Optimum Oil Production Planning Using Infeasibility Driven Evolutionary Algorithm

2013 ◽  
Vol 21 (1) ◽  
pp. 65-82 ◽  
Author(s):  
Hemant Kumar Singh ◽  
Tapabrata Ray ◽  
Ruhul Sarker
Keyword(s):  
Evolutionary Algorithm ◽  
Production Planning ◽  
Oil Recovery ◽  
Optimization Problems ◽  
Nonlinear Function ◽  
Practical Interest ◽  
Oil Production ◽  
Economic Benefits ◽  
Financial Benefit ◽  
Single Objective

In this paper, we discuss a practical oil production planning optimization problem. For oil wells with insufficient reservoir pressure, gas is usually injected to artificially lift oil, a practice commonly referred to as enhanced oil recovery (EOR). The total gas that can be used for oil extraction is constrained by daily availability limits. The oil extracted from each well is known to be a nonlinear function of the gas injected into the well and varies between wells. The problem is to identify the optimal amount of gas that needs to be injected into each well to maximize the amount of oil extracted subject to the constraint on the total daily gas availability. The problem has long been of practical interest to all major oil exploration companies as it has the potential to derive large financial benefit. In this paper, an infeasibility driven evolutionary algorithm is used to solve a 56 well reservoir problem which demonstrates its efficiency in solving constrained optimization problems. Furthermore, a multi-objective formulation of the problem is posed and solved using a number of algorithms, which eliminates the need for solving the (single objective) problem on a regular basis. Lastly, a modified single objective formulation of the problem is also proposed, which aims to maximize the profit instead of the quantity of oil. It is shown that even with a lesser amount of oil extracted, more economic benefits can be achieved through the modified formulation.

A Comprehensive Review of Thermal Enhanced Oil Recovery: Techniques Evaluation

2018 ◽  
Vol 141 (3) ◽  
Author(s):  
Esmail M. A. Mokheimer ◽  
M. Hamdy ◽  
Zubairu Abubakar ◽  
Mohammad Raghib Shakeel ◽  
Mohamed A. Habib ◽  
...  
Keyword(s):  
Crude Oil ◽  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Oil Production ◽  
Economic Benefits ◽  
Comprehensive Review ◽  
Second Stage ◽  
Energy Demands ◽  
Recovery Techniques ◽  
Third Stage

The oil production from any well passes through three stages. The first stage is the natural extraction of oil under the well pressure, the second stage starts when the well pressure decreases. This second stage includes flooding the well with water via pumping sea or brackish water to increase the well pressure and push the oil up enhancing the oil recovery. After the first and secondary stages of oil production from the well, 20–30% of the well reserve is extracted. The well is said to be depleted while more than 70% of the oil are left over. At this stage, the third stage starts and it is called the enhanced oil recovery (EOR) or tertiary recovery. Enhanced oil recovery is a technology deployed to recover most of our finite crude oil deposit. With constant increase in energy demands, EOR will go a long way in extracting crude oil reserve while achieving huge economic benefits. EOR involves thermal and/or nonthermal means of changing the properties of crude oil in reservoirs, such as density and viscosity that ensures improved oil displacement in the reservoir and consequently better recovery. Thermal EOR, which is the focus of this paper, is considered the dominant technique among all different methods of EOR. In this paper, we present a brief overview of EOR classification in terms of thermal and nonthermal methods. Furthermore, a comprehensive review of different thermal EOR methods is presented and discussed.

Multimodal Optimization Using a Bi-Objective Evolutionary Algorithm

2012 ◽  
Vol 20 (1) ◽  
pp. 27-62 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Amit Saha
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Search Space ◽  
Test Problems ◽  
Multimodal Optimization ◽  
Optimal Solutions ◽  
Optimization Task ◽  
Pareto Optimal Set ◽  
Single Objective

In a multimodal optimization task, the main purpose is to find multiple optimal solutions (global and local), so that the user can have better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable optimum solution. To this end, evolutionary optimization algorithms (EA) stand as viable methodologies mainly due to their ability to find and capture multiple solutions within a population in a single simulation run. With the preselection method suggested in 1970, there has been a steady suggestion of new algorithms. Most of these methodologies employed a niching scheme in an existing single-objective evolutionary algorithm framework so that similar solutions in a population are deemphasized in order to focus and maintain multiple distant yet near-optimal solutions. In this paper, we use a completely different strategy in which the single-objective multimodal optimization problem is converted into a suitable bi-objective optimization problem so that all optimal solutions become members of the resulting weak Pareto-optimal set. With the modified definitions of domination and different formulations of an artificially created additional objective function, we present successful results on problems with as large as 500 optima. Most past multimodal EA studies considered problems having only a few variables. In this paper, we have solved up to 16-variable test problems having as many as 48 optimal solutions and for the first time suggested multimodal constrained test problems which are scalable in terms of number of optima, constraints, and variables. The concept of using bi-objective optimization for solving single-objective multimodal optimization problems seems novel and interesting, and more importantly opens up further avenues for research and application.

scholarly journals Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
History Matching ◽  
Optimization Problems ◽  
Learning Technologies ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Learning Approaches ◽  
Field Development ◽  
Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

scholarly journals A Lagrange Relaxation Based Decomposition Algorithm for Large-Scale Offshore Oil Production Planning Optimization

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1257
Author(s):  
Xiaoyong Gao ◽  
Yue Zhao ◽  
Yuhong Wang ◽  
Xin Zuo ◽  
Tao Chen
Keyword(s):  
Production Planning ◽  
Large Scale ◽  
Oil Production ◽  
Original Model ◽  
Decomposition Algorithm ◽  
Scale Model ◽  
Lagrange Relaxation ◽  
Planning Optimization ◽  
Offshore Oil Production ◽  
Offshore Oil

In this paper, a new Lagrange relaxation based decomposition algorithm for the integrated offshore oil production planning optimization is presented. In our previous study (Gao et al. Computers and Chemical Engineering, 2020, 133, 106674), a multiperiod mixed-integer nonlinear programming (MINLP) model considering both well operation and flow assurance simultaneously had been proposed. However, due to the large-scale nature of the problem, i.e., too many oil wells and long planning time cycle, the optimization problem makes it difficult to get a satisfactory solution in a reasonable time. As an effective method, Lagrange relaxation based decomposition algorithms can provide more compact bounds and thus result in a smaller duality gap. Specifically, Lagrange multiplier is introduced to relax coupling constraints of multi-batch units and thus some moderate scale sub-problems result. Moreover, dual problem is constructed for iteration. As a result, the original integrated large-scale model is decomposed into several single-batch subproblems and solved simultaneously by commercial solvers. Computational results show that the proposed method can reduce the solving time up to 43% or even more. Meanwhile, the planning results are close to those obtained by the original model. Moreover, the larger the problem size, the better the proposed LR algorithm is than the original model.

scholarly journals Simultaneous lipid biosynthesis and recovery for oleaginous yeast Yarrowia lipolytica

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Pratik Prashant Pawar ◽  
Annamma Anil Odaneth ◽  
Rajeshkumar Natwarlal Vadgama ◽  
Arvind Mallinath Lali
Keyword(s):  
Yarrowia Lipolytica ◽  
Oil Recovery ◽  
Oleaginous Yeast ◽  
Fermentation Broth ◽  
Continuous Production ◽  
Oil Production ◽  
Biofuel Production ◽  
Oil Yield ◽  
Microbial Oil

Abstract Background Recent trends in bioprocessing have underlined the significance of lignocellulosic biomass conversions for biofuel production. These conversions demand at least 90% energy upgradation of cellulosic sugars to generate renewable drop-in biofuel precursors (Heff/C ~ 2). Chemical methods fail to achieve this without substantial loss of carbon; whereas, oleaginous biological systems propose a greener upgradation route by producing oil from sugars with 30% theoretical yields. However, these oleaginous systems cannot compete with the commercial volumes of vegetable oils in terms of overall oil yields and productivities. One of the significant challenges in the commercial exploitation of these microbial oils lies in the inefficient recovery of the produced oil. This issue has been addressed using highly selective oil capturing agents (OCA), which allow a concomitant microbial oil production and in situ oil recovery process. Results Adsorbent-based oil capturing agents were employed for simultaneous in situ oil recovery in the fermentative production broths. Yarrowia lipolytica, a model oleaginous yeast, was milked incessantly for oil production over 380 h in a media comprising of glucose as a sole carbon and nutrient source. This was achieved by continuous online capture of extracellular oil from the aqueous media and also the cell surface, by fluidizing the fermentation broth over an adsorbent bed of oil capturing agents (OCA). A consistent oil yield of 0.33 g per g of glucose consumed, corresponding to theoretical oil yield over glucose, was achieved using this approach. While the incorporation of the OCA increased the oil content up to 89% with complete substrate consumptions, it also caused an overall process integration. Conclusion The nondisruptive oil capture mediated by an OCA helped in accomplishing a trade-off between microbial oil production and its recovery. This strategy helped in realizing theoretically efficient sugar-to-oil bioconversions in a continuous production process. The process, therefore, endorses a sustainable production of molecular drop-in equivalents through oleaginous yeasts, representing as an absolute microbial oil factory.

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