The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Turgay Ertekin - One of the best experts on this subject based on the ideXlab platform.
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Production performance prediction and Field Development design tool for coalbed methane reservoirs: A neuro-simulation approach
Application of Computers and Operations Research in the Mineral Industry - Proceedings of the 37th International Symposium APCOM 2015, 2015Co-Authors: V. Rajput, E.d.k. Basel, Turgay ErtekinAbstract:Implementation of numerical simulations for Field Development optimization can be overly demanding in terms of their time and manpower requirements. To overcome these problems, a methodology has been developed that can be used to predict production performance of a given Field and perform Field Development studies with nominal manpower and computational requirements. Artificial neural networks (ANN) are used for Development of expert systems for prediction of instantaneous and cumulative gas and water production, as well as for reservoir property prediction. A commercial reservoir simulator is employed for generation of database for training, validation and testing of these expert systems. Uncertainty in reservoir properties is taken into account by varying the reservoir parameters within an estimated range of values. Analysis of results obtained from trained networks showed error values of less than 3% for prediction of gas and water production profiles (forward networks), while those that are obtained for prediction of reservoir characteristics gave error levels of 15-18% (inverse networks). Forward networks were then used for optimization of Field Development based upon the criteria of maximizing the net present value (NPV) of a given Field. Several case studies were carried out and analyzed.
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Methods of Neuro-Simulation for Field Development
SPE Rocky Mountain Regional Low-Permeability Reservoirs Symposium, 1998Co-Authors: D. Hari, Turgay Ertekin, A.s. GraderAbstract:This paper introduces a methodology for optimizing Field Development schemes. The use of numerical simulation models in establishing Field Development strategies is a widely practiced approach. In Field Development studies, a large number of scenarios which result in a time consuming and expensive process must be considered. The objective of this paper is to structure the Field Development schemes using artificial neural networks (ANNs) in conjunction with numerical reservoir simulation; a process we call neuro-simulation. In neuro-simulation, a few Field Development scenarios are examined using a numerical simulator. The results of these studies are then used to train the ANN. The trained ANN is used as a predictive tool for Field Development purposes. Using neuro-simulation, the number of numerical simulations is significantly reduced. The use of the neuro-simulation approach becomes practical especially during the early life of the Field when the recovery data and the Field properties are sparse and sporadic. The neuro-simulation approach provides the flexibility of considering any location as a potential site in contrast to the conventional simulation approach when the well locations are restricted to the predefined block centers. The neuro-simulation approach is faster and more efficient than its conventional counterpart. The results obtained from neuro-simulation compare well with the results obtained from a reservoir simulator.
V. Rajput - One of the best experts on this subject based on the ideXlab platform.
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Production performance prediction and Field Development design tool for coalbed methane reservoirs: A neuro-simulation approach
Application of Computers and Operations Research in the Mineral Industry - Proceedings of the 37th International Symposium APCOM 2015, 2015Co-Authors: V. Rajput, E.d.k. Basel, Turgay ErtekinAbstract:Implementation of numerical simulations for Field Development optimization can be overly demanding in terms of their time and manpower requirements. To overcome these problems, a methodology has been developed that can be used to predict production performance of a given Field and perform Field Development studies with nominal manpower and computational requirements. Artificial neural networks (ANN) are used for Development of expert systems for prediction of instantaneous and cumulative gas and water production, as well as for reservoir property prediction. A commercial reservoir simulator is employed for generation of database for training, validation and testing of these expert systems. Uncertainty in reservoir properties is taken into account by varying the reservoir parameters within an estimated range of values. Analysis of results obtained from trained networks showed error values of less than 3% for prediction of gas and water production profiles (forward networks), while those that are obtained for prediction of reservoir characteristics gave error levels of 15-18% (inverse networks). Forward networks were then used for optimization of Field Development based upon the criteria of maximizing the net present value (NPV) of a given Field. Several case studies were carried out and analyzed.
E.d.k. Basel - One of the best experts on this subject based on the ideXlab platform.
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Production performance prediction and Field Development design tool for coalbed methane reservoirs: A neuro-simulation approach
Application of Computers and Operations Research in the Mineral Industry - Proceedings of the 37th International Symposium APCOM 2015, 2015Co-Authors: V. Rajput, E.d.k. Basel, Turgay ErtekinAbstract:Implementation of numerical simulations for Field Development optimization can be overly demanding in terms of their time and manpower requirements. To overcome these problems, a methodology has been developed that can be used to predict production performance of a given Field and perform Field Development studies with nominal manpower and computational requirements. Artificial neural networks (ANN) are used for Development of expert systems for prediction of instantaneous and cumulative gas and water production, as well as for reservoir property prediction. A commercial reservoir simulator is employed for generation of database for training, validation and testing of these expert systems. Uncertainty in reservoir properties is taken into account by varying the reservoir parameters within an estimated range of values. Analysis of results obtained from trained networks showed error values of less than 3% for prediction of gas and water production profiles (forward networks), while those that are obtained for prediction of reservoir characteristics gave error levels of 15-18% (inverse networks). Forward networks were then used for optimization of Field Development based upon the criteria of maximizing the net present value (NPV) of a given Field. Several case studies were carried out and analyzed.
Tom Skoglunn - One of the best experts on this subject based on the ideXlab platform.
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Yme, a marginal-Field Development in the North Sea
Journal of Petroleum Technology, 1999Co-Authors: Odd J. Apeland, Svein A. Flaate, Tom SkoglunnAbstract:This paper summarizes a Field-Development strategy with implementation of an impressively broad range of innovative technology at the Yme Field, a small, marginal oil Field in the North Sea.
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Yme, a marginal Field Development in the North Sea
Software - Practice and Experience, 1998Co-Authors: Odd J. Apeland, Svein A. Flaate, Tom SkoglunnAbstract:The small Yme oil Field offshore Norway was discovered in 1987, but the Plan for Development and Operation (PDO) was first submitted in 1993 due to the Field's marginal character. The recoverable oil reserves as basis for the PDO were only 3,4 mill. Sm 3 , With the Field located far from infrastructure, the Development decision required a dedicated effort from the license owners. Focus was put on increasing and firming up the reserves basis while maintaining acceptable production costs. Accordingly, a jack-up drilling rig was leased and modified for simultaneous drilling and production. An oil tanker was leased for storage and Field off-loading. The time from contract awards to first oil from the main structure, Yme Gamma Vest, in February 1996 was only 13 months. An important part of the Development strategy in order to improve profitability was active exploration of mapped prospects in the license area in parallel with Field Development activities and production. The first subsea satellite Development, Yme Beta Ost, was put on production in June 1996. This was only nine months after submitting the PDO to the authorities and three months after production start for the main Field. During the production phase three more exploration wells have been drilled. Overall, these have increased the reserves basis and prolonged the Field lifetime. The Yme reservoirs comprise several challenges such as low reservoir pressure and solution gas content, special fluid properties such as high salinity of the formation water with dissolved solids of 190.000 ppm, and high asphaltene content of the oil. Artificial lift using ESPs and subsea gas lift was installed from the start. A compact water injection unit contributes to improved oil recovery. Barefoot open hole multilateral horizontal completions and hydraulic fracturing of vertical wells have also been used to improve drainage from the reservoir which has large permeability contrasts. The Yme Field Development shows how a marginal offshore Field Development can be initiated and improved using decision making under risk and application of unconventional techniques.
Honggang Wang - One of the best experts on this subject based on the ideXlab platform.
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Mixed integer simulation optimization for petroleum Field Development under geological uncertainty
2013 Winter Simulations Conference (WSC), 2013Co-Authors: Honggang WangAbstract:Optimal Development of oil and gas Fields involves determining well locations in oil reservoirs and well control through the production time. Field Development problems are mixed-integer optimization problems because the well locations are dened by integer-valued block indices in the discrete reservoir model, while the well control variables such as bottom hole pressures or injection rates are continuous. Reservoir simulation software is used to evaluate production performance given a well placement and control plan. In the presence of reservoir uncertainty, we sample and simulate multiple model realizations to estimate the expected eld performance. We present a retrospective optimization using dynamic simplex interpolation (RODSI) algorithm for oil Field Development under uncertainty. The numerical results show that the RODSI algorithm efficiently finds a solution yielding a 20% increase (compared to a solution suggested from heuristics) in the expected net present value (NPV) over 30 years of reservoir production for the considered Brugge case.