" and "Xiayan 1" are all exploration well names. The same applies to the following diagrams.

[0068] Table 1 Reservoir Evaluation Values

[0069]

[0070] (2) Trap conditions

[0071] Based on the top boundary structural map of the Sangonghe Formation and the results of trap interpretation, trap types are classified into major categories such as structural traps and lithological traps. Structural traps are further divided into confirmed traps and unconfirmed traps, with evaluation values ​​shown in Table 2. Based on sedimentary microfacies maps, lithological traps are divided into lithological lenses (shoals, bars, etc.) and lithological barriers (bays, etc.), with evaluation values ​​also shown in Table 2. Additionally, discovered oil and gas reservoirs are confirmed traps. Similarly, using a random sampling method with the same reservoir conditions, trap evaluation maps were drawn, as shown in Table 2. Figure 3 shown (Note Figure 2 (In the center: ● indicates an oil and gas well; ○ indicates a non-oil and gas well) Figure 3 Different colors correspond to different quantitative evaluation values ​​of trap conditions, with larger values ​​indicating better trap conditions.

[0072] Table 2. Enclosure Evaluation Values

[0073]

[0074] (3) Hydrocarbon supply conditions

[0075] The hydrocarbons in the Sangonghe Formation mainly originate from the underlying Permian Lower Wuerhe Formation source rocks. Vertically, they are primarily connected via source rock faults, while lateral migration is mainly controlled by sand bodies and tectonic ridges. The southern part, closer to the source rock, benefits from faults playing a crucial role in communication, making it relatively favorable overall. The northern part, much farther from the source rock (up to hundreds of kilometers), lacks fault connections and relies mainly on long-distance lateral migration for hydrocarbon supply, making it relatively unfavorable overall. Based on this understanding, and after completing the study of fault distribution and simulation of hydrocarbon migration channels, hydrocarbon supply conditions are divided into four levels: source rock-connecting faults + main path, main path, secondary path, southern near-source area, and northern far-source area. Evaluation values ​​are shown in Table 3. Similarly, a random sampling method was used to create an evaluation map of hydrocarbon supply conditions, as shown in Table 3. Figure 4 shown (Note Figure 2 (In the center: ● indicates an oil and gas well; ○ indicates a non-oil and gas well) Figure 4 Different colors correspond to different quantitative evaluation values ​​of hydrocarbon supply conditions, with higher values ​​indicating better supply conditions.

[0076] Table 3 Evaluation values ​​of hydrocarbon supply conditions

[0077]

[0078]

[0079] (4) Covering layer and preservation conditions

[0080] The caprock and preservation conditions are mainly considered based on the development of overlying faults and the distribution of unconformity weathering zones. Areas with well-developed overlying faults have a destructive effect, resulting in lower evaluation values ​​(0.1–0.2). Unconformity weathered clay provides good protection, leading to higher evaluation values ​​(0.7–0.9). Other areas have relatively well-developed mudstone and shale, acting as local caprocks, with evaluation values ​​(0.4–0.6). Then, quantitative evaluation values ​​for caprock and preservation conditions are obtained through random sampling, such as… Figure 5 shown (Note Figure 2 (In the center: ● indicates an oil and gas well; ○ indicates a non-oil and gas well) Figure 5 Different colors correspond to different quantitative evaluation values ​​of capping layer and preservation conditions; the larger the value, the better the capping layer and preservation conditions.

[0081] S102: Calculate the comprehensive geological evaluation value based on the evaluation map and draw a comprehensive geological evaluation map. Based on the quantitative evaluation of the geological conditions above, calculate the comprehensive geological evaluation value using the traditional geological risk assessment method (i.e., the multiplication method), multiplying multiple single-condition evaluation values ​​(referring to the same coordinate points) to obtain the comprehensive geological evaluation value Z. o And draw it into a diagram, abbreviated as Z. o-Map , . Among them, Z oThe value ranges from 0 to 1. The higher the evaluation value, the better the geological conditions and the more favorable for hydrocarbon accumulation. The comprehensive geological evaluation value map obtained in this embodiment is as Figure 6 shown (note: Figure 6 in □ are oil and gas wells; × are non-oil and gas wells). As can be seen from Figure 6 , the evaluation value Z o is between 0 and 0.525. At the location of oil and gas wells (red squares), the evaluation value is relatively high; at the location of non-oil and gas wells (blue crosses), the evaluation value is relatively low. This shows that the geological evaluation results are in good agreement with the oil testing results of exploration wells. This reflects the rationality of the quantitative evaluation results of geological conditions.

[0082] S103: Extract the comprehensive geological evaluation value of exploration wells from the comprehensive geological evaluation map

[0083] The comprehensive geological evaluation value has the characteristic of logarithmic change, which is not convenient for statistical analysis. Therefore, it is necessary to convert the geological evaluation value to make it a linearly changing value. According to the coordinates of 203 wells (see Table 4), the data of reservoir conditions, trap conditions, hydrocarbon supply conditions, caprock and preservation conditions, and Zo are respectively extracted from Figures 2-6 for logarithmic conversion. The formula is: Z t =ln(Z o +a)+b, where Z t is the converted comprehensive geological evaluation value. The higher the evaluation value, the better the geological conditions and the more favorable for hydrocarbon accumulation; a is the offset value, dimensionless, 0≤a≤1, and an exemplary value is 0, aiming to avoid the occurrence of 0 value in Z o ; b is the system translation value, dimensionless, and it is appropriate that 8 < b < 15, and an exemplary value is 10, aiming to avoid the occurrence of negative values in Z t (positive number calculation is more convenient). After converting Z o , Z t is obtained. All calculation results are shown in Table 4, where X and Y represent the geodetic coordinate values.

[0084] Table 4 Data of 203 wells in the Sangonghe Formation

[0085]

[0086]

[0087] S104: Calculate the exploration success rate according to the comprehensive geological evaluation value of exploration wells and establish an exploration success rate prediction template

[0088] (1) Statistically analyze Z t in intervals and calculate the exploration success rate

[0089] There are 203 oil testing wells in the study area, including 109 oil and gas wells and 94 non-oil and gas wells. The Z tThe data was analyzed in eight intervals, from smallest to largest, and the results are shown in Table 5. Different Z... t The statistical chart of the number of oil and gas wells and the number of non-oil and gas wells corresponding to the interval is as follows: Figure 7 ,from Figure 7 It is evident that the right-hand interval with higher evaluation values ​​has more oil and gas wells, while the left-hand interval with lower evaluation values ​​has more non-oil and gas wells.

[0090] Table 5. Z-values ​​of 203 wells in the Sangonghe Formation t Interval division and statistical results

[0091]

[0092] (2) Draw an exploration success rate chart.

[0093] With the exploration success rate P as the vertical axis (y-axis), the transformed comprehensive geological evaluation value Z t Using the x-axis as the horizontal coordinate, plot P(j) and Z for each interval. t The data for (j) are plotted on a graph and connected to form a curve, creating an exploration success rate chart. Wherein, Z... t (j) represents the j-th interval Z t The median value. For example, Z in Table 5... t Using the median as the X-axis and the exploration success rate as the Y-axis, connect the eight pairs of data points in Table 5 to form a curve, representing the relationship between the exploration success rate and the Z-axis. t Relationship chart, i.e., exploration success rate template, such as Figure 8 .from Figure 8 As can be seen from Z, t When Z is less than 2.5, the success rate is 0; when Z t When Z is greater than 9.5, the success rate is 100%; when Z t Between 2.5 and 9.5, the success rate increases with Z. t It increases with the increase of [something].

[0094] S105: Visualization of exploration risks Figure 8 Using the success rate chart as a ruler, the geological comprehensive evaluation map ( Figure 6 Converted into an exploration success rate map, such as Figure 9 shown (Note Figure 9 (□ represents oil and gas wells; × represents non-oil and gas wells), this is an exploration risk visualization map. To highlight favorable low-risk areas, areas with an exploration success rate of less than 50% are removed (white in the map), leaving only areas with a success rate greater than 50%, i.e., areas where oil and gas may be distributed. The color changes in the map indicate changes in risk levels, providing important reference for exploration deployment.

[0095] Based on the aforementioned oil and gas exploration success rate prediction methods, an oil and gas exploration success rate prediction system based on classified exploration well statistics is proposed, including:

[0096] The quantitative module is used to quantitatively evaluate the geological conditions for hydrocarbon accumulation and draw evaluation maps. The geological conditions for hydrocarbon accumulation include several single conditions, which are any one or more of the following: hydrocarbon generation conditions, hydrocarbon supply conditions, reservoir conditions, trap conditions, caprock conditions, or preservation conditions. Specifically, the single conditions are analyzed and quantified one by one to obtain the evaluation value of each single condition, and the evaluation map of each single condition is drawn based on the evaluation value. The evaluation value is between 0 and 1.

[0097] The calculation module is used to calculate the comprehensive geological evaluation value based on the evaluation map and draw the comprehensive geological evaluation map. Specifically, it multiplies the evaluation values ​​of a single condition at the same coordinate point on the evaluation map to obtain the comprehensive geological evaluation value, wherein the comprehensive geological evaluation value is between 0 and 1.

[0098] The extraction module is used to extract the comprehensive geological evaluation values ​​of exploration wells from the comprehensive geological evaluation map;

[0099] The prediction module is used to calculate the exploration success rate based on the comprehensive geological evaluation value of the exploration well, thus enabling prediction.

[0100] Furthermore, the prediction module includes:

[0101] The conversion unit is used to perform a logarithmic conversion on the comprehensive geological evaluation value. The conversion formula is as follows:

[0102] Z t =ln(Z) o +a)+b, where Z o This is the comprehensive geological evaluation value; Z t Here are the converted comprehensive geological evaluation values; a is the offset value, 0 ≤ a ≤ 1; b is the system translation value, 8 <b<15;

[0103] The partition unit is used to divide the converted geological comprehensive evaluation value into intervals to count the number of exploration wells. Specifically, it involves: determining the maximum and minimum values ​​of the converted geological comprehensive evaluation value, dividing the value into several intervals based on the maximum and minimum values, and counting the number of oil and gas exploration wells and non-oil and gas exploration wells in each interval.

[0104] The calculation unit is used to calculate the exploration success rate based on the number of wells in the interval. The formula is as follows:

[0105] P(j) = 100 × n(j) / [n(j) + m(j)], where j is the interval number; P(j) is the exploration success rate of the j-th interval, %; n(j) is the number of exploration wells of the oil and gas well type in the j-th interval; m(j) is the number of exploration wells of the oil and gas well type in the j-th interval;

[0106] The visualization unit is used to draw an exploration success rate map based on the calculated exploration success rate and to perform visualization prediction.

[0107] In summary, this invention combines the quantitative evaluation results of hydrocarbon accumulation in a two-dimensional plane (figure) with the results of exploratory well drilling to establish an exploration success rate prediction chart and quantitatively predict exploration risks. This includes: incorporating the geological evaluation value Z... o The transformed geological comprehensive evaluation value Z is obtained by performing logarithmic transformation. t Find all exploration wells Z t The minimum and maximum values ​​are used to divide the range into several intervals. The number of exploration wells (both oil and gas and non-oil and gas) in each interval is counted. The number of oil and gas wells in each interval is divided by the total number of exploration wells in that interval to obtain the exploration success rate for that interval. Several sets of exploration success rates are then compared with Z. t The data is plotted as curves to form an exploration success rate prediction chart. This prediction chart is used to transform the comprehensive geological evaluation map into an exploration success rate map, thereby visualizing risks, avoiding exploration risks, and improving exploration efficiency.

[0108] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the success rate of oil and gas exploration based on classified wildcat statistics, characterized by, Includes the following steps: Quantitatively evaluate the geological conditions for hydrocarbon accumulation and draw evaluation maps; Calculate the comprehensive geological evaluation value based on the evaluation map and draw a comprehensive geological evaluation map; Extract the comprehensive geological evaluation value of the exploration well from the aforementioned comprehensive geological evaluation map; The exploration success rate is calculated based on the comprehensive geological evaluation value of the exploration well, thus achieving prediction. The calculation of the exploration success rate based on the comprehensive geological evaluation value of the exploration well includes: The geological comprehensive evaluation values ​​were then logarithmically converted. The number of exploration wells was counted in intervals based on the converted comprehensive geological evaluation values. The exploration success rate is calculated based on the number of exploration wells. The step of dividing the converted geological comprehensive evaluation value into intervals to count the number of exploration wells includes: determining the maximum and minimum values ​​of the converted geological comprehensive evaluation value, dividing different intervals according to the maximum and minimum values, and counting the number of oil and gas exploration wells and the number of non-oil and gas exploration wells in each interval. The formula for calculating the exploration success rate based on the number of exploration wells is as follows: P(j) = 100 × n(j) / [n(j) + m(j)] Where j is the interval number; P(j) is the exploration success rate of the j-th interval; n(j) is the number of exploration wells of oil and gas type in the j-th interval; and m(j) is the number of exploration wells of non-oil and gas type in the j-th interval.

2. The method for predicting oil and gas exploration success rate based on classification well statistics according to claim 1, characterized in that, The geological conditions for hydrocarbon accumulation include single conditions, which are any one or more of the following: hydrocarbon generation conditions, hydrocarbon supply conditions, reservoir conditions, trap conditions, caprock conditions, or preservation conditions.

3. The method for predicting oil and gas exploration success rate based on classification well statistics according to claim 2, characterized in that, The quantitative evaluation of oil and gas accumulation geological conditions and the drawing of evaluation maps include: analyzing and quantifying each of the individual conditions to obtain the evaluation value of each individual condition, and drawing the evaluation map of each individual condition based on the evaluation value, wherein the evaluation value is 0-1.

4. The method for predicting oil and gas exploration success rate based on classification well statistics according to claim 2, characterized in that, The calculation of the comprehensive geological evaluation value based on the evaluation map includes: multiplying the evaluation values ​​of a single condition at the same coordinate point on the evaluation map to obtain the comprehensive geological evaluation value, wherein the comprehensive geological evaluation value takes the range of 0-1.

5. The method for predicting oil and gas exploration success rate based on classification well statistics according to claim 1, characterized in that, The formula for logarithmically converting the comprehensive geological evaluation value is as follows: Zt=ln(Zo+a)+b Where Zo is the comprehensive geological evaluation value; Zt is the converted comprehensive geological evaluation value; a is the offset value, 0≤a≤1; b is the system translation value, 8 <b<15。 6. The method for predicting the success rate of oil and gas exploration based on classified wildcat statistics according to claim 1, characterized in that, Also includes Based on the exploration success rate, an exploration success rate map is drawn for visual prediction.

7. The method for predicting the success rate of oil and gas exploration based on classified wildcat statistics according to claim 5, characterized in that, The value of 'a' is 0, and the value of 'b' is 10.

8. A system for predicting the success rate of oil and gas exploration based on the statistics of classified wildcat wells, characterized by, include The quantitative module is used to quantitatively evaluate the geological conditions for hydrocarbon accumulation and generate evaluation maps. The calculation module is used to calculate the comprehensive geological evaluation value based on the evaluation map and draw it into a comprehensive geological evaluation map; The extraction module is used to extract the comprehensive geological evaluation value of the exploration well from the comprehensive geological evaluation map; The prediction module is used to calculate the exploration success rate based on the comprehensive geological evaluation value of the exploration well, thereby achieving prediction. The prediction module includes A conversion unit is used to perform a logarithmic conversion on the comprehensive geological evaluation value; The partition unit is used to divide the converted comprehensive geological evaluation value into intervals to count the number of exploration wells. A calculation unit is used to calculate the exploration success rate based on the number of exploration wells; The partitioning unit is used to count the number of exploration wells in intervals of the converted geological comprehensive evaluation value, including: determining the maximum and minimum values ​​of the converted geological comprehensive evaluation value, dividing different intervals according to the maximum and minimum values, and counting the number of oil and gas exploration wells and the number of non-oil and gas exploration wells in each interval. The calculation unit uses the following formula to calculate the exploration success rate based on the number of exploration wells: P(j) = 100 × n(j) / [n(j) + m(j)] Where j is the interval number; P(j) is the exploration success rate of the j-th interval, %; n(j) is the number of exploration wells of oil and gas type in the j-th interval; and m(j) is the number of exploration wells of non-oil and gas type in the j-th interval.

9. The oil and gas exploration success rate prediction system based on classification well statistics according to claim 8, characterized in that, The geological conditions for hydrocarbon accumulation include several single conditions, which are any one or more of the following: hydrocarbon generation conditions, hydrocarbon supply conditions, reservoir conditions, trap conditions, caprock conditions, or preservation conditions. The quantitative module, used for quantitatively evaluating the geological conditions for hydrocarbon accumulation and drawing evaluation maps, includes: analyzing and quantifying each of the individual conditions to obtain the evaluation value of each individual condition, and drawing the evaluation map of each individual condition based on the evaluation value, wherein the evaluation value is between 0 and 1.

10. The oil and gas exploration success rate prediction system based on classification well statistics according to claim 9, characterized in that, The calculation module is used to calculate the comprehensive geological evaluation value based on the evaluation map, including multiplying the evaluation values ​​of a single condition at the same coordinate point on the evaluation map to obtain the comprehensive geological evaluation value, wherein the comprehensive geological evaluation value takes the value of 0-1.

11. The oil and gas exploration success rate prediction system based on classification well statistics according to claim 8, characterized in that, The conversion unit uses the following formula to perform a logarithmic conversion on the comprehensive geological evaluation value: Zt=ln(Zo+a)+b Where Zo is the comprehensive geological evaluation value; Zt is the converted comprehensive geological evaluation value; a is the offset value, 0≤a≤1; b is the system translation value, 8 <b<15。 12. The oil and gas exploration success rate prediction system based on classification well statistics according to claim 8, characterized in that, The prediction module also includes The visualization unit is used to draw an exploration success rate map based on the exploration success rate and perform visualization prediction.

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