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

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.

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 Optimasi Kadar Ultimate dan Tingkat Kebasaan Bio-arang Limbah Kayu Durian sebagai Pembenah Tanah

2021 ◽  
Vol 11 (02) ◽  
pp. 59
Author(s):  
Heri Soedarmanto ◽  
Evy Setiawaty ◽  
Taufik Iskandar
Keyword(s):  
Particle Size ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Oil And Gas ◽  
Pyrolysis Temperature ◽  
Raw Materials ◽  
Wood Waste ◽  
Quadratic Model ◽  
Ph Optimum ◽  
Pyrolysis Of Biomass

Konversi biomassa melalui pirolisis menghasilkan bio-arang, bio-minyak dan gas. Pirolisis biomassa dipengaruhi oleh kondisi pirolisis seperti bahan baku dan suhu pirolisis. Tujuan dari penelitian ini adalah menganalisis kondisi optimum kadar ultimate (CHO) dan pH bio-arang berdasarkan ukuran partikel bahan baku limbah kayu durian dan suhu pirolisis sebagai pembenah tanah. Limbah kayu durian yang digunakan dalam penelitian ini berukuran diameter 0,17–0,42 mm; 0,42–1,00 mm; dan 1,00–2,83 mm, dengan variasi suhu pirolisis 350°C, 450°C, dan 550°C sebanyak tiga kali ulangan. Optimasi menggunakan metode Response Surface Methodology. Berdasarkan model kuadratik, didapatkan kadar karbon optimum bio-arang sebesar 81,78% dengan ukuran partikel bahan baku pada 2,09 mm dan suhu pirolisis 530,5oC. Kadar hidrogen optimum bio-arang sebesar 3,35% dengan ukuran partikel bahan baku 2,89 mm dan suhu pirolisis 547,4oC. Kadar oksigen optimum bio-arang sebesar 12,22% dengan ukuran partikel bahan baku 1,89 mm dan suhu pirolisis 529,5oC. pH optimum bio-arang sebesar 8,35 dengan ukuran partikel bahan baku 0,6 mm dan suhu pirolisis 521,8oC. Kondisi proses terbaik untuk menghasilkan kadar ultimate dan pH yang paling optimal berada pada range ukuran diameter bahan baku 0,6 mm–2,89 mm dan suhu pirolisis sebesar 521,8oC–547,4oC.  The Optimization of Ultimate Levels and Basicity of Durian Wood Waste Biochar as Soil AmendmentAbstractBiomass conversion through pyrolysis produces biochar, bio-oil and gas. Pyrolysis of biomass is influenced by pyrolysis conditions such as raw materials and pyrolysis temperature. The purpose of this study was to analyze the optimum conditions for ultimate levels (CHO) and pH of biochar based on the particle size of the durian wood waste and the pyrolysis temperature as soil amendment. Particle sizes of durian waste were 0.17–0.42 mm; 0.42–1.00 mm; and 1.00–2.83 mm in diameter where pyrolysis temperatures were 350°C; 450°C; and 550°C. Optimization was used by the Response Surface Methodology method. Based on the quadratic model, the optimum carbon content of biochar was 81.78% with the particle size at 2.09 mm and the pyrolysis temperature of 530.5oC. The optimum hydrogen content of biochar was 3.35% with a particle size of 2.89 mm and a pyrolysis temperature of 547.4oC. The optimum oxygen content of biochar was 12.22% with a particle size of 1.89 mm and a pyrolysis temperature of 529.5oC. The optimum pH of biochar was 8.35 with a particle size of 0.6 mm and a pyrolysis temperature of 521.8oC. The most optimal ultimate levels and pH were in the diameter size range of 0.6 mm-2.89 mm and pyrolysis temperature of 521.8oC-547.4oC.

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 Intelligent Drilling of Oil and Gas Wells Using Response Surface Methodology and Artificial Bee Colony

2021 ◽  
Vol 13 (4) ◽  
pp. 1664
Author(s):  
Saurabh Tewari ◽  
Umakant Dhar Dwivedi ◽  
Susham Biswas
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Artificial Bee Colony ◽  
Oil And Gas ◽  
Data Driven ◽  
Drill Bit ◽  
Drilling Operation ◽  
Bee Colony ◽  
Drill Bits ◽  
Selection Of

The oil and gas industry plays a vital role in meeting the ever-growing energy demand of the human race needed for its sustainable existence. Newer unconventional wells are drilled for the extraction of hydrocarbons that requires advanced innovations to encounter the challenges associated with the drilling operations. The type of drill bits utilized in any drilling operation has an economical influence on the overall drilling operation. The selection of suitable drill bits is a challenging task for driller while planning for new wells. Usually, when it comes to deciding the drill bit type, generally, the data of previously drilled wells present in similar geological formation are analyzed manually, making it subjective, erroneous, and time consuming. Therefore, the main objective of this study was to propose an automatic data-driven bit type selection method for drilling the target formation based on the Optimum Penetration Rate (ROP). Response Surface Methodology (RSM) and Artificial Bee Colony (ABC) have been utilized to develop a new data-driven modeling approach for the selection of optimum bit type. Data from three nearby Norwegian wells have been utilized for the testing of the proposed approach. RSM has been implemented to generate the objective function for ROP due to its strong data-fitting characteristic, while ABC has been utilized to locate the global optimal value of ROP. The proposed model has been generated with a 95% confidence level and compared with the existing model of Artificial Neural Network and Genetic Algorithm. The proposed approach can also be applied over any other geological field to automate the drill bit selection, which can minimize human error and drilling cost. The United Nations Development Programme also promotes innovations that are economical for industrial sectors and human sustainability.

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