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Well Placement Optimization Using Simulated Annealing and Genetic Algorithm
Abstract
The general success ratio of wells drilled lies at 1:4, which highlights the difficulty in properly ascertaining sweetspots. well placement location selection is one of the most important processes to ensure optimal recovery of hydrocarbons. Conventionally, a subjective decision is based on the visualization of the HUPHISO (a product of net-to-gross, porosity and oil saturation) map. While this approach identifies regions of high HUPHISO regarded as sweetspots in the reservoir; it lacks consideration for neighbouring regions of the sweetspot. This sometimes lead to placement of wells in a sweetspot but near an adjoining aquifer; giving rise to early water breakthrough - low hydrocarbon recovery. Recently, heuristic optimization techniques. Genetic algorithm (GA) and simulated annealing (SA) has received attention as methods of selection of well-placement locations. This project developed and implemented GA and SA well-placement algorithms and compared the reservoir performance outputs to that of conventional method. Firstly, a reservoir performance model was built using a reservoir flow simulator. In the base case, the wells were placed based on a subjective selection of gridblocks upon the visualization of the HUPHISO map. Thereafter, JAVA routines of GA and SA well-placement algorithms were developed. The numeric data (ASCII format) underlying the map were then exported to the routines.
Finally, the performance model was updated with new well locations as selected based on the GA and SA-based approach and the results were compared to the base case. The Comparison of the results showed that both GA and SA-based approach resulted to an increased recovery and time before water breakthrough.
... So, here we aim to maximize production of field. In modern works (Minton, 2012;Tukur et al., 2019;Jesmani et al., 2020) real limitations in the development of fields are not considered. More precisely, it does not pay attention to various geographical objects (lakes, swamps or rivers) that make it difficult to build wells and roads. ...
In recent years generative design techniques have become firmly established in numerous applied fields, especially in engineering. These methods are demonstrating intensive growth owing to promising outlook. However, existing approaches are limited by the specificity of problem under consideration. In addition, they do not provide desired flexibility. In this paper we formulate general approach to an arbitrary generative design problem and propose novel framework called GEFEST (Generative Evolution For Encoded STructure) on its basis. The developed approach is based on three general principles: sampling, estimation and optimization. This ensures the freedom of method adjustment for solution of particular generative design problem and therefore enables to construct the most suitable one. A series of experimental studies was conducted to confirm the effectiveness of the GEFEST framework. It involved synthetic and real-world cases (coastal engineering, microfluidics, thermodynamics and oil field planning). Flexible structure of the GEFEST makes it possible to obtain the results that surpassing baseline solutions.
... They are particularly useful in solving problems where the relationships between variables are unknown or poorly understood [51,52]. Examples of the application of evolutionary algorithms in reservoir engineering include modelling and production optimization [53][54][55], estimation of effectiveness and optimization of EOR methods (including WAG) [56][57][58][59], estimating values of parameters such as MMP [60], the formation volume factor [61] or the emulsion viscosity [62], and issues related to the reservoir development [63][64][65][66][67]. ...
Production from mature oil fields is gradually declining, and new discoveries are not sufficient to meet the growing demand for oil products. Hence, enhanced oil recovery is emerging as an essential link in the global oil industry. This paper aims to recognize the possibility of increasing oil recovery from Polish carbonate reservoirs by the water alternating gas injection process (WAG) using various types of gases, including CO2, acid gas (a mixture of CO2 and H2S of 70/30% vol/vol) and high-nitrogen natural gases occurring in the Polish Lowlands. A series of 17 core flooding experiments were performed under the temperature of 126 °C, and at pressures of 270 and 170 bar on composite carbonate cores consisting of four dolomite core plugs. Original reservoir rock and fluids were used. A set of slim tube tests was conducted to determine the miscibility conditions of the injected fluids with reservoir oil. The WAG process was compared to continuous gas injection (CGI) and continuous water injection (CWI) and was proven to be more effective. CO2 WAG injection resulted in a recovery factor (RF) of up to 82%, where the high nitrogen natural gas WAG injection was less effective with the highest recovery of 70%. Based on the core flooding results and through implementing a genetic programming algorithm, a mathematical model was developed to estimate recovery factors using variables specific to a given WAG scheme.
... A vital aspect that has been overlooked when considering injection options is injection patterns which are described in Schlumberger (2020) and Ogbeiwi et al. (2017). Njar (2018) summarized that the use of a genetic algorithm and simulated annealing for well placement optimization improves hydrocarbon recovery, delayed water production, and accommodates production at higher rates. ...
Thin oil rim are reservoirs with hydrocarbon pay zone not more than 100 ft. Production challenges mainly water, and gas coning arises due to this trait and presence of a larger gas cap and aquifer. To safely exploit from oil rim reservoirs, the right production choices and optimization of production parameters need to be implemented before the commencement of any recovery scheme. A case study reservoir of an oil rim in the Niger-delta producing region is placed on a concurrent production via 8 horizontal producer wells after history matching and optimization of production parameters using a black oil simulator (Eclipse). Six different pattern scenarios of water and gas injection at 3 rates are considered: optimized oil recovery estimates of 5.43%, 4.51%, 5.41%, 4.93%, 5.89%, and 5.82% for inverted direct line water injection, inverted staggered water injection, inverted 4 spot injection, 5 spot water injection, inverted 7 spot water injection, and inverted 9 spot water injection respectively. The incremental oil recovery over the base case (no injection) recorded in like manner are 1.43%, 0.51%, 1.41%, 0.93%, 1.89%, and 1.82%. The results show that pattern water injection at 1000 stb/day is more suitable for optimum oil recovery for oil rims under concurrent production.
... • The pheromone related parameters in the formula are selected empirically. SA [53,162,163] • Faster convergence. • Incorporate individual decision variables. ...
Well placement optimization is one of the major challenging factors in the field development process of oil and gas industry. The objective function of well placement optimization is considered as high dimensional, discontinuous and multi-model. Over the last decade, both gradient-based and gradient-free optimization methods have been implemented to tackle this problem. Nature-inspired gradient-free optimization algorithms like particle swarm optimization, genetic algorithm, covariance matrix adaptation evolution strategy and differential evolution have been utilized in this area. These optimization techniques are implemented as stand-alone or as hybrid form to maximize the economic factors. In this paper, several nature-inspired metaheuristic optimization techniques and their application to maximize the economic factors are reviewed. Newly developed optimization algorithms are very efficient and favorable when compared to other established optimization algorithms and in all cases, it has been noticed that hybridization of two or more algorithms works better than stand-alone algorithms. Furthermore, none of the single optimization techniques can be established as being universally superior which aligns with no free lunch theorem. For future endeavor, combining optimization methods and exploiting multiple optimization processes for faster convergence and developing efficient proxy model is expected.
The well optimization technique with backward elimination aims to determine the optimum number of wells and their locations that can maximise project value and its recoverable resources, through repeated ranking of candidate wells and eliminating the poorest performer. For a greenfield development, subsurface uncertainties are typically still very large due to limited data from exploration and appraisal wells. This study outlines our approach to perform well optimization with these governing uncertainties in order to support the decision-making process.
First, multiple realizations of reservoir models are constructed to represent range of possible outcomes by sampling different values from uncertainty parameters. Backward elimination for well optimization is then performed on those realizations. Wells can be ranked based on means and standard deviations of their performance, and the lowest rank candidate is eliminated from the process one at a time. At this point, project economic and resources are evaluated to find optimum set of wells for field development. Furthermore, well performance data from multiple realization models are carefully analyzed to define key subsurface uncertainties that need to be managed.
Solution from this backward elimination with subsurface uncertainty workflow can maximize project valuation because it balances the risk of overspending to drill sub-optimum wells in some realizations with the risk of losing sell opportunity due to insufficient field deliverability in the other realizations. Development decision will be more robust because it is based on the optimum configuration that is applicable irrespective of the unknown uncertain quantities. Moreover, detailed analysis on well performance data allows us to better understand the risk associated with our planned wells so that appropriate de-risking plan can be developed and combined into development strategy.
The backward elimination process is straightforward to implement and normally does not require very high computational expense. Thus, it is suitable to be used with uncertainty workflow with multiple realizations of reservoir models, which will increase computational requirement by multiple times. Other commonly used techniques for well optimization such as a Genetic Algorithm (GA) or an Evolutionary Algorithm (EA) are computational expensive by themselves already; and they will require even more runtime when using them with this uncertainty workflow.
This paper extends backward elimination approach for well optimization to be used with uncertainty workflow. The overall uncertainty analysis workflow is discussed and provided, with key steps detailing the approach taken. Project valuation and recoverable resources can be further optimized with this new approach, and ultimately can guide the decision making in field development.
Production optimization is an effective technique to maximize the oil recovery or the net present value in reservoir development. Recently, the stochastic simplex approximation gradient (StoSAG) optimization algorithm draws significant attention in the optimization algorithm family. It shows high searching quality in large-scale engineering problems. However, its optimization performance and features are not fully understood. This study evaluated and analyzed the influence of some key parameters related to the optimization process of StoSAG including the ensemble size to estimate the approximation gradient, the search step size, the cut number, the perturbation step size, and the initial position by using 47 mathematical benchmark functions. Statistical analysis was employed to diminish the randomness of the algorithm. The quality of the optimization results, the convergence, and the computational time consuming were analyzed and compared. The parameter selection strategy was presented. The results showed that a larger ensemble size was not always favorable to obtain better optimization results. The increase of the search step size was favorable to escape from the local optimum. A large step size needed to match a large cut number. The increase of cut number was beneficial to increase the local searchability, but also made the algorithm more easily fall into the local optimum. The random initial position was beneficial to find the global optimal point. Moreover, the effectiveness of the parameter selection strategy was tested by a classical reservoir production optimization example. The final net present value (NPV) for water flooding reservoir production optimization substantially increased, which indicated the excellent performance of StoSAG by adjusting the key parameters.
In this chapter, water saturation, water conductivity, surface conductance of clay, and contact angle of graphite are simultaneously estimated by processing multifrequency electromagnetic (EM) measurements using the Markov-chain Monte Carlo (MCMC) inversion and the simulated annealing (SA) inversion. Multifrequency EM log responses from 25 distinct layers in a synthetic formation were processed by the two inversion methodologies for purposes of comparing their estimation accuracies and computational times. MCMC inversion generates a range of values for the desired estimate denoting the uncertainty in the estimate. However, the determination of data covariance matrix for the MCMC inversion can be a challenge. On the other hand, the SA inversion provides a single value of the desired estimate, which generally represents the optimum value of the estimate. Both the inversion methods provide reliable estimates. MCMC inversion takes about 100 min to perform inversion for all the 25 synthetic layers, while the SA inversion takes about 40 min. MCMC needs more iteration steps to ensure chain convergence as it explores the range of possible values of the desired estimate, representing the uncertainty in the estimation. The computation time of SA can be further reduced by selecting proper values of initial temperature and temperature-decline rate.
Shale gas reservoirs have been successfully developed due to the advancement of the horizontal well drilling and multistage hydraulic fracturing techniques. However, the optimization design of the horizontal well drilling, hydraulic fracturing, and operational schedule is a challenging problem. An ensemble-based optimization method (EnOpt) is proposed here to optimize the design of the hydraulically fractured horizontal well in the shale gas reservoir. The objective is to maximize the net present value (NPV) which requires a simulation model to predict the cumulative shale gas production. To accurately describe the geometry of the hydraulic fractures, the embedded discrete fracture modeling method (EDFM) is used to construct the shale gas simulation model. The effects of gas absorption, Knudsen diffusion, natural and hydraulic fractures, and gas–water two phase flow are considered in the shale gas production system. To improve the parameter continuity and Gaussianity required by the EnOpt method, the Hough transformation parameterization is used to characterize the horizontal well. The results show that the proposed method can effectively optimize the design parameters of the hydraulically fractured horizontal well, and the NPV can be improved greatly after optimization so that the design parameters can approach to their optimal values.
Much work has been performed on the optimal well placement/control problem, including some investigations on optimizing well types (injector or producer) and/or drilling order. However, to the best of our knowledge, there are only a handful of papers dealing with the following problem that is sometimes given to reservoir-engineering groups: given a potential set of reasonable drilling paths and a drilling budget that is sufficient to drill only a few wells, find the optimal well paths, determine whether a well should be an injector or a producer, and determine the drilling order that maximizes the net present value (NPV) of production over the life of the reservoir.
In this work, the optimal choices of drilling paths, types, and drilling order are found using the genetic algorithm (GA) with mixed encodings. A binary encoding for the optimization variables pertaining to well-location indices and well types is proposed to effectively handle a large amount of categorical variables, while the drilling sequence is parameterized with ordinal numbers. These two sets of variables are optimized both simultaneously and sequentially. Finally, control optimization using a stochastic simplex approximate gradient (StoSAG) is performed to further improve the NPV of life-cycle production.
The proposed workflow is tested on two examples: a 3D channelized reservoir where the potential well paths are either vertical or horizontal, and the Brugge model where only vertical wells are drilled. Both numerical examples indicate that GA combined with StoSAG is a viable solution to the problem considered.
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