Promising Regions Of Search Space Research Articles

Life-Cycle Gradient-Based Production Optimization Including Well-Shutoff Option with Least-Squares Support Vector Regression

Summary In this work, we focus on well-shutoff/well-control optimization, which enables shutting off a production well or injection well to be part of the well-control optimization within a net present value (NPV) formulation that includes the operation expenditures (OPEX) if the well is not economical to produce or inject. For this purpose, we formulate an objective function for the NPV by introducing a production time fraction as the design variable to shut off a production well over its total production life, divided into a fixed number of cycles. Unlike the previous studies, we use a shutoff period within each cycle instead of shutting it off with a bottomhole pressure (BHP) control, where the BHPs for each producer are part of the optimization variables. When BHPs are considered control optimization variables with an NPV having OPEX, the NPV could be discontinuous for BHPs. To avoid this problem, we built a continuously differentiable proxy function for NPV using least-square support vector regression (LS-SVR). We use linear equality constraints so that the sums of the lengths of the cycles at each producer and injector are equal to the life of the production. Thus, we do not need to truncate the size of the last cycle, as in the previous studies, which may lead to suboptimal solutions. We use a simulator-based optimization method with stochastic simplex approximate gradient (StoSAG) and a machine learning-based (LS-SVR) optimization method to solve such an optimization problem. We update the LS-SVR proxy during optimization so that the updated proxy remains predictive toward promising regions of search space during the optimization. We compare the performance of the proposed LS-SVR-based iterative sampling refinement (ISR) method with the StoSAG-based and the finite difference (FD)-based optimization methods. To demonstrate the applicability of our proposed methodologies, we consider a synthetic example of a waterflooding process in a tight oil reservoir with two water injectors and four producers. Results show that the LS-SVR-based optimization method is at least three to seven times more computationally efficient than the StoSAG-based optimization method using a high-fidelity numerical simulator. However, we observe that the size and sampling of the training data, as well as the selection of bound constraints for the well controls, influence the performance of the LS-SVR-based optimization method. Designing multiple shutoffs and making cycle lengths unknown are found to be ineffective compared to single shutoff cases, as they yield lower NPVs.

Life-cycle production optimization with nonlinear constraints using a least-squares support-vector regression proxy

When nonlinear constraints such as field liquid or water production rate, injection pressures, etc., as functions of time need to be honored in addition to linear ones, the life-cycle production optimization problem, a component of a closed loop reservoir management, becomes challenging and computationally expensive to perform using a high-fidelity reservoir simulator with the existing gradient-based methods using the adjoint or stochastic approximate gradient methods. Therefore, the objective of this study is to present computationally efficient methods for deterministic production optimization under nonlinear constraints using a kernel-based machine learning method where the cost function is the net present value (NPV). We use the least-squares support-vector regression (LSSVR) to approximate the NPV function. To achieve computational efficiency, we generate a set of output values of the NPV and nonlinear constraint functions, which are field liquid production rate (FLPR) and water production rate (FWPR) in this study, by running the high-fidelity simulator for a broad set of input design variables (well controls) and then using the collection of input/output data to train LSSVR proxy models to replace the high-fidelity simulator to compute NPV and nonlinear state constraint functions during iterations of sequential quadratic programming (SQP). To obtain improved (higher) estimated optimal NPV values, we use the existing so-called iterative sampling refinement (ISR) method to update the LSSVR proxy so that the updated proxy remains predictive toward promising regions of search space during the optimization. Direct and indirect ways of constructing LSSVR-based NPVs as well as different combinations of input data, including nonlinear state constraints and/or the bottomhole pressures (BHPs) and water injection rates, are tested as alternative feature spaces. The results obtained from our proposed LSSVR-based optimization methods are compared with those obtained from our in-house stochastic simplex approximate gradient (StoSAG)-based line-search SQP programming (LS-SQP-StoSAG) algorithm that uses directly a high-fidelity simulator to compute the gradients of the objective function and the nonlinear state functions with StoSAG for the Brugge reservoir model. The results show that nonlinear constrained optimization with the LSSVR ISR with SQP is computationally 3.25 fold more efficient than LS-SQP-StoSAG. In addition, the results show that constructing NPV indirectly using the field liquid and water rates for a waterflooding problem where inputs come from LSSVR proxies of the nonlinear state constraints requires significantly fewer training samples than the method constructing NPV directly from the NPVs computed from a high-fidelity simulator. LSSVR has advantages for its computational efficiency which is the main goal in our research, and robustness against overfitting especially for the cases where we have limited data. However, DNNs and random forest require large training size and require much more computational resources and longer training times. Also, Random Forests and Gradient Boosting Machines can be prone to overfitting and become computationally intensive.

Sentiment Classification Using Two Effective Optimization Methods Derived From The Artificial Bee Colony Optimization And Imperialist Competitive Algorithm

AbstractArtificial bee colony (ABC) optimization and imperialist competitive algorithm (ICA) are two famous metaheuristic methods. In ABC, exploration is good because each bee moves toward random neighbors in the first and second phases. In ABC, exploitation is poor because it does not try to examine a promising region of search space carefully to see if it contains a good local minimum. In this study, ICA is considered to improve ABC exploitation, and two novel swarm-based hybrid methods called ABC–ICA and ABC–ICA1 are proposed, which combine the characteristics of ABC and ICA. The proposed methods improve the evaluations results in both continuous and discrete environments compared to the baseline methods. The second method improves the first optimization method as well. Feature selection can be considered to be an optimization problem because selecting the appropriate feature subset is very important and the action of appropriate feature selection has a great influence on the efficiency of classifier algorithms in supervised methods. Therefore, to focus on feature selection is a key issue and is very important. In this study, different discrete versions of the proposed methods have been introduced that can be used in feature selection and feature scoring problems, which have been successful in evaluations. In this study, a problem called cold start is introduced, and a solution is presented that has a great impact on the efficiency of the proposed methods in feature scoring problem. A total of 16 UCI data sets and 2 Amazon data sets have been used for the evaluation of the proposed methods in feature selection problem. The parameters that have been compared are classification accuracy and the number of features required for classification. Also, the proposed methods can be used to create a proper sentiment dictionary. Evaluation results confirm the better performance of the proposed methods in most experiments.