Volume fracturing horizontal well sectional and clustered method based on mechanical specific energy and genetic algorithm

Abstract

The invention discloses a method for sectional and clustered fracturing of horizontal wells based on mechanical specific energy and genetic algorithm, which relates to the field of drilling and completion engineering in oil and gas. It is difficult to meet the refined requirements for sectional schemes in actual engineering based on traditional geometric partitioning methods. By collecting drilling dynamic data, a drilling dynamic data set table at equal well depth intervals is formed according to the well depth step length, and the bit torque and weight on bit are calculated. The mechanical specific energy of the horizontal well section is calculated based on the drilling dynamic data set table to obtain a set of mechanical specific energy values along the wellbore. Based on the set constraints, an initial population is generated, and the fitness of each individual in the initial population is obtained. A set number of individuals are randomly selected from the initial population to form a candidate set, and operations such as fitness calculation, selection, crossover, and mutation are performed until the maximum number of iterations is reached to complete the iterative optimization. The optimal sectional scheme and perforation cluster distribution are output, and drilling construction is carried out according to the optimal sectional scheme and perforation cluster distribution.

Description

Technical Field

[0001] The present invention relates to the field of drilling and completion engineering in oil and gas, and specifically to a method for segmenting and clustering horizontal wells in volumetric fracturing based on mechanical specific energy and genetic algorithm. Background Art

[0002] With the rapid development of social economy, the demand for the exploitation of unconventional oil and gas resources (such as shale gas and tight oil) is becoming increasingly urgent. In the process of exploiting these resources, volumetric fracturing technology has become the key technology to improve the oil and gas production per well. By creating a complex fracture network in the reservoir, volumetric fracturing technology can significantly enhance the fluidity of oil and gas around the wellbore and effectively improve the productivity of a single well. Horizontal well drilling technology, as an optimal supporting technology for volumetric fracturing, can make the wellbore drill along with the reservoir, thereby increasing the contact area between the wellbore and the reservoir and providing strong support for the efficient exploitation of oil and gas resources. However, to achieve good results of horizontal well volumetric fracturing technology, it is necessary to segment and cluster the horizontal section based on the physical properties of the reservoir. The segmentation and clustering need to be based on various attributes of the drilled reservoir. First, the reservoir is divided into different segments according to the attributes, and then each segment is further subdivided into clusters according to the distribution characteristics of the attributes. Segmenting and clustering play an important role in effectively tapping the production of oil and gas wells. However, in actual operation, due to the complex variety of parameters characterizing the physical properties of the reservoir, it is also necessary to select appropriate parameters to carry out the horizontal section division. Mechanical specific energy, as a physical quantity calculated through various engineering parameters (such as drilling pressure, torque, drilling speed, etc.) during the actual drilling process, can more accurately reflect the difficulty of rock fragmentation and can be used as a comprehensive index reflecting the heterogeneity of the reservoir. In addition to the selection of division parameters, when dealing with complex multi-constraint conditions and dynamic adjustment requirements, traditional geometric division methods have deficiencies in dealing with multi-constraints (such as segment length, non-fracturing well section) and dynamic adjustment requirements and are difficult to meet the refined requirements for the segmentation scheme in actual engineering. Genetic algorithm, as an optimization algorithm, simulates natural selection and genetic mechanisms and can quickly search for the global optimal solution or approximate optimal solution in complex multi-dimensional optimization problems through operations such as selection, crossover, and mutation. In view of this, the present invention aims to make full use of the advantages of mechanical specific energy and genetic algorithm to develop a method for segmenting and clustering horizontal wells in volumetric fracturing. This method calculates the mechanical specific energy of the drilled reservoir through drilling data and combines genetic algorithm to globally optimize the segmentation scheme, which can effectively handle complex constraints and dynamic adjustment requirements and generate a better segmentation scheme. This method can provide effective technical support for the precise control and optimization of horizontal well volumetric fracturing operations, thereby effectively improving the production and economic benefits of oil and gas wells, promoting the efficient development of unconventional oil and gas resources, and having significant social and economic benefits and broad application prospects. Summary of the Invention

[0003] The object of the present invention is to overcome the deficiencies of the prior art and provide a method for segmenting and clustering horizontal wells in volumetric fracturing based on mechanical specific energy and genetic algorithm, including the following steps:

[0004] Step 1: Collect drilling dynamic data, form a drilling dynamic data set table at equal well depth intervals according to the well depth step length, and calculate the bit torque and weight on bit.

[0005] Step 2: Calculate the mechanical specific energy of the horizontal well section based on the drilling dynamic data set table to obtain a set of mechanical specific energy values along the wellbore.

[0006] Step 3: Generate an initial population based on the set constraints and obtain the fitness of each individual in the initial population.

[0007] Step 4: Randomly select a set number of individuals from the initial population to form a candidate set, and use the tournament selection method to screen out the individuals with high fitness in the candidate set; generate offspring through the segmented boundary replacement strategy for crossover operation; randomly adjust the segmented boundary depth and reorder for mutation operation; repeat the fitness calculation, selection, crossover, and mutation operations until the maximum number of iterations is reached to complete the iterative optimization.

[0008] Step 5: Output the optimal segmentation scheme and perforation cluster distribution, and perform drilling construction according to the optimal segmentation scheme and perforation cluster distribution.

[0009] Further, the forming of the drilling dynamic data set table at equal well depth intervals according to the well depth step length and the calculation of the bit torque and weight on bit include:

[0010] Based on the interval from the starting well depth to the ending well depth of the horizontal well section, take values of the drilling dynamic data at the set well depth step length within the interval to form a drilling dynamic data set table at equal well depth intervals; calculate the bit torque and weight on bit corresponding to each record based on the drilling dynamic data set table:

[0011] Calculate the bit torque and weight on bit for the well sections with and without the use of a screw drill respectively.

[0012] For the well section with a screw drill, the bit torque is TBit = Kt*ΔP, where Kt is the torque output per unit pressure difference of the screw drill, and ΔP is the pressure difference of the screw drill in the drilling state; where ΔP is estimated using the standpipe pressure, subtracting the standpipe pressure when the bit is lifted off the bottom from the standpipe pressure when the bit is drilling at the bottom; the weight on bit is estimated through the measured hook load, subtracting the hook load when the bit is lifted off the bottom from the hook load when the bit is drilling at the bottom.

[0013] For the well section without the use of a screw drill, the bit torque is predicted using the friction torque model, taking the rotary table torque as the input value of the friction torque model and calculating from the wellhead to the bottom to predict the bit torque, and the method for estimating the weight on bit is the same as when using a screw drill.

[0014] Further, calculating the mechanical specific energy of the horizontal well section according to the drilling dynamic data set table to obtain a set of mechanical specific energy values along the wellbore, including:

[0015] Based on the drilling dynamic data, calculate the mechanical specific energy within the horizontal well section interval according to the set well depth step length to form a mechanical specific energy data set. The mechanical specific energy is calculated using the following formula:

[0016]

[0017] where Dbit is the bit diameter, ROP is the drilling rate, Kn is the output speed of the screw under unit mud flow rate, Q is the mud flow rate, TBit is the bit torque, N is the rotary table speed. If there is no screw, Kn is taken as 0.

[0018] Further, generating an initial population based on the set constraint conditions, including:

[0019] The constraint conditions include the number of fracturing stages, the range of stage lengths, the range of the number of clusters per stage, the length of each cluster, and the non-fracturing well section. The generation of the initial population includes:

[0020] Step 31: Set the total number of individuals in the population. Each individual represents a fracturing segmentation scheme, which consists of fracturing stage number - 1 fracturing boundary depth values, and the boundary values are arranged in ascending order.

[0021] Step 32: Randomly generate individuals. Starting from the starting depth, generate fracturing boundaries according to a random step length, and the step length range is within the stage length range.

[0022] Step 33: Repeat Step 31 and Step 32 until the number of individuals in the initial population is the total number of individuals set in the population.

[0023] Further, obtaining the fitness of each individual in the initial population, including:

[0024] Verify the legality of the fracturing boundary of the individual. First, check the number of fracturing segments to verify whether the number of fracturing boundaries of the individual is fracturing stage number - 1. If not, directly return -Infinity. Then, perform the stage length constraint check: traverse all fracturing stages and calculate whether the length of each stage satisfies minStageLength ≤ stage length ≤ maxStageLength. Record the number of violations stageViolations. The calculation method of the number of violations is:

[0025]

[0026] Individual perforation cluster division and verification. First, perforation clusters are generated. For each fracturing stage, perforation clusters are divided according to the length of each cluster; and the length of each cluster is greater than half of the length of each cluster. Then, cluster number constraint checking is performed. The number of perforation clusters in each stage is counted. If it exceeds the range of the number of clusters in each stage, the number of violations is recorded.

[0027] Fitness calculation is carried out. First, data mapping is performed. The well depth range of the perforation clusters is mapped to the mechanical specific energy dataset. For each perforation cluster, the mechanical specific energy values within its corresponding well depth range are extracted, and the cluster variance is calculated.

[0028] Cluster variance = Variance(MSEs, MSEs+1, …, MSEe)

[0029] Where s and e are the data indices corresponding to the starting and ending depths of the cluster respectively. If the cluster length is invalid, the variance is set to 0. Finally, the cluster variances of all perforation clusters are accumulated and taken as negative as the fitness. If there are any stage or cluster violations, -Infinity is returned to impose a penalty. The final fitness formula is:

[0030] 。

[0031] Furthermore, a set number of individuals are randomly selected from the initial population to form a candidate set. The tournament selection method is used to screen the individuals with high fitness in the candidate set, including:

[0032] Step 41: Randomly select a set number of individuals from the population to form a candidate set; compare the fitness values of the candidate individuals, and select the individual with the highest fitness as the winner.

[0033] Step 42: Repeat Step 41 until the size of the new population reaches the total number of individuals in the set population.

[0034] Furthermore, offspring are generated through a segmented boundary replacement strategy for crossover operations, including: randomly selecting two parent individuals parent1 and parent2 from the population. For their segmented boundary lists, traverse each segmented boundary position, and generate the corresponding boundary value of the offspring child by selecting the corresponding boundary value of parent1 or parent2 with a set probability; reorder the boundary values of the offspring to ensure monotonic increase, and constrain the boundary values of child within the valid interval.

[0035] Further, the random adjustment of the segmented boundary depth and reordering, and the mutation operation include: each individual in the population triggers the mutation operation with a set probability, randomly selects a position in the individual segmented boundary list, applies a random perturbation within a set range to the boundary value corresponding to this position to generate a new value, and constrains it to the effective interval; replaces the original boundary value and reorders the entire list. If the perforation clusters generated after mutation conflict with non-fracture well sections, a penalty is imposed in the fitness calculation.

[0036] Further, the output of the optimal segmentation scheme and perforation cluster distribution includes: dividing the perforation clusters from the optimal solution of the genetic algorithm according to the length of each cluster, ensuring that the number of clusters is within the range of the number of clusters in each segment and avoiding non-fracture well sections; depth correction, including comparing the depth deviation between the perforation clusters and the formation characteristic interface by combining gamma logging data. If there is a deviation, the position of the cluster is translated, or based on the absolute depth marking of the casing collar data, the well depth range of the cluster is re-anchored to eliminate the error; outputting the depth-corrected perforation cluster distribution, including the segmentation number, start and end depths, and cluster details, to ensure accurate positioning of the target reservoir for the fracturing operation.

[0037] The beneficial effects of the present invention are: dynamically characterizing reservoir heterogeneity based on mechanical specific energy; optimizing the variance of mechanical specific energy of perforation clusters within a fracturing section through a genetic algorithm, and combining constraints such as section length range, number of clusters limit, and non-fracture well sections to achieve uniform segmentation of reservoir physical properties. The present invention is of great significance for improving the fracturing effect of horizontal wells and the oil and gas production of single wells. Description of the Drawings

[0038] Figure 1 It is a flow schematic diagram of the volume fracturing horizontal well segmentation and clustering method based on mechanical specific energy and genetic algorithm;

[0039] Figure 2 It is an implementation flowchart of the volume fracturing horizontal well segmentation and clustering method based on mechanical specific energy and genetic algorithm. Detailed Embodiment

[0040] The technical solution of the present invention will be further described in detail below with reference to the drawings, but the protection scope of the present invention is not limited to the following.

[0041] The features and performance of the present invention will be further described in detail below with reference to the embodiments.

[0042] As Figure 1 shown, the volume fracturing horizontal well segmentation and clustering method based on mechanical specific energy and genetic algorithm includes the following steps:

[0043] Step 1, collect drilling dynamic data, form a drilling dynamic data set table at equal well depth intervals according to the well depth step size, and calculate the bit torque and weight on bit.

[0044] Step 2: Calculate the mechanical specific energy of the horizontal well section based on the drilling dynamic data set table to obtain the set of mechanical specific energy values along the wellbore;

[0045] Step 3: Generate an initial population based on the set constraints and obtain the fitness of each individual in the initial population;

[0046] Step 4: Randomly select a set number of individuals from the initial population to form a candidate set, and use the tournament selection method to screen out the individuals with high fitness in the candidate set; generate offspring through the segmented boundary replacement strategy for crossover operation; randomly adjust the depth of the segmented boundary and reorder for mutation operation; repeat the fitness calculation, selection, crossover, and mutation operations until the maximum number of iterations is reached to complete the iterative optimization;

[0047] Step 5: Output the optimal segmentation scheme and perforation cluster distribution, and perform drilling construction according to the optimal segmentation scheme and perforation cluster distribution.

[0048] The formation of the drilling dynamic data set table at equal well depth intervals according to the well depth step and the calculation of the bit torque and weight on bit include:

[0049] Based on the interval from the starting well depth to the ending well depth of the horizontal well section, the drilling dynamic data is sampled at the set well depth step within the interval to form a drilling dynamic data set table at equal well depth intervals; calculate the bit torque and weight on bit corresponding to each record based on the drilling dynamic data set table:

[0050] Calculate the bit torque and weight on bit for the well sections with and without using the positive displacement motor (PDM) respectively;

[0051] For the well section with PDM, the bit torque is TBit = Kt*ΔP, where Kt is the torque output per unit differential pressure of the PDM, and ΔP is the differential pressure of the PDM in the drilling state; where ΔP is estimated using the standpipe pressure, which is the standpipe pressure when the bit is drilling at the bottom of the well minus the standpipe pressure when the bit is lifted off the bottom of the well; the weight on bit is estimated through the measured hook load, which is the hook load when the bit is lifted off the bottom of the well minus the hook load when the bit is drilling at the bottom of the well;

[0052] For the well section without using the PDM, the bit torque is predicted using the friction torque model, and the rotary table torque is used as the input value of the friction torque model to calculate the bit torque from the wellhead to the bottom of the well. The method of estimating the weight on bit is the same as when using the PDM.

[0053] The calculation of the mechanical specific energy of the horizontal well section based on the drilling dynamic data set table to obtain the set of mechanical specific energy values along the wellbore includes:

[0054] Based on the drilling dynamic data, calculate the mechanical specific energy at the set well depth step within the horizontal well section interval to form a mechanical specific energy data set. The mechanical specific energy is calculated using the following formula:

[0055]

[0056] Among them, Dbit is the bit diameter, ROP is the drilling rate, Kn is the output rotation speed of the screw under the unit mud flow rate, Q is the mud flow rate, TBit is the bit torque, N is the rotary table rotation speed. If there is no screw, Kn is taken as 0.

[0057] Generating an initial population based on the set constraint conditions includes:

[0058] The constraint conditions include the number of fracturing stages, the range of stage lengths, the range of the number of clusters per stage, the length of each cluster, and the non-fracturing well section; the generating of the initial population includes:

[0059] Step 31: Set the total number of individuals in the population. Each individual represents a segmentation scheme, which consists of fracturing stage number - 1 fracturing boundary depth values, and the boundary values are arranged in ascending order;

[0060] Step 32: Randomly generate individuals. Starting from the starting depth, generate fracturing boundaries according to a random step length, and the step length range is within the stage length range;

[0061] Step 33: Repeat Step 31 and Step 32 until the number of individuals in the initial population is the total number of individuals set in the population.

[0062] Obtaining the fitness of each individual in the initial population includes:

[0063] Checking the legality of the fracturing boundary of the individual. First, perform a fracturing number check to verify whether the number of fracturing boundaries of the individual is fracturing stage number - 1; if not, directly return -Infinity; then perform a stage length constraint check: traverse all fracturing stages, calculate whether the length of each stage satisfies minStageLength ≤ stage length ≤ maxStageLength; record the number of violations stageViolations, and the calculation method of the number of violations is:

[0064] ;

[0065] Dividing and checking the perforation clusters of the individual. First, perform perforation cluster generation. For each fracturing stage, divide the perforation clusters according to the length of each cluster; and the length of each cluster is greater than half of the length of each cluster; then perform a cluster number constraint check, count the number of perforation clusters in each stage, and if it exceeds the range of the number of clusters per stage, record the number of violations;

[0066] Perform fitness calculation. First, perform data mapping, map the well depth range of the perforation clusters to the mechanical specific energy data set, for each perforation cluster, extract the mechanical specific energy values within its corresponding well depth range, and calculate the cluster variance;

[0067] Cluster variance = Variance(MSEs, MSEs+1, …, MSEe)

[0068] where s and e are the data indices corresponding to the starting and ending depths of the cluster respectively. If the cluster length is invalid, the variance is set to 0. Finally, the cluster variances of all perforation clusters are accumulated and negated as the fitness. If any segment or cluster violation exists, -Infinity is returned to impose a penalty. The final fitness formula is:

[0069] .

[0070] Randomly selecting a set number of individuals from the initial population to form a candidate set, and using the tournament selection method to screen out the individuals with high fitness in the candidate set, including:

[0071] Step 41: Randomly select a set number of individuals from the population to form a candidate set; compare the fitness values of the candidate individuals and select the individual with the highest fitness as the winner;

[0072] Step 42: Repeat Step 41 until the size of the new population reaches the total number of individuals in the set population.

[0073] Generating offspring through the segmented boundary replacement strategy and performing crossover operations, including:

[0074] Randomly select two parent individuals parent1 and parent2 from the population. For their segmented boundary lists, traverse each segmented boundary position and generate an offspring child by selecting the corresponding boundary value of parent1 or parent2 with a set probability;

[0075] Reorder the boundary values of the offspring to ensure monotonic increase and constrain the boundary values of child within the valid interval.

[0076] Randomly adjusting the segmented boundary depth and reordering to perform mutation operations, including:

[0077] Each individual in the population triggers the mutation operation with a set probability, randomly selects a position in the individual's segmented boundary list, applies a random perturbation within a certain range to the corresponding boundary value at that position to generate a new value, and constrains it to the valid interval;

[0078] Replace the original boundary value and reorder the entire list. If the perforation cluster generated after mutation conflicts with the non-fractured well section, a penalty is imposed in the fitness calculation.

[0079] Outputting the optimal segmentation scheme and perforation cluster distribution, including:

[0080] Dividing the perforation clusters from the optimal solution of the genetic algorithm according to the length of each cluster, ensuring that the number of clusters is within the range of the number of clusters in each segment and avoiding the non-fractured well sections;

[0081] Perform depth calibration, including comparing the depth deviation between the perforation clusters and the formation characteristic interface by combining gamma logging data. If there is a deviation, translate the cluster position, or re-anchor the depth range of the cluster wells based on the absolute depth markings of the casing collars to eliminate errors;

[0082] Output the distribution of perforation clusters after depth calibration, including section numbers, start and end depths, and cluster details, to ensure accurate positioning of the target reservoir for the fracturing operation.

[0083] As Figure 2 shown, the technical solution adopted by the present invention is: a method for sectional fracturing of horizontal wells based on mechanical specific energy and genetic algorithm, and the specific steps are as follows:

[0084] First, collect drilling dynamic data to form a table of drilling dynamic data sets at equal well depth intervals, and calculate the bit torque and weight on bit.

[0085] Next, calculate the mechanical specific energy of the horizontal well section to obtain a set of mechanical specific energy values along the wellbore.

[0086] Then, define constraint conditions such as the number of fracturing sections, section length range, number of clusters per section range, and non-fracturing well sections for the horizontal well section.

[0087] Subsequently, initialize the population to generate an initial candidate solution that meets the section length constraint.

[0088] After that, perform fitness calculation, use the variance of the mechanical specific energy corresponding to the perforation clusters within each section as the fitness function, and impose penalties on the solutions that violate the constraints.

[0089] Next, perform selection operations, use the tournament selection method to screen individuals with high fitness; generate offspring through the sectional boundary replacement strategy for crossover operations; randomly adjust the sectional boundary depths and reorder for mutation operations; repeat the fitness calculation, selection, crossover, and mutation operations until the maximum number of iterations is reached to complete iterative optimization.

[0090] Finally, output the optimal sectional fracturing plan and the distribution of perforation clusters, and perform depth calibration to dynamically adjust the well section range deviation. The analysis process is as Figure 1 shown.

[0091] The present invention needs to record relevant drilling dynamic data of the construction well, including but not limited to the hook load (HKLoad), rate of penetration (ROP), mud flow rate (Q), rotary table speed (N), rotary table torque (TSurface), bit diameter (DBit), and standpipe pressure (SPP).

[0092] Organize the drilling dynamic data of the horizontal well section. Based on the interval from the starting well depth (totalStart) to the ending well depth (totalEnd) of the horizontal well section, the drilling dynamic data is sampled at a certain well depth step within the interval to form a table of drilling dynamic data set at equal well depth intervals.

[0093] Calculate the bit torque (TBit) and weight on bit (WOB) corresponding to each record based on the drilling dynamic data set table:

[0094] Analyze whether a positive displacement motor (PDM) is used during the drilling of the construction well in the entire horizontal well section. Calculate the bit torque (TBit) and weight on bit (WOB) separately for the well sections with and without the use of a PDM.

[0095] For the well section with a PDM, the bit torque is TBit = Kt*ΔP, where Kt is the output torque per unit differential pressure of the PDM, ΔP is the differential pressure of the PDM in the drilling state, and ΔP is estimated using the standpipe pressure (SPP), which is the standpipe pressure when the bit is drilling at the bottom minus the standpipe pressure when the bit is lifted off the bottom. The weight on bit (WOB) is estimated by measuring the hook load (HKLoad), which is the hook load when the bit is lifted off the bottom minus the hook load when the bit is drilling at the bottom.

[0096] For the well section without a PDM, the bit torque (TBit) is predicted using the friction torque model. The rotary table torque (TSurface) is used as the input value of the friction torque model to calculate the bit torque from the wellhead to the bottom. The method for estimating the weight on bit (WOB) is the same as that when using a PDM.

[0097] Based on the drilling dynamic data, the mechanical specific energy (MSE) is calculated at a certain well depth step within the horizontal well section interval to form a mechanical specific energy data set (mseData). The mechanical specific energy is calculated using the following formula:

[0098]

[0099] Where, Dbit is the bit diameter, ROP is the rate of penetration, Kn is the output rotational speed per unit mud flow rate of the PDM, Q is the mud flow rate, TBit is the bit torque, N is the rotary table speed. If there is no PDM, Kn is taken as 0.

[0100] Set the constraint conditions for the genetic algorithm, including the number of fracturing stages (numStages), stage length range (minimum stage length, maximum stage length) (minStageLength, maxStageLength), number of clusters per stage range (minimum number of clusters, maximum number of clusters) (minClustersPerStage, maxClustersPerStage), length of each cluster (clusterLength), and non-fracturing well sections (avoidZones).

[0101] Generate an initial population that meets the segment length constraint:

[0102] Step 1: Set the total number of individuals in the population (populationSize). Each individual represents a segmentation scheme, which consists of numStages - 1 segment boundary depth values, and the boundary values are arranged in ascending order.

[0103] Step 2: Randomly generate individuals. Starting from the starting depth, generate segment boundaries with random step lengths. The step length range is within [minStageLength, maxStageLength]; ensure that the last segment boundary does not exceed totalEnd - minStageLength to prevent the length of the last segment from being insufficient.

[0104] Step 3: Repeat Step 1 and Step 2 until the number of individuals in the initial population is populationSize.

[0105] Calculate the fitness of individuals in the population. Fitness is used to evaluate the quality of individual segmentations. In the present invention, it is achieved by minimizing the variance of the corresponding mechanical specific energy (MSE) of the perforation clusters within each segment to ensure the uniformity of reservoir characteristics within the same fracturing segment. The specific steps are as follows:

[0106] Verify the legality of the segment boundaries of individuals. First, check the number of segments, and verify whether the number of segment boundaries of the individual is numStages - 1. If not, directly return -Infinity (indicating that the scheme is completely infeasible); then check the segment length constraint: traverse all fracturing segments and calculate whether the length of each segment satisfies minStageLength ≤ segment length ≤ maxStageLength. Record the number of violations stageViolations. The calculation method of the number of violations is:

[0107]

[0108] Partition and verification of perforation clusters of individuals. First, generate perforation clusters. For each fracturing segment, divide the perforation clusters by the clusterLength. And ensure that the clusters do not overlap with non-fracturing well sections (avoidZones), and the length of each cluster is at least clusterLength / 2 to avoid invalid short clusters; then check the cluster number constraint, count the number of perforation clusters in each segment, and if it exceeds the range of [minClustersPerStage, maxClustersPerStage], record the number of violations (clusterViolations).

[0109] Perform fitness calculation. First, perform data mapping to map the well depth range of the perforation cluster to the mechanical specific energy dataset mseData. For each perforation cluster, extract the mechanical specific energy values within its corresponding well depth range and calculate the variance. The cluster variance = Variance(MSEs, MSEs+1, …, MSEe), where s and e are the data indices corresponding to the starting and ending depths of the cluster respectively. If the cluster length is invalid (such as being completely located in a non-fracturing well section), the variance is set to 0. Finally, sum up the cluster variances of all perforation clusters and take the negative value as the fitness (the optimization goal is to minimize the variance). If there are any stage or cluster violations (stageViolations>0 or clusterViolations>0), return -Infinity to impose a penalty. The final fitness formula is:

[0110]

[0111] Carry out the selection operation. This invention is implemented based on the tournament selection method to screen out individuals with high fitness from the current population and enter the next generation, driving the population to evolve towards a better solution:

[0112] First, randomly select a certain number (tournamentSize) of individuals from the population to form a candidate set; then compare the fitness values of the candidate individuals and select the individual with the highest fitness as the winner. For example, if the fitness values of the individuals in the candidate set are [-10, -Infinity, -5, -20, -Infinity], then select the individual with a fitness of -5.

[0113] Repeat the execution until the size of the new population reaches the original population size (populationSize). During this process, individuals with a fitness of -Infinity cannot win and are naturally eliminated.

[0114] Carry out the crossover operation. Generate offspring individuals by fusing the section boundary information of two parent individuals. This invention adopts a section boundary replacement strategy. The crossover operation needs to ensure that the generated offspring individuals meet the section length constraints (minStageLength and maxStageLength) and the boundary monotonically increasing property, and at the same time be compatible with the correction ability of subsequent mutation operations. The specific process is as follows:

[0115] Randomly select two parent individuals, parent1 and parent2, from the population. For their segmented boundary lists (e.g., parent1 = [1550, 1650, 1750, 1850], parent2 = [1570, 1670, 1770, 1870]), traverse each segmented boundary position, and generate the offspring child (e.g., it may generate child = [1550, 1670, 1750, 1870]) by selecting the corresponding boundary value of parent1 or parent2 with a certain probability (e.g., 50%).

[0116] Subsequently, reorder the boundary values of the offspring to ensure monotonic increase, and constrain the boundary values of child within the valid interval [totalStart + minStageLength, totalEnd - minStageLength] (for example, when totalEnd = 1900 and minStageLength = 50, if the last segment boundary exceeds 1850, it is forced to be corrected to 1850) to avoid exceeding the segment length limit.

[0117] Perform the mutation operation. On the premise of ensuring the legality of the individual, fine-tune the segmented boundaries to explore the neighboring solution space. The present invention adopts a random boundary offset strategy, combined with a boundary sorting and constraint correction mechanism, to ensure that the mutated individual still meets the requirements of segment length range, non-fracture well segments, and boundary monotonicity. The specific steps are as follows:

[0118] Each individual in the population triggers the mutation operation with a certain probability (mutationRate), randomly selects a position (pos) in the individual's segmented boundary list, applies a random perturbation within a certain range to the boundary value corresponding to this position to generate a new value, and constrains it to the valid interval [totalStart + minStageLength, totalEnd - minStageLength];

[0119] Subsequently, replace the original boundary value and reorder the entire list to ensure that the boundaries are strictly increasing. If the perforation cluster generated after mutation conflicts with the avoidZones, a penalty (return -Infinity) is imposed in the fitness calculation to prompt the subsequent evolution to eliminate this individual.

[0120] Perform iterative optimization (i.e., repeat the previous fitness calculation, selection, crossover, and mutation operations until the maximum number of iterations is reached or the convergence condition is met) to obtain the final segmented and clustered result:

[0121] Calculate the fitness values for all individuals in the current population, and perform selection, crossover, and mutation operations until the maximum number of iterations (maxGenerations) is reached or the convergence condition is satisfied. Update the population after each iteration, record the historical optimal solution (bestSolution), and enter the next iteration.

[0122] Finally, output the segmented boundaries corresponding to bestSolution and the perforation cluster division for each segment, ensuring that the scheme meets the requirements of the segment length range, the number of clusters limit, and the non-fracture well section avoidance (avoidZones) constraint.

[0123] Based on the actual engineering measurement data, calibrate the depth of the optimal segmented and clustered fracturing scheme obtained by the genetic algorithm to ensure the accuracy of the fracturing construction, and obtain the final scheme:

[0124] Divide the perforation clusters from the optimal solution bestSolution of the genetic algorithm according to the clusterLength, ensure that the number of clusters is within the range of [minClustersPerStage, maxClustersPerStage], and avoid the non-fracture well sections (avoidZones).

[0125] Perform depth calibration. For example, compare the depth deviation between the perforation clusters and the formation characteristic interface by combining gamma logging data. If there is a deviation, translate the position of the cluster, or re-anchor the depth range of the cluster based on the absolute depth markers of the casing collars to eliminate the error.

[0126] Output the distribution of the perforation clusters after depth calibration, including the segment number, start and end depths, and cluster details, to ensure the accurate positioning of the target reservoir for the fracturing operation.

[0127] The volume fracturing horizontal well segmentation method based on mechanical specific energy and genetic algorithm of the present invention comprises the following specific steps: First, collect the drilling dynamic data to form a table of drilling dynamic data sets at equal well depth intervals, and calculate the bit torque and weight on bit; Then, calculate the mechanical specific energy of the horizontal well section to obtain a set of mechanical specific energy values along the wellbore; Next, define the constraint conditions such as the number of fracturing segments, segment length range, number of clusters per segment range, and non-fracture well sections of the horizontal well section; Subsequently, generate an initial candidate scheme that meets the segment length constraint and initialize the population; After that, calculate the variance of the mechanical specific energy corresponding to the perforation clusters in each segment as the fitness function, and impose penalties on the schemes that violate the constraints; Then, use the tournament selection method to screen out the individuals with high fitness and perform the selection operation; Generate offspring through the segmented boundary replacement strategy and perform the crossover operation; Randomly adjust the depth of the segmented boundaries and reorder them to perform the mutation operation; Repeat the fitness calculation, selection, crossover, and mutation operations until the maximum number of iterations is reached to complete the iterative optimization; Finally, output the optimal segmented scheme and the perforation cluster distribution, and perform depth calibration to dynamically adjust the well section range.

[0128] The embodiment provides a method for segmenting horizontal wells in volumetric fracturing based on mechanical specific energy and genetic algorithm, and the specific steps are as follows:

[0129] Step 71: Record the drilling dynamic data of the construction well, including but not limited to the hook load (HKLoad), rate of penetration (ROP), mud flow rate (Q), rotary table speed (N), rotary table torque (TSurface), bit diameter (DBit), and standpipe pressure (SPP).

[0130] Step 72: Based on the interval from the starting well depth (totalStart) to the ending well depth (totalEnd) of the horizontal well section, take values of the drilling dynamic data at a certain well depth step within the interval to form a drilling dynamic data set table at equal well depth intervals.

[0131] Step 73: Calculate the bit torque (TBit) and weight on bit (WOB) corresponding to each record in the set table.

[0132] (1) When using a positive displacement motor section, the bit torque TBit = Kt * ΔP, where Kt is the torque output per unit pressure difference of the positive displacement motor, ΔP is the pressure difference of the positive displacement motor in the drilling state, and ΔP can be estimated using the standpipe pressure (SPP). Subtract the standpipe pressure when the bit is lifted off the bottom from the standpipe pressure when the bit is drilling at the bottom. The weight on bit (WOB) can be estimated by measuring the hook load (HKLoad). Subtract the hook load when the bit is lifted off the bottom from the hook load when the bit is drilling at the bottom.

[0133] (2) When not using a positive displacement motor section, the bit torque (TBit) is predicted using a friction torque model. Take the rotary table torque (TSurface) as the input value of the friction torque model and calculate from the wellhead to the bottom to predict the bit torque. The method for estimating the weight on bit (WOB) is the same as that when using a positive displacement motor.

[0134] Step 74: Based on the drilling dynamic data, calculate the mechanical specific energy (MSE) at a certain well depth step within the horizontal well section interval to form a mechanical specific energy data set (mseData).

[0135] Step 75: Set the constraint conditions for the genetic algorithm, including the number of fracturing stages (numStages), the range of stage lengths (minimum stage length, maximum stage length) (minStageLength, maxStageLength), the range of the number of clusters per stage (minimum number of clusters, maximum number of clusters) (minClustersPerStage, maxClustersPerStage), the length of each cluster (clusterLength), and the non-fracturing well sections (avoidZones).

[0136] Step 76: Generate an initial population that satisfies the stage length constraints.

[0137] (1) Set the total number of individuals in the population (populationSize), and set the individual generation rule. Each individual represents a segmentation scheme, which consists of numStages - 1 segmentation boundary depth values, and the boundary values are arranged in ascending order.

[0138] (2) Randomly generate individuals. Starting from the starting depth, generate segmentation boundaries with random step lengths within the range of [minStageLength, maxStageLength]; ensure that the last segmentation boundary does not exceed totalEnd - minStageLength to prevent the length of the last segment from being insufficient; if the number of segments requirement cannot be met during the generation process, supplement the segmentation boundaries to numStages - 1 to ensure the legality of the individuals.

[0139] (3) Repeat step 71 and step 72 until the number of individuals in the initial population is populationSize.

[0140] Step 77: Calculate the fitness of the individuals in the population.

[0141] Step 78: Conduct a selection operation on the population.

[0142] Step 79: Conduct a crossover operation on the population.

[0143] Step 710: Conduct a mutation operation on the population.

[0144] Step 711: Repeat step 77 to step 710 until the number of repetitions reaches the set limit. Record the historical optimal solution (bestSolution) in each iteration process and then enter the next generation iteration. After the iteration ends, output the final optimal solution.

[0145] Step 712: Depth correct the optimal segmentation and clustering scheme obtained by the genetic algorithm based on the actual measured engineering data to ensure the accuracy of the fracturing construction and obtain the final scheme.

[0146] The following further elaborates on the embodiments in combination with specific examples.

[0147] The first step: Record the drilling dynamic data of the wells that have been constructed, including the hook load (HKLoad), rate of penetration (ROP), mud flow rate (Q), rotary table speed (N), rotary table torque (TSurface), bit diameter (DBit), and standpipe pressure (SPP).

[0148] The second step: Organize the drilling dynamic data into equal intervals. As shown in Table 1, it is an example display of the drilling dynamic data corresponding to each meter of well depth interval from 3000m to 4000m.

[0149] Table 1 Parameters of Completed Wells

[0150]

[0151] Step 3: Calculate the bit torque (TBit) and weight on bit (WOB) based on the data in Table 1.

[0152] (1) Assume the section without positive displacement motor (3000 - 3300 m).

[0153] Take the well depth of 3000 m as an example:

[0154] 1) Calculation of TBit. Assume the result of the friction torque model calculation is 85% of TSurface, then TBit = 15500×0.85 = 13175 N·m;

[0155] 2) Calculation of WOB. WOB = 1100−1050 = 50 kN (assuming HKLoad = 1100 kN when the bit is lifted off the bottom).

[0156] (2) Assume the section with positive displacement motor (3300 - 4000 m).

[0157] Take the well depth of 3300 m as an example:

[0158] 1) Calculation of ΔP. ΔP = SPP during drilling - SPP when lifted off = 25−20 = 5 MPa (assuming SPP = 20 MPa when lifted off);

[0159] 2) Calculation of TBit (positive displacement motor differential pressure torque coefficient Kt = 500 N·m / MPa): TBit = Kt×ΔP = 500×5 = 2500 N·m;

[0160] 3) Calculation of WOB: WOB = 1300−1250 = 50 kN (assuming HKLoad = 1300 kN when lifted off).

[0161] Step 4: Calculate the mechanical specific energy. Calculate the mechanical specific energy (MSE) for the section without positive displacement motor and the section with positive displacement motor respectively, using the formula:

[0162]

[0163] Assume the obtained results are as shown in the following table:

[0164] Table 2 Example Table of Mechanical Specific Energy Dataset

[0165]

[0166] Step 5: Set the constraint conditions of the genetic algorithm. To simplify the calculation process in the example, the cluster number constraint condition in the segment length is not considered: numStages = 5; minStageLength = 150; maxStageLength = 300; clusterLength = 10; avoidZones = {{3050, 3060}}; totalStart = 3000; totalEnd = 4000.

[0167] Step 6: Generate the initial population. Assume that the initial population individuals (populationSize) are 4, including the following individuals:

[0168] (1) Individual A: {3150, 3450, 3750, 3850}

[0169] (2) Individual B: {3300, 3550, 3800, 3900}

[0170] (3) Individual C: {3050, 3200, 3400, 3600}

[0171] (4) Individual D: {3100, 3150, 3500, 3800}

[0172] Step 7, Fitness calculation:

[0173] (1) Fitness calculation of Individual A:

[0174] 1) Segmentation interval: Segment 1: 3000 - 3150 meters (150 meters, legal), Segment 2: 3150 - 3450 meters (300 meters, legal), Segment 3: 3450 - 3750 meters (300 meters, legal), Segment 4: 3750 - 3850 meters (100 meters, legal), Segment 5: 3850 - 4000 meters (150 meters, legal).

[0175] 2) Perforation cluster generation, generated according to the cluster length. For example, the interval of Segment 2 is (3150 - 3450 meters), then the clusters are {{3150, 3160}, {3160, 3170},..., {3440, 3450}}. Cluster legality check: Avoid the non-fracturing segment {3050, 3060}, legal.

[0176] 3) Variance calculation: Assume the cluster is 3150 - 3160 meters. According to the mechanical energy ratio in Table 2, the variance is obtained: 24.8. The variance of the cluster 3450 - 3460 meters is: 18.5. Calculate in turn, then the total fitness: -Σ(24.8 + 18.5 +...) = -320.5.

[0177] (2)Calculation of Individual B's Fitness:

[0178] 1) Segmented intervals: Segment 1: 3000 - 3300 meters (300 meters, legal), Segment 2: 3300 - 3550 meters (250 meters, legal), Segment 3: 3550 - 3800 meters (250 meters, legal), Segment 4: 3800 - 3900 meters (100 meters, legal), Segment 5: 3900 - 4000 meters (100 meters, legal).

[0179] 2) Generation of perforation clusters, generated according to the cluster length. For example, for the interval of Segment 3 (3550 - 3800 meters), the clusters are {{3550, 3560}, {3560, 3570},..., {3790, 3800}}. Cluster legality check: Avoid non-fracturing segments {3050, 3060}, legal.

[0180] 3) Variance calculation: Assume the cluster is 3550 - 3560 meters. According to the mechanical energy ratio in Table 2, the variance is 20.1. Calculate sequentially, then the total fitness: -Σ(20.1 +...) = -280.6.

[0181] (3)Calculation of Individual C's Fitness

[0182] Segment 1: 3000 - 3050 meters (50 meters, violation), fitness - Infinity.

[0183] (4)Calculation of Individual D's Fitness

[0184] Segment 2: 3100 - 3150 meters (50 meters, violation), fitness - Infinity.

[0185] The eighth step, selection operation, tournament rule, generate 4 new individuals:

[0186] (1)The first tournament: Select A and B → B wins (higher fitness)

[0187] (2)The second tournament: Select C and D → Randomly select D (both are - Infinity)

[0188] (3)The third tournament: Select B and D → B wins

[0189] (4)The fourth tournament: Select A and C → A wins

[0190] The new population obtained is: B, D, B, A.

[0191] Step 9, Crossover operation. Select parents from the population in the previous step. For example, select B{3300, 3550, 3800, 3900} and A{3150, 3450, 3750, 3850}, and use the crossover strategy of 50% probability for each bit to generate offspring E:

[0192] (1) Bit 1: Select 3300 of B; Bit 2: Select 3450 of A; Bit 3: Select 3800 of B; Bit 4: Select 3850 of A. Finally, obtain offspring E: {3300, 3450, 3800, 3850}.

[0193] (2) Section length check: Section 3: 3450 - 3800 meters (350 meters, violation) → Need to be corrected by mutation.

[0194] Step 10, Mutation operation. Adjust the generated offspring. For example, adjust offspring E to:

[0195] (1) Perturbation boundary: The 3rd boundary 3800 → 3750 (section length = 300 meters, legal).

[0196] (2) The corrected offspring E: {3300, 3450, 3750, 3850}.

[0197] Step 11, Record the optimal result after the mutation operation, and then start iterative optimization. Record the optimal result after each iteration. Assume that the number of iterations is set to 15 times. After the iteration is completed, the optimal result selected from all the results is: {3300, 3550, 3750, 3900}, and the clusters are arranged according to a section length of 10m.

[0198] Step 12, Well depth correction. According to the logging data, the boundary at 3550 meters is corrected to 3552 meters. Then the final sectioning result is {3300, 3552, 3800, 3900}.

Claims

1. A method for segmenting and clustering horizontal wells in volume fracturing based on mechanical specific energy and genetic algorithm, characterized in that, It includes the following steps: Step 1: Collect drilling dynamic data, form a drilling dynamic data set table at equal well depth intervals according to the well depth step length, and calculate the bit torque and weight on bit. Step 2: Calculate the mechanical specific energy of the horizontal section based on the drilling dynamic data set table to obtain a set of mechanical specific energy values along the wellbore. Step 3: Generate an initial population based on the set constraint conditions, and obtain the fitness of each individual in the initial population. Step 4: Randomly select a set number of individuals from the initial population to form a candidate set, and use the tournament selection method to screen out the individuals with high fitness in the candidate set. Generate offspring through the segmented boundary replacement strategy for crossover operation; randomly adjust the segmented boundary depth and reorder for mutation operation; repeat the fitness calculation, selection, crossover, and mutation operations until the maximum number of iterations is reached to complete the iterative optimization. Step 5: Output the optimal segmentation scheme and perforation cluster distribution, and perform drilling construction according to the optimal segmentation scheme and perforation cluster distribution. The generation of the initial population based on the set constraint conditions includes: The constraint conditions include the number of fracturing stages, stage length range, number of clusters per stage range, length of each cluster, and non-fracturing well sections; the generation of the initial population includes: Step 31: Set the total number of individuals in the population. Each individual represents a segmentation scheme, which consists of fracturing stage number - 1 segmented boundary depth values, and the boundary values are arranged in ascending order. Step 32: Randomly generate individuals. Starting from the starting depth, generate segmented boundaries according to a random step length, and the step length range is within the stage length range. Step 33: Repeat Step 31 and Step 32 until the number of individuals in the initial population is the set total number of individuals in the population. The obtaining of the fitness of each individual in the initial population includes: Legality verification of the individual's segmented boundaries. First, perform a segmented quantity check to verify whether the number of segmented boundaries of the individual is fracturing stage number - 1; if not, directly return -Infinity; then perform a stage length constraint check: traverse all fracturing stages and calculate whether the length of each stage satisfies minStageLength ≤ stage length ≤ maxStageLength; record the number of violations stageViolations, and the calculation method of the number of violations is: Perforation cluster division and verification of the individual. First, perform perforation cluster generation. For each fracturing stage, divide the perforation clusters according to the length of each cluster; and the length of each cluster is greater than half of the length of each cluster; then perform a cluster number constraint check, count the number of perforation clusters in each stage, and if it exceeds the number of clusters per stage range, record the number of violations. Perform fitness calculation. First, perform data mapping, map the well depth range of the perforation clusters to the mechanical specific energy data set, extract the mechanical specific energy values within the corresponding well depth range for each perforation cluster, and calculate the cluster variance. Cluster variance = Variance(MSEs, MSEs+1, …, MSEe) where s and e are the data indices corresponding to the starting and ending depths of the cluster respectively. If the cluster length is invalid, the variance is set to 0. Finally, sum up the cluster variances of all perforation clusters and take the negative as the fitness; if there are any stage or cluster violations, return -Infinity to impose a penalty. The final fitness formula is: 。 2. The method for sectional and clustered fracturing of horizontal wells in volume fracturing based on mechanical specific energy and genetic algorithm according to claim 1, wherein Form a drilling dynamic data set table at equal well depth intervals according to the well depth step length, and calculate the bit torque and weight on bit, including: Based on the interval from the starting well depth to the ending well depth of the horizontal well section, take values of the drilling dynamic data at the set well depth step length within the interval to form a drilling dynamic data set table at equal well depth intervals; calculate the bit torque and weight on bit corresponding to each record based on the drilling dynamic data set table: Calculate the bit torque and weight on bit for the well sections with and without the use of the positive displacement motor respectively; For the well section with the positive displacement motor, the bit torque is TBit = Kt*ΔP, where Kt is the output torque per unit differential pressure of the positive displacement motor, and ΔP is the differential pressure of the positive displacement motor in the drilling state; where ΔP is estimated using the standpipe pressure, and the standpipe pressure when the bit is drilling at the bottom of the well is subtracted from the standpipe pressure when the bit is lifted off the bottom of the well; the weight on bit is estimated through the measured hook load, and the hook load when the bit is lifted off the bottom of the well is subtracted from the hook load when the bit is drilling at the bottom of the well; For the well section without the use of the positive displacement motor, the bit torque is predicted using the friction torque model, and the rotary table torque is used as the input value of the friction torque model to calculate the bit torque from the wellhead to the bottom of the well, and the estimation method of the weight on bit is the same as that when using the positive displacement motor.

3. The method for segmenting and clustering horizontal wells in volume fracturing based on mechanical specific energy and genetic algorithm according to claim 1, wherein Calculate the mechanical specific energy of the horizontal well section according to the drilling dynamic data set table to obtain a set of mechanical specific energy values along the wellbore, including: Based on the drilling dynamic data, calculate the mechanical specific energy at the set well depth step length within the horizontal well section interval to form a mechanical specific energy data set, and the mechanical specific energy is calculated using the following formula: where Dbit is the bit diameter, ROP is the rate of penetration, Kn is the output rotational speed per unit mud flow rate of the positive displacement motor, Q is the mud flow rate, TBit is the bit torque, N is the rotary table speed, and if there is no positive displacement motor, Kn is taken as 0.

4. The method for segmenting and clustering horizontal wells in volume fracturing based on mechanical specific energy and genetic algorithm according to claim 1, wherein Randomly select a set number of individuals from the initial population to form a candidate set, and use the tournament selection method to screen out the individuals with high fitness in the candidate set, including: Step 41: Randomly select a set number of individuals from the population to form a candidate set; compare the fitness values of the candidate individuals, and select the individual with the highest fitness as the winner; Step 42: Repeat Step 41 until the size of the new population reaches the total number of individuals in the set population.

5. The method for sectional and clustered fracturing of horizontal wells in volumetric fracturing based on mechanical specific energy and genetic algorithm according to claim 1, characterized in that Generate offspring through the segmented boundary replacement strategy and perform crossover operations, including: Randomly select two parent individuals parent1 and parent2 from the population. For their segmented boundary lists, traverse each segmented boundary position, and generate an offspring child by selecting the corresponding boundary value of parent1 or parent2 with a set probability; reorder the boundary values of the offspring to ensure monotonic increase, and constrain the boundary values of child within the valid interval.

6. The method for sectional and clustered fracturing of horizontal wells in volumetric fracturing based on mechanical specific energy and genetic algorithm according to claim 1, wherein Randomly adjust the segmented boundary depth and reorder it to perform mutation operations, including: Each individual in the population triggers the mutation operation with a set probability, randomly select a certain position in the individual's segmented boundary list, apply a random perturbation within the set range to the boundary value corresponding to this position to generate a new value, and constrain it to the valid interval; replace the original boundary value and reorder the entire list. If the perforation cluster generated after mutation conflicts with the non-fracture well section, a penalty is imposed in the fitness calculation.

7. The method for segmenting and clustering horizontal wells in volume fracturing based on mechanical specific energy and genetic algorithm according to claim 1, wherein The described optimal output segmentation scheme and perforation cluster distribution include: dividing the perforation clusters according to the length of each cluster from the optimal solution of the genetic algorithm, ensuring that the number of clusters is within the range of the number of clusters per segment and avoiding non-fracturing well sections; performing depth calibration, including comparing the depth deviation between the perforation clusters and the formation characteristic interface by combining gamma logging data, and if there is a deviation, translating the position of the cluster or re-anchoring the well depth range of the cluster based on the absolute depth marking of the casing collar data to eliminate errors; outputting the perforation cluster distribution after depth calibration, including the segmentation number, start and end depths, and cluster details, to ensure accurate positioning of the target reservoir for the fracturing operation.

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