Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

2019 ◽  
Author(s):  
Anders Andreasen
Keyword(s):  
Evolutionary Algorithms ◽  
Gas Separation ◽  
Oil And Gas ◽  
Optimal Solution ◽  
Computer Experiment ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Inlet Temperature ◽  
Separation Plant ◽  
Separation Stage

In this article the optimization of a realistic oil and gas separation plant has been studied. Two different fluids are investigated and compared in terms of the optimization potential. Using Design of Computer Experiment (DACE) via Latin Hypercube Sampling (LHS) and rigorous process simulations, surrogate models using Kriging have been established for selected model responses. The surrogate models are used in combination with a variety of different evolutionary algorithms for optimizing the operating profit, mainly by maximizing the recoverable oil production. A total of 10 variables representing pressure and temperature various key places in the separation plant are optimized to maximize the operational profit. The optimization is bounded in the variables and a constraint function is included to ensure that the optimal solution allows export of oil with an RVP < 12 psia. The main finding is that, while a high pressure is preferred in the first separation stage, apparently a single optimal setting for the pressure in downstream separators does not appear to exist. In the second stage separator apparently two different, yet equally optimal, settings are revealed. In the third and final separation stage a correlation between the separator pressure and the applied inlet temperature exists, where different combinations of pressure and temperature yields equally optimal results.<br>

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

2019 ◽  
Author(s):  
Anders Andreasen
Keyword(s):  
Evolutionary Algorithms ◽  
Gas Separation ◽  
Oil And Gas ◽  
Optimal Solution ◽  
Computer Experiment ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Inlet Temperature ◽  
Separation Plant ◽  
Separation Stage

In this article the optimization of a realistic oil and gas separation plant has been studied. Two different fluids are investigated and compared in terms of the optimization potential. Using Design of Computer Experiment (DACE) via Latin Hypercube Sampling (LHS) and rigorous process simulations, surrogate models using Kriging have been established for selected model responses. The surrogate models are used in combination with a variety of different evolutionary algorithms for optimizing the operating profit, mainly by maximizing the recoverable oil production. A total of 10 variables representing pressure and temperature various key places in the separation plant are optimized to maximize the operational profit. The optimization is bounded in the variables and a constraint function is included to ensure that the optimal solution allows export of oil with an RVP < 12 psia. The main finding is that, while a high pressure is preferred in the first separation stage, apparently a single optimal setting for the pressure in downstream separators does not appear to exist. In the second stage separator apparently two different, yet equally optimal, settings are revealed. In the third and final separation stage a correlation between the separator pressure and the applied inlet temperature exists, where different combinations of pressure and temperature yields equally optimal results.<br>

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

2019 ◽  
Author(s):  
Anders Andreasen
Keyword(s):  
Evolutionary Algorithms ◽  
Gas Separation ◽  
Oil And Gas ◽  
Optimal Solution ◽  
Computer Experiment ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Inlet Temperature ◽  
Separation Plant ◽  
Separation Stage

In this article the optimization of a realistic oil and gas separation plant has been studied. Two different fluids are investigated and compared in terms of the optimization potential. Using Design of Computer Experiment (DACE) via Latin Hypercube Sampling (LHS) and rigorous process simulations, surrogate models using Kriging have been established for selected model responses. The surrogate models are used in combination with a variety of different evolutionary algorithms for optimizing the operating profit, mainly by maximizing the recoverable oil production. A total of 10 variables representing pressure and temperature various key places in the separation plant are optimized to maximize the operational profit. The optimization is bounded in the variables and a constraint function is included to ensure that the optimal solution allows export of oil with an RVP < 12 psia. The main finding is that, while a high pressure is preferred in the first separation stage, apparently a single optimal setting for the pressure in downstream separators does not appear to exist. In the second stage separator apparently two different, yet equally optimal, settings are revealed. In the third and final separation stage a correlation between the separator pressure and the applied inlet temperature exists, where different combinations of pressure and temperature yields equally optimal results.<br>

scholarly journals Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization Using Surrogate Modeling and Evolutionary Algorithms

2020 ◽  
Vol 4 (1) ◽  
pp. 11
Author(s):  
Anders Andreasen
Keyword(s):  
Gas Separation ◽  
Oil And Gas ◽  
Optimal Solution ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Inlet Temperature ◽  
Separation Plant ◽  
Separation Stage ◽  
Process Simulations ◽  
Plant Optimization

In this article, the optimization of a realistic oil and gas separation plant has been studied. Using Latin Hypercube Sampling (LHS) and rigorous process simulations, surrogate models using Kriging have been established for selected model responses. The surrogate models are used in combination with an evolutionary algorithm for optimizing the operating profit, mainly by maximizing the recoverable oil production. A total of 10 variables representing pressure and temperature at various key places in the separation plant are optimized to maximize the operational profit. The optimization is bounded in the variables and a constraint function is included to ensure that the optimal solution allows export of oil with a Reid Vapor Pressure (RVP) < 12 psia. The main finding is that, while a high pressure is preferred in the first separation stage, apparently a unique optimal setting for the pressure in downstream separators does not appear to exist. In the second stage separator, apparently different, yet more or less equally optimal, settings are revealed. In the third and final separation stage a correlation between the separator pressure and the applied inlet temperature exists, where different combinations of pressure and temperature yields equally optimal results.

Fixture Configuration Design Using a Computer Experiment

2000 ◽  
Author(s):  
L. Shelley Xie ◽  
S. Jack Hu ◽  
Wei Li ◽  
A. Sudjianto
Keyword(s):  
Computer Experiments ◽  
Computer Experiment ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Rigid Bodies ◽  
Multivariate Adaptive Regression Splines ◽  
Configuration Design ◽  
Element Analysis ◽  
Design Variables ◽  
Fixture Configuration

Abstract A new approach to fixture configuration design is developed based on the design and analysis of computer experiments (DACE). In the past, fixture configuration design undergoes three steps in an iterative manner: locator position design, clamp position design, and clamping force design. The fixture and workpiece were generally assumed to be rigid bodies. In this study, a sampled computer experiment is conducted with a finite element analysis (FEA). Surrogate models are developed based on the responses of this experiment. The purpose of building these models is to represent complex physical relations among the design variables in a simple functional form such that design analysis can be conducted efficiently. The surrogate response models can be used to provide a directional guidance in optimization, to identify feasible fixture configuration designs for flexible workpieces, and more importantly to enable a simultaneous design of locator/clamp positions and clamping forces. In the surrogate models workpiece deflections and locator reaction forces are used as variables. Distance-based Latin Hypercube Sampling Design (LHD) is used to increase the number of the sample points and to evenly spread them in the design space. Multivariate Adaptive Regression Splines (MARS) and Gaussian Stochastic Kriging (GSK) models are employed to capture nonlinear relationship among the design variables. The proposed approach is illustrated with examples.

scholarly journals Plant Wide Oil and Gas Separation Plant Optimisation using Response Surface Methodology

2018 ◽  
Vol 51 (8) ◽  
pp. 178-184 ◽  
Author(s):  
Anders Andreasen ◽  
Kasper Rønn Rasmussen ◽  
Matthias Mandø
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Gas Separation ◽  
Oil And Gas ◽  
Separation Plant

Well Placement Technique and Production Optimization for Mature Field - A Case Study of L-X Field

2020 ◽  
Vol 46 ◽  
pp. 168-179
Author(s):  
Patrick Nwafor ◽  
Kelani Bello
Keyword(s):  
Oil And Gas ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Oil And Gas Industry ◽  
Optimization Method ◽  
Simulation Software ◽  
Production Optimization ◽  
Direct Optimization ◽  
Well Placement ◽  
Local Optimal Solution

A Well placement is a well-known technique in the oil and gas industry for production optimization and are generally classified into local and global methods. The use of simulation software often deployed under the direct optimization technique called global method. The production optimization of L-X field which is at primary recovery stage having five producing wells was the focus of this work. The attempt was to optimize L-X field using a well placement technique.The local methods are generally very efficient and require only a few forward simulations but can get stuck in a local optimal solution. The global methods avoid this problem but require many forward simulations. With the availability of simulator software, such problem can be reduced thus using the direct optimization method. After optimization an increase in recovery factor of over 20% was achieved. The results provided an improvement when compared with other existing methods from the literatures.

Centrifugal Gas Compressors Energy Optimization Initiatives: A Way to Save Valuable Energy at Upstream Operation

2020 ◽  
Author(s):  
Mohammed Abdo Alwani ◽  
Mohammed Ahmad Soliman
Keyword(s):  
Energy Consumption ◽  
Capital Investment ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
High Energy ◽  
International Standards ◽  
Inlet Temperature ◽  
Process Data ◽  
Saving Energy ◽  
Discharge Pressure

Abstract The objective of this paper is to showcase successful and innovative means and techniques to improve and enhance centrifugal gas compressors (CGCs) performance, using methods to minimize power consumption, with no need for capital investment. These techniques will assure, if effectively followed, considerable reduction of the consumed energy. CGCs are the most widely used equipment in the oil and gas industry to boost gas, mainly hydrocarbons, to satisfy process treatments and pipeline requirements. In addition, CGCs are one of the major energy consumers, and therefore present an exceptional opportunity for saving energy. Focusing on lowering inlet gas temperatures, considering suction throttling of discharge pressure instead of the traditional discharge throttling, will help to reduce energy consumption. In this paper, a detailed analysis of factors aggravate or lead to undesired CGCs performance will be discussed along with solutions to minimize adverse impact. For example, operating the gas compressors at relatively high inlet temperature will result in higher energy consumption. After performing need analysis, results prove that we would save 3-7% of running compressors consumed energy. In addition, during compressor design phase, it was found that most motor driven compressor system uses discharge throttling, which incurs high-energy consumption. Instead, it is recommended to consider suction throttling to control discharge pressure, as will be explained. This paper will focus on a detailed case study in one of the running CGCs in an upstream gas-oil separation plant (GOSP-A). This paper proves the effectiveness of the proposed techniques in reinstating the CGCs in GOSP-A, to ensure better performance and save energy. This innovative technique is based on extensive process data analysis — evaluating operating, design data, related performance curves, and reviewing international standards. It will be illustrated that this type of analysis and techniques is a valuable tool for saving energy, in most cases, at oil and gas industries

On Adaptive Sampling for Single and Multi-Response Bayesian Surrogate Models

2006 ◽  
Author(s):  
David A. Romero ◽  
Cristina H. Amon ◽  
Susan Finger
Keyword(s):  
Experimental Design ◽  
Adaptive Sampling ◽  
Design Space Exploration ◽  
A Priori ◽  
Computer Experiments ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Sampling Techniques ◽  
Design And Optimization ◽  
Sampling Points

In order to reduce the time and resources devoted to design-space exploration during simulation-based design and optimization, the use of surrogate models, or metamodels, has been proposed in the literature. Key to the success of metamodeling efforts are the experimental design techniques used to generate the combinations of input variables at which the computer experiments are conducted. Several adaptive sampling techniques have been proposed to tailor the experimental designs to the specific application at hand, using the already-acquired data to guide further exploration of the input space, instead of using a fixed sampling scheme defined a priori. Though mixed results have been reported, it has been argued that adaptive sampling techniques can be more efficient, yielding better surrogate models with less sampling points. In this paper, we address the problem of adaptive sampling for single and multi-response metamodels, with a focus on Multi-stage Multi-response Bayesian Surrogate Models (MMBSM). We compare distance-optimal latin hypercube sampling, an entropy-based criterion and the maximum cross-validation variance criterion, originally proposed for one-dimensional output spaces and implemented in this paper for multi-dimensional output spaces. Our results indicate that, both for single and multi-response surrogate models, the entropy-based adaptive sampling approach leads to models that are more robust to the initial experimental design and at least as accurate (or better) when compared with other sampling techniques using the same number of sampling points.

Sign in / Sign up

Export Citation Format

Share Document