CN111382543A - Offshore cluster well drilling sequence optimization method based on improved ant colony algorithm - Google Patents
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Abstract
The invention discloses an offshore cluster well drilling sequence optimization method based on an improved ant colony algorithm, which is characterized by comprising the following steps of S1, defining underwater well mouth layout; s2, optimizing the drilling sequence based on the improved ant colony algorithm, and outputting a result through iterative computation; s3, screening results of the ant colony algorithm improvement calculation based on the drilling sequence screening criterion, and outputting the optimal drilling sequence result; according to the method, the drilling sequence is optimized by adopting the improved ant colony algorithm, the result of the improved ant colony algorithm is screened by adopting the operation safety criterion and the operation efficiency criterion, the drilling safety is ensured, and the drilling efficiency is obviously improved.
Description
Technical Field
The invention relates to an offshore cluster well drilling sequence optimization method based on an improved ant colony algorithm, and belongs to the field of efficient drilling of tension leg platform cluster wells.
Background
The global resource shortage increases the demand for the development of ocean oil and gas resources, and the resource exploitation in the deepwater field becomes the trend of resource development. Tension Leg Platforms (TLPs) have excellent mobility and stability, can resist severe environmental loads, and are increasingly becoming the primary platform for deep-sea oil and gas field development. Different from a semi-submersible drilling platform, the tension leg platform adopts cluster underwater well heads, the number of the well heads is large, the distribution is dense, the wake effect of the stand pipe is easily caused, meanwhile, a drilling scheme from near to far is generally adopted in the drilling process, the drilling efficiency is low, the stand pipes are easily interfered due to the shielding effect among the stand pipes, and the operation safety cannot be guaranteed.
Aiming at the problems that TLP drilling efficiency is low, operation safety cannot be guaranteed, and the drilling sequence needs to be optimized on the basis of riser collision prevention. Considering the distribution characteristics of TLP cluster well heads, the optimization of the drilling sequence actually optimizes the drilling path, the ant colony algorithm adopts a positive feedback mechanism on solving the path optimization problem, the calculation speed is high, but the basic ant colony algorithm has poor optimization convergence speed and is easy to fall into local optimization.
Therefore, it is urgently needed to develop an improved ant colony algorithm to optimize the drilling sequence of the TLP cluster well, improve the drilling efficiency of the cluster well, save the drilling cost and ensure the operation safety.
Disclosure of Invention
In view of the above problems, the present invention aims to provide an offshore cluster well drilling sequence optimization method based on an improved ant colony algorithm, which can improve the cluster well drilling efficiency, save the drilling cost, and enhance the operation safety.
In order to achieve the purpose, the invention adopts the following technical scheme that the offshore cluster well drilling sequence optimization method based on the improved ant colony algorithm comprises the following steps:
s1, defining the layout of the underwater wellhead;
s2, optimizing the drilling sequence based on the improved ant colony algorithm, and outputting a result through iterative computation;
and S3, screening the results of the improved ant colony algorithm calculation based on the drilling sequence screening criteria, and outputting the optimal drilling sequence result.
Further, in step S1, the determining of the underwater wellhead layout means that when the platform device is determined, the platform wellhead layout is known, and there is a certain corresponding relationship between the underwater wellhead layout and the platform wellhead distribution, the underwater wellhead layout is determined.
Further, in step S2, the improved ant colony algorithm iterative computation process is as follows:
(1) initializing drilling sequence optimization algorithm parameters
The number m of ants in the ant colony; the number n of cluster well heads; dij(i, j ═ 1,2,3, …, n) is the path distance between wellhead i and wellhead j, representing the path distance connecting i j the two wellheads; tau isij(t) (i, j ═ 1,2,3 …, n) is the pheromone concentration on the routes of the wellheads i and j at the time t; set initial valuesThe concentration of pheromones on the connection path between the well heads is the same, and is set to be tauij(0)＝τmaxThe system comprises a pheromone volatilization factor rho, a pheromone importance factor α, a factor β for inspiring the importance of a function, and iteration times;
(2) constructing a drilling sequence optimization solution space
Setting each ant to randomly select a well mouth as an initial well mouth, calculating the transition probability of each ant k (k is 1,2,3, … m), and determining the next well mouth according to the probability until the path sequence of all ants is recorded to form a path recording table; collecting the optimal path in the path record table in real time;
(3) updating the pheromone;
after all ants complete all well head traversals for the first time, no pheromone, tau, is leftij(0)＝τmax；
After the first traversal is completed, ants with the shortest paths are selected, the ants can release pheromones in the next traversal, and then after each traversal is completed, the pheromone concentration on the routes between every two wellheads is updated according to the pheromones released by the ants with the shortest paths;
(4) judging whether stagnation exists or not and trapping in local optimum;
firstly, setting a pheromone detection value, calculating pheromones of all paths leading to other wellheads of each wellhead, determining the maximum value in the pheromones of all paths leading to other wellheads of each wellhead, comparing the maximum value with the pheromone detection value, and if the maximum value is greater than the pheromone detection value, indicating that the path is counted as an effective path, otherwise, the path is an invalid path; when the detected active paths are below a certain number, it is determined that the algorithm has stuck,
if the well is stopped, resetting pheromones of all paths, enhancing the concentration of the pheromones of all paths, namely increasing the initial pheromone concentration value among the well mouths, and repeating the step (3); if the stagnation is not caused, the process proceeds to step S2 (5);
(5) judging whether the iteration times are reached;
if the iteration times are not reached, clearing the path record table, adding 1 to the iteration times, and repeating the steps (2) to (4); and if the iteration times are reached, outputting a result of the improved ant colony algorithm.
Further, in (2) of step S2, the transition probability calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
the probability that the ant k is transferred from the well mouth i to the well mouth j at the moment t, ηij(t) represents the heuristic function,
the significance of this is the desired degree of movement of the ant from wellhead i to wellhead j;
the significance of this is the desired degree of movement of the ant from wellhead i to wellhead s; tau isis(t) pheromone concentrations on the routes of a wellhead i and a wellhead s at the moment t; allkRepresents the set of wells that ant k will access, and s represents any well that ant k will access.
Further, in (3) of step S2, the update formula of the pheromone is as follows:
in the formula, LkFinally selecting the total length of the path for the kth ant, namely the drilling distance; l represents the length of the shortest path; q is constant, representing the total amount of pheromone released by an ant after completing one complete path search;
representing the information concentration released by the ants corresponding to the shortest path on the connection path of the well mouth i and the well mouth j; tau isij(t +1) represents the pheromone concentration on the routes of the well i and the well j at the time of t + 1.
Further, in step S2 (3), the pheromone between wells is set to a range, and an upper limit value and a lower limit value are set, and when the pheromone between wells is increased to or above the upper limit value at the time of updating the pheromone, the pheromone between wells is automatically set to the upper limit value; when the well-to-well pheromone falls below the lower limit, the well-to-well pheromone is automatically set to the lower limit.
Further, in step S3, the drilling order screening criteria include operation safety criteria and operation efficiency criteria;
the operation safety criterion comprises a mechanical property limiting criterion and a collision criterion; wherein the mechanical property limiting criteria include (1) no riser buckling is allowed to occur; (2) the riser bending moment is not allowed to exceed a limit value; crash guidelines include (1) no riser crash is allowed to occur at any environmental load; (2) the space between the vertical pipes in the lowering process is required to meet the operation space of the equipment; the operating efficiency criterion is that the drilling path is shortest.
By adopting the technical scheme, the invention has the following advantages: 1. according to the method, the drilling sequence is optimized by adopting the improved ant colony algorithm, the result of the improved ant colony algorithm is screened by adopting the operation safety criterion and the operation efficiency criterion, the drilling safety is ensured, and the drilling efficiency is obviously improved. 2. The improved ant colony algorithm forms a path recording table by constructing a drilling sequence optimization solution space, collects an optimal path in real time, does not leave pheromone after the first traversal is completed, releases the pheromone in the next traversal by the ant with the shortest path, updates the concentration of the pheromone on the routes among the wellheads according to the pheromone released by the ant with the shortest path after each traversal is completed, accelerates the convergence speed, performs stagnation detection before the next iteration is completed after one iteration is completed, jumps out a local optimal path in time, obtains a global optimal path, and improves the drilling efficiency.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic illustration of a well sequence screening criteria of the present invention;
FIG. 3 is a schematic illustration of an underwater wellhead distribution of an embodiment of the present invention;
FIG. 4 is a modified ant colony algorithm drilling sequence optimization roadmap according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the objects, features and advantages of the invention can be more clearly understood. It should be understood that the embodiments shown in the drawings are not intended to limit the scope of the present invention, but are merely intended to illustrate the spirit of the technical solution of the present invention.
As shown in fig. 1, the invention provides a method for optimizing a drilling sequence of an offshore cluster well based on an improved ant colony algorithm, which comprises the following steps:
s1, defining the layout of the underwater wellhead;
s2, optimizing the drilling sequence based on the improved ant colony algorithm, and outputting a result through iterative computation;
and S3, screening the results of the improved ant colony algorithm calculation based on the drilling sequence screening criteria, and outputting the optimal drilling sequence result.
Further, in step S1, the determining of the underwater wellhead layout means that after the platform device is determined, the platform wellhead layout is known, and a certain corresponding relationship exists between the underwater wellhead layout and the platform wellhead distribution, so that the underwater wellhead layout is determined, the underwater wellhead layout is generally arranged at a certain interval, and the underwater wellheads are densely distributed.
Further, in step S2, the improved ant colony algorithm iterative computation process is as follows:
(1) initializing drilling sequence optimization algorithm parameters;
namely, inputting improved ant colony algorithm parameters, including: the number m of ants in the ant colony; the number n of cluster well heads; dij(i, j ═ 1,2,3, …, n) is the path distance between wellhead i and wellhead j, representing the path distance connecting i j the two wellheads, typically a two-point straight line distance; tau isij(t) (i, j ═ 1,2,3 …, n) is the pheromone concentration on the routes of the wellheads i and j at the time t; setting the pheromone concentration on the initial connection path between the wellheadsDegree of same, can be set as tauij(0)＝τmaxThe information element comprises a factor α for the importance of the information element, a factor β for the importance of the elicitation function, a factor rho for the volatility of the information element, and the number of iterations.
Wherein, the selection range of the parameters for improving the ant colony algorithm is as the following table,
wherein rho represents the volatilization degree of the pheromone, the rho is related to the global search capability and the convergence rate of the algorithm, and the pheromone in the iterative cycle cannot be completely evaporated, so that a smaller evaporation rate rho (namely a pheromone volatilization factor) is selected.
(2) Constructing a drilling sequence optimization solution space;
setting each ant to randomly select a well mouth as an initial well mouth, calculating a transition probability for each ant k (k is 1,2,3, … m), determining the next well mouth according to the probability until the path sequence of all ants is recorded (namely all drilling sequences are recorded), forming a path record table, and collecting the optimal path (shortest drilling distance) in the path record table in real time;
wherein, the transition probability calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
the probability that the ant k is transferred from the well mouth i to the well mouth j at the moment t, ηij(t) represents the heuristic function,
the significance of this is the desired degree of movement of the ant from wellhead i to wellhead j;
the significance of this is the desired degree of movement of the ant from wellhead i to wellhead s;τis(t) pheromone concentrations on the routes of a wellhead i and a wellhead s at the moment t; allkRepresents the set of wells that ant k will access, and s represents any well that ant k will access.
(3) Updating pheromones
The significance of pheromone updating is that a basis is provided for subsequent ants to select intersections, and the frequency of pheromone updating determines the real-time performance and result accuracy of the ant colony algorithm.
Wherein, after all ants complete all well head traversals for the first time, namely, the construction of the well drilling sequence optimization solution space is completed for the first time, no pheromone is left, tauij(0)＝τmax；
After the first traversal is completed, ants with the shortest paths are selected, the ants can release pheromones in the next traversal, and then after each traversal is completed, the pheromone concentration on the routes between every two wellheads is updated according to the pheromones released by the ants with the shortest paths;
the update formula of pheromone is as follows:
in the formula, LkFinally selecting the total length of the path for the kth ant, namely the drilling distance; l represents the length of the shortest path; q is constant, representing the total amount of pheromone released by an ant after completing one complete path search;
representing the information concentration released by the ants corresponding to the shortest path on the connection path of the well mouth i and the well mouth j; tau isij(t +1) represents the pheromone concentration on the routes of the well mouth i and the well mouth j at the moment of t + 1;
(4) judging whether stagnation exists or not and trapping in local optimum;
after a certain number of iterations, if the best result is not changed, checking whether the algorithm is stuck to a stall, and the specific process comprises the following steps:
firstly, setting a pheromone detection value (usually selecting a low value, 0.05 or 0.02), calculating pheromones of all paths leading to other wellheads for each wellhead, determining the maximum value in the pheromones of all paths leading to other wellheads for each wellhead, comparing the maximum value with the pheromone detection value, and judging that the path is a valid path if the maximum value is greater than the pheromone detection value, otherwise, the path is an invalid path; when the detected active path is below a certain number, which can be set according to the number of wellheads, it is determined that the algorithm has stuck,
if the well is stopped, resetting pheromones of all paths, enhancing the concentration of the pheromones of all paths, namely increasing the initial pheromone concentration value among the well mouths, and repeating the step (3); if the stagnation is not caused, the process proceeds to step S2 (5);
(5) judging whether the iteration times are reached;
if the iteration times are not reached, emptying a path record table (the path record table refers to the recorded ant path, namely the drilling path), adding 1 to the iteration times, and repeating the steps (2) to (4); and if the iteration times are reached, outputting a result of the improved ant colony algorithm.
Further, in step S2 (3), the pheromone between wells is set to a range, an upper limit value and a lower limit value are set, and the condition that the pheromone between wells is too high or too low is avoided, and when the pheromone between wells is increased to or decreased to or below the upper limit value at the time of updating the pheromone, the pheromone between wells is automatically set to the upper limit value or the lower limit value.
Further, as shown in fig. 2, in step S3, the drilling order screening criteria include operation safety criteria and operation efficiency criteria;
the operation safety criterion comprises a mechanical property limiting criterion and a collision criterion; wherein the mechanical property limiting criteria include (1) no riser buckling is allowed to occur; (2) the riser bending moment is not allowed to exceed a limit value; crash guidelines include (1) no riser crash is allowed to occur at any environmental load; (2) the space between the vertical pipes in the lowering process should meet the operation space of equipment such as an ROV;
the operating efficiency criterion is that the drilling path is shortest.
The invention will now be illustrated with reference to specific examples;
s1, defining the layout of the underwater wellhead:
after the drilling platform is determined, the distribution of underwater well heads relative to the platform is determined, as shown in fig. 3, the underwater well heads adopt a cluster well distribution design, the well heads are densely distributed, the interval between the well heads is L, and well head numbers 1,2,3, … and 45 are defined;
s2, optimizing the drilling sequence based on the improved ant colony algorithm, and outputting the result through iterative computation:
(1) initializing drilling sequence optimization algorithm parameters;
setting the number m of ants as 50, the number n of wellheads as 45, and the path distance between a marking wellhead i and a marking wellhead j as dij(i, j ═ 1,2,3,. n), recording the information concentration tau on the paths of the well head i and the well head j at the time tij(t) (i, j is 1,2,3 …, n), setting pheromone importance factor α to 1, heuristic function importance factor β to 5, maximum number of iterations to 50, and randomly choosing a starting position.
(2) Constructing a drilling sequence optimization solution space;
setting each ant to randomly select a wellhead as an initial wellhead, and calculating the transition probability for each ant k (k is 1,2,3, … m):
determining the next well head by the transition probability until the path sequence of all ants is recorded (namely all well drilling sequences are recorded), forming a path record table and collecting the optimal path (shortest well drilling distance).
(3) Updating pheromones
After all ants complete all well head traversals for the first time, namely the construction of the well drilling sequence optimization solution space is completed for the first time, no pheromone, tau, is leftij(0)＝τmax(ii) a Setting the upper limit value to be 0.99 and the lower limit value to be 0.01 for the pheromone between wells; and updating the concentration of the pheromone on each inter-wellhead route according to the pheromone released by the ant with the shortest route:
(4) judging whether stagnation exists or not and trapping in local optimum;
firstly, setting a pheromone detection value to be 0.05, calculating pheromones of all paths from each wellhead to other wellheads, and when the maximum value in the pheromones is larger than the pheromone detection value, calculating the path as an effective path; when the detected effective paths are lower than a certain number (the number can be set according to the number of well heads), judging that the algorithm is stuck to be stopped, if the algorithm is stuck to be stopped, resetting pheromones of all paths, enhancing the concentration of the pheromones of all paths, and repeating the step (3); if the stagnation is not caused, the process proceeds to step S2 (5);
(5) judging whether the iteration times are reached;
if the iteration times are not reached, clearing the path record table, adding 1 to the iteration times, and repeating the steps (2) to (4); and if the iteration times are reached, outputting a result of the improved ant colony algorithm.
S3, screening the results of the improved ant colony algorithm calculation based on the drilling sequence screening criteria, and outputting the optimal drilling sequence result:
screening out the optimal path meeting the mechanical property limiting criterion and the collision criterion from the result of the improved ant colony algorithm output in the step S2 (5), namely, screening out the optimal path as the optimal result of the drilling sequence.
A well drilling sequence optimization roadmap for the cluster well is obtained, and as shown in FIG. 4, an initial well head is arbitrarily selected (e.g., the well head 13 is the initial well head), and the optimal well drilling path is determined to be 13 → 12 → 17 → 18 → 19 → 14 → 15 → 20 → 25 → 24 → 29 → 34 → 35 → 30 → 28 → 33 → 32 → 27 → 26 → 21 → 22 → 23 → 16 → 11 → 43 → 38 → 39 → 40 → 45 → 44 → 36 → 41 → 42 → 31 → 9 → 10 → 5 → 4 → 3 → 2 → 6 → 7 → 8 → 13 (the sequence number is the well head number).
The present invention has been described with reference to the above embodiments, and the structure, arrangement, and connection of the respective members may be changed. On the basis of the technical scheme of the invention, the improvement or equivalent transformation of the individual components according to the principle of the invention is not excluded from the protection scope of the invention.
Claims (7)
1. An offshore cluster well drilling sequence optimization method based on an improved ant colony algorithm is characterized by comprising the following steps of:
s1, defining the layout of the underwater wellhead;
s2, optimizing the drilling sequence based on the improved ant colony algorithm, and outputting a result through iterative computation;
and S3, screening the results of the improved ant colony algorithm calculation based on the drilling sequence screening criteria, and outputting the optimal drilling sequence result.
2. The offshore cluster well drilling sequence optimization method based on the improved ant colony algorithm as claimed in claim 1, wherein:
in step S1, the determining of the underwater wellhead layout means that when the platform device is determined, the platform wellhead layout is known, and there is a certain correspondence between the underwater wellhead layout and the platform wellhead distribution, the underwater wellhead layout is determined.
3. The method for optimizing offshore cluster well drilling sequence based on the improved ant colony algorithm as claimed in claim 1, wherein in step S2, the iterative calculation process of the improved ant colony algorithm is as follows:
(1) initializing drilling sequence optimization algorithm parameters
The number m of ants in the ant colony; the number n of cluster well heads; dij(i, j ═ 1,2,3, …, n) is the path distance between wellhead i and wellhead j, representing the path distance connecting the two wellheads ij; tau isij(t) (i, j ═ 1,2,3 …, n) is the pheromone concentration on the routes of the wellheads i and j at the time t; the initial concentration of pheromone on the connection path between the wellheads is set to be the same, and is set to be tauij(0)＝τmaxThe system comprises a pheromone volatilization factor rho, a pheromone importance factor α, a factor β for inspiring the importance of a function, and iteration times;
(2) constructing a drilling sequence optimization solution space
Setting each ant to randomly select a well mouth as an initial well mouth, calculating the transition probability of each ant k (k is 1,2,3, … m), and determining the next well mouth according to the probability until the path sequence of all ants is recorded to form a path recording table; collecting the optimal path in the path record table in real time;
(3) updating the pheromone;
after all ants complete all well head traversals for the first time, no pheromone, tau, is leftij(0)＝τmax；
After the first traversal is completed, ants with the shortest paths are selected, the ants can release pheromones in the next traversal, and then after each traversal is completed, the pheromone concentration on the routes between every two wellheads is updated according to the pheromones released by the ants with the shortest paths;
(4) judging whether stagnation exists or not and trapping in local optimum;
firstly, setting a pheromone detection value, calculating pheromones of all paths leading to other wellheads of each wellhead, determining the maximum value in the pheromones of all paths leading to other wellheads of each wellhead, comparing the maximum value with the pheromone detection value, and if the maximum value is greater than the pheromone detection value, indicating that the path is counted as an effective path, otherwise, the path is an invalid path; when the detected active paths are below a certain number, it is determined that the algorithm has stuck,
if the well is stopped, resetting pheromones of all paths, enhancing the concentration of the pheromones of all paths, namely increasing the initial pheromone concentration value among the well mouths, and repeating the step (3); if the stagnation is not caused, the process proceeds to step S2 (5);
(5) judging whether the iteration times are reached;
if the iteration times are not reached, clearing the path record table, adding 1 to the iteration times, and repeating the steps (2) to (4); and if the iteration times are reached, outputting a result of the improved ant colony algorithm.
4. The offshore cluster well drilling sequence optimization method based on the improved ant colony algorithm as claimed in claim 3, wherein in step S2 (2), the transition probability calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
the probability that the ant k is transferred from the well mouth i to the well mouth j at the moment t, ηij(t) represents the heuristic function,
the significance of this is the desired degree of movement of the ant from wellhead i to wellhead j;
the significance of this is the desired degree of movement of the ant from wellhead i to wellhead s; tau isis(t) pheromone concentrations on the routes of a wellhead i and a wellhead s at the moment t; allkRepresents the set of wells that ant k will access, and s represents any well that ant k will access.
5. The offshore cluster well drilling sequence optimization method based on the improved ant colony algorithm as claimed in claim 3, wherein in step S2 (3), the pheromone updating formula is as follows:
in the formula, LkFinally selecting the total length of the path for the kth ant, namely the drilling distance; l represents the length of the shortest path; q is constant, representing the total amount of pheromone released by an ant after completing one complete path search;
representing the information concentration released by the ants corresponding to the shortest path on the connection path of the well mouth i and the well mouth j; tau isij(t +1) represents the well at time t +1Pheromone concentration on the route of port i and well j.
6. The offshore cluster well drilling sequence optimization method based on the improved ant colony algorithm as claimed in claim 3, wherein:
in step S2 (3), the pheromone between wells is set to a range, and an upper limit value and a lower limit value are set, and when the pheromone between wells is increased to or above the upper limit value at the time of updating the pheromone, the pheromone between wells is automatically set to the upper limit value; when the well-to-well pheromone falls below the lower limit, the well-to-well pheromone is automatically set to the lower limit.
7. The offshore cluster well drilling sequence optimization method based on the improved ant colony algorithm as claimed in claim 1, wherein:
in step S3, the drilling order screening criteria include operational safety criteria and operational efficiency criteria;
the operation safety criterion comprises a mechanical property limiting criterion and a collision criterion; wherein the mechanical property limiting criteria include (1) no riser buckling is allowed to occur; (2) the riser bending moment is not allowed to exceed a limit value; crash guidelines include (1) no riser crash is allowed to occur at any environmental load; (2) the space between the vertical pipes in the lowering process is required to meet the operation space of the equipment; the operating efficiency criterion is that the drilling path is shortest.
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