Automated Optimization Strategies for Horizontal Wellbore and Hydraulic Fracture Stages Placement in Unconventional Gas Reseroirs

In the last decades rapid advances in horizontal drilling and hydraulic fracturing technologies ensure production of commercial quantities of natural gas from many unconventional reservoirs. Reservoir management and development strategies for shale and tight gas plays have evolved from ad hoc approaches to more rigorous strategies that involve numerical optimization in presence of multiple economic and production objectives and constraints. Application of an automated integrated optimization framework for placement of horizontal wellbores and transverse hydraulic fracture stages along them has potential of increasing shale gas reserves and projects’ revenue even further. This dissertation introduces a novel integrated evolutionary-based optimization framework for placement of horizontal wellbores and hydraulic fracture stages that allows enhancing production from shale gas formations and provides a solid foundation for future field-scale application once better understanding of shale petrophysics and geomechanics is developed. The proposed optimization workflow is developed and tested in stages. First, we summarize what has been done in the subject field previously by scholars and identify what is missing. Second, we present assumptions for the shale gas simulation model that make our framework and the simulation model applicable. Third, we pre-screen several economic and petrophysical parameters in order to identify the most significant for the subsequent sensitivities analysis. Forth, we develop evolutionary-based optimization strategy for placement of hydraulic fracture stages along a single horizontal wellbore. We investigate how sensitive the optimization results to changes in the key parameters pre-selected during pre-screening. Fifth, we enhance the framework to handle multiple horizontal producers, discuss the conditions when such approach is applicable, and extensively test this integrated workflow on a suite of simulation runs. Finally, we implement and apply multi-objective optimization approach (the improved non-dominated sorting genetic algorithm) to the problem of optimal HF stage placement in shale gas reservoirs and analyze the efficiency of our evolutionary-based optimization scheme in presence of multiple conflicting or non-conflicting objectives. Based on our extensive testing and rigorous formulation of the optimization problem, we find that the chosen evolutionary framework is effective in calculating the optimal number of horizontal wells, the number of HF stages, their specific locations along the wells as well as their half-length. We also conclude that further computational efficiency can be achieved if minimum stage spacing and same chromosome elimination procedure are used. The multi-objective approach has been tested on conflicting and non-conflicting objectives and proved to compute the Pareto optimal front of solutions (or production scenarios) in computationally efficient manner

Empirical Investigations of Reference Point Based Methods When Facing a Massively Large Number of Objectives: First Results

EMO 2017: 9th International Conference on Evolutionary Multi-Criterion Optimization, 19-22 March 2017, Münster, GermanyThis is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.Multi-objective optimization with more than three objectives has become one of the most active topics in evolutionary multi-objective optimization (EMO). However, most existing studies limit their experiments up to 15 or 20 objectives, although they claimed to be capable of handling as many objectives as possible. To broaden the insights in the behavior of EMO methods when facing a massively large number of objectives, this paper presents some preliminary empirical investigations on several established scalable benchmark problems with 25, 50, 75 and 100 objectives. In particular, this paper focuses on the behavior of the currently pervasive reference point based EMO methods, although other methods can also be used. The experimental results demonstrate that the reference point based EMO method can be viable for problems with a massively large number of objectives, given an appropriate choice of the distance measure. In addition, sufficient population diversity should be given on each weight vector or a local niche, in order to provide enough selection pressure. To the best of our knowledge, this is the first time an EMO methodology has been considered to solve a massively large number of conflicting objectives.This work was partially supported by EPSRC (Grant No. EP/J017515/1

Evolutionary many-objective optimization：A survey

Many-objective optimization problems (MaOPs) widely exist in industrial and scientific fields, where there are more than 3 objectives that are conflicting with each other (i.e., the improvement of the performance in one objective may lead to the deterioration of the performance of some other objectives). Because of the conflict between objectives, there is no unique optimal solution for MaOPs, but a group of compromise solutions need to be obtained to balance between objectives. As a class of population-based optimization algorithms inspired by biological evolution principles evolutionary algorithms have been proved to be effective in solving MaOPs, and have become one of the research hot spots in the field of multi-objective optimization. In the past 20 years, the research on many-objective evolutionary algorithms (MaOEAs) has made great progress, and a large number of advanced evolutionary methods and evaluation systems have been proposed and improved. In this paper, the research progress of evolutionary many-objective optimization (EMaO) is comprehensively reviewed. Specifically, it includes: (1) Describing the relevant theoretical background of EMaO; (2) Analyzing the problems and challenges faced by evolutionary algorithms in solving MaOPs; (3) Discussing the development of MaOEAs in detail; (4) Summarizing MaOPs and performance indicators in detail; (5) Introducing the visualization tools for high-dimensional objective space; (6) Summarizing the application of MaOEAs in some fields, and (7) Providing suggestions for future research in the domain

Hybrid non-dominated sorting genetic algorithm with adaptive operators selection

Multiobjective optimization entails minimizing or maximizing multiple objective functions subject to a set of constraints. Many real world applications can be formulated as multi-objective optimization problems (MOPs), which often involve multiple conflicting objectives to be optimized simultaneously. Recently, a number of multi-objective evolutionary algorithms (MOEAs) were developed suggested for these MOPs as they do not require problem specific information. They find a set of non-dominated solutions in a single run. The evolutionary process on which they are based, typically relies on a single genetic operator. Here, we suggest an algorithm which uses a basket of search operators. This is because it is never easy to choose the most suitable operator for a given problem. The novel hybrid non-dominated sorting genetic algorithm (HNSGA) introduced here in this paper and tested on the ZDT (Zitzler-Deb-Thiele) and CEC’09 (2009 IEEE Conference on Evolutionary Computations) benchmark problems specifically formulated for MOEAs. Numerical results prove that the proposed algorithm is competitive with state-of-the-art MOEAs

Multi-and many-objective optimization: present and future in de novo drug design

de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field

A review of Nadir point estimation procedures using evolutionary approaches: a tale of dimensionality reduction

Estimation of the nadir objective vector is an important task, particularly for multi-objective optimization problems having more than two conflicting objectives. Along with the ideal point, nadir point can be used to normalize the objectives so that multi-objective optimization algorithms can be used more reliably. The knowledge of the nadir point is also a pre-requisite to many multiple criteria decision making methodologies.Moreover, nadir point is useful for an aid in interactive methodologies and visualization softwares catered for multi-objective optimization. However, the computation of exact nadir point formore than two objectives is not an easy matter, simply because nadir point demands the knowledge of extreme Paretooptimal solutions. In the past few years, researchers have proposed several nadir point estimation procedures using evolutionary optimization methodologies. In this paper, we review the past studies and reveal an interesting chronicle of events in this direction. To make the estimation procedure computationally faster and more accurate, the methodologies were refined one after the other by mainly focusing on increasingly lower dimensional subset of Pareto-optimal solutions. Simulation results on a number of numerical test problems demonstrate better efficacy of the approach which aims to find only the extreme Pareto-optimal points compared to its higher-dimensional counterparts

Evolutionary Algorithms for

Many real-world problems involve two types of problem difficulty: i) multiple, conflicting objectives and ii) a highly complex search space. On the one hand, instead of a single optimal solution competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. In the absence of preference information, none of the corresponding trade-offs can be said to be better than the others. On the other hand, the search space can be too large and too complex to be solved by exact methods. Thus, efficient optimization strategies are required that are able to deal with both difficulties. Evolutionary algorithms possess several characteristics that are desirable for this kind of problem and make them preferable to classical optimization methods. In fact, various evolutionary approaches to multiobjective optimization have been proposed since 1985, capable of searching for multiple Paretooptimal solutions concurrently in a single simulation run. However, in spite of this variety, there is a lack of extensive comparative studies in the literature. Therefore, it has remained open up to now

A Hybrid MOEA/D-TS for Solving Multi-Objective Problems

In many real-world applications, various optimization problems with conflicting objectives are very common. In this paper we employ Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), a newly developed method, beside Tabu Search (TS) accompaniment to achieve a new manner for solving multi-objective optimization problems (MOPs) with two or three conflicting objectives. This improved hybrid algorithm, namely MOEA/D-TS, uses the parallel computing capacity of MOEA/D along with the neighborhood search authority of TS for discovering Pareto optimal solutions. Our goal is exploiting the advantages of evolutionary algorithms and TS to achieve an integrated method to cover the totality of the Pareto front by uniformly distributed solutions. In order to evaluate the capabilities of the proposed method, its performance, based on the various metrics, is compared with SPEA, COMOEATS and SPEA2TS on well-known Zitzler-Deb-Thiele’s ZDT test suite and DTLZ test functions with separable objective functions. According to the experimental results, the proposed method could significantly outperform previous algorithms and produce fully satisfactory results