Model identification of nonlinear time variant processes via artificial neural network

Nikravesh, Masoud; Farell, A.E.; Stanford, Thomas G.

doi:10.1016/0098-1354(95)00245-6

“…These weights are adjusted to improve petiormance, depending on the task at hand. They are either determined via some prescribed off-line algorithm and thus remain freed during operation, or adjusted on-line via a learning process [5,18]. The node weights provide the memory which is necessary in a learning process.…”

Section: Neural Networkmentioning

confidence: 99%

Neural Networks for Field-Wise Waterflood Management in Low Permeability, Fractured Oil Reservoirs

Nikravesh

¹

,

Kovscek

²

,

Murer

³

et al. 1996

Proceedings of SPE Western Regional Meeting

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policies that lead to the minimum injected water and the best ThM paper w= prepared for p!'cstnlatmn ut [he Wih SPE Western Rcgmnal Mceung held , n Anchorage AK, 22.24 May l!)% 'This paper wu selected for presentalmn hy [hc SPE Program Comm!uce followlng rcwew of miormation ccmtamcd In tin ahs!ract suhmtttcd hy the aughtx( s). Contents of the paper as presented have mu hccn rewewcc hy the Sccae[y of Pctmlcum Englncers and are suhjcct to correction(s) hy (he authr>r(s) The material, as presented, does no[ nccesswdy reflect any pclwtiofi :,f (he SocIeIY O( Pctmlcum Engineers or IIS mcmhers Papers prrsenmd at SPE meel,n~s arc subject [n puhhciwm rewew hy Edthmal Commmecs of the Srciety of Petroleum Engineers. Penmssmn to copy IS rcsmcld to m ahstmcl of noi more than 3(H) words Illuslratmns may not he cop.sd The abstract should contain consplcums acknowledgment of where and hy whom tic pa~r WM prcscn!cd. AbstractAn optimal water injection policy maximizes oil recovery per barrel of injected water while minimizing formation darnage and maintaining reservoir pressure. Optimal water injection into low permeability, tiactr.md oil reservoirs is problematic because of highly nonlinear and complex reservoir dynamics. Likewise, current first principle models of fluid movement in fractured, low permeability rock systems are insufficient to design, operate, and predict the performance of large scale waterflood, Historically, the conflict between prudent reservoir management and meeting field injection-production targets has resulted in reservoir and well damage, injectant recirculation and irreversibly lost oil production.Here we present the next generation of "intelligent" field surveillance and prediction software based on neural networks and implemented on a PC. We demonstrate a new approach to field-wise performance prediction and optimization cf waterfloods that recognizes an oil field as a coupled, highly nonlinear system of injectors and producers. With lease-wide historical data tlom a waterflood in the Lost Hills Diatomite (Kern County, CA), we construct several neural networks which recognize that individual well behavior may depend on well history and the injection-production conditions of surrounding wells. Some of our neural networks accurately predict wellhead pressure as a function of injection rate, and vice versa, for all injectors. Other networks history-match oi I and water production on the well-by-well basis, and predict firture production on a quarterly or half-year basis. Finally, our neural networks recognize and suggest water injection oil recove2y.

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“…These weights are adjusted to improve performance, depending on the task at hand. They are either determined via some prescribed off-line algorithm and thus remain fixed during operation, or adjusted on-line via a learning process [5,18]. The node weights provide the memory which is necessary in a learning process.…”

Section: Neural Networkmentioning

confidence: 99%

Neural Networks for Field-Wise Waterflood Management in Low Permeability, Fractured Oil Reservoirs

Nikravesh

¹

,

Kovscek

²

,

Murer

³

et al. 1996

SPE Western Regional Meeting

Self Cite

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policies that lead to the minimum injected water and the best ThM paper w= prepared for p!'cstnlatmn ut [he Wih SPE Western Rcgmnal Mceung held , n Anchorage AK, 22.24 May l!)% 'This paper wu selected for presentalmn hy [hc SPE Program Comm!uce followlng rcwew of miormation ccmtamcd In tin ahs!ract suhmtttcd hy the aughtx( s). Contents of the paper as presented have mu hccn rewewcc hy the Sccae[y of Pctmlcum Englncers and are suhjcct to correction(s) hy (he authr>r(s) The material, as presented, does no[ nccesswdy reflect any pclwtiofi :,f (he SocIeIY O( Pctmlcum Engineers or IIS mcmhers Papers prrsenmd at SPE meel,n~s arc subject [n puhhciwm rewew hy Edthmal Commmecs of the Srciety of Petroleum Engineers. Penmssmn to copy IS rcsmcld to m ahstmcl of noi more than 3(H) words Illuslratmns may not he cop.sd The abstract should contain consplcums acknowledgment of where and hy whom tic pa~r WM prcscn!cd. AbstractAn optimal water injection policy maximizes oil recovery per barrel of injected water while minimizing formation darnage and maintaining reservoir pressure. Optimal water injection into low permeability, tiactr.md oil reservoirs is problematic because of highly nonlinear and complex reservoir dynamics. Likewise, current first principle models of fluid movement in fractured, low permeability rock systems are insufficient to design, operate, and predict the performance of large scale waterflood, Historically, the conflict between prudent reservoir management and meeting field injection-production targets has resulted in reservoir and well damage, injectant recirculation and irreversibly lost oil production.Here we present the next generation of "intelligent" field surveillance and prediction software based on neural networks and implemented on a PC. We demonstrate a new approach to field-wise performance prediction and optimization cf waterfloods that recognizes an oil field as a coupled, highly nonlinear system of injectors and producers. With lease-wide historical data tlom a waterflood in the Lost Hills Diatomite (Kern County, CA), we construct several neural networks which recognize that individual well behavior may depend on well history and the injection-production conditions of surrounding wells. Some of our neural networks accurately predict wellhead pressure as a function of injection rate, and vice versa, for all injectors. Other networks history-match oi I and water production on the well-by-well basis, and predict firture production on a quarterly or half-year basis. Finally, our neural networks recognize and suggest water injection oil recove2y.

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“…During the past few years, neural networks have been applied effectively as controllers for time varying processes with highly nonlinear behavior. It has been shown that the neural network model based control strategies are robust enough to perform well over a wide range of operating conditions, and they are much easier to design and implement than classical PID control [ 17]. Currently, we are developing a neural network model based control strategy for water and steam injectors to augment the present PID control strategy.…”

Section: Fracture Extensionmentioning

confidence: 99%

“…Details regarding neural networks are available in the literature [9,10,16,17]. Therefore, only the important network characteristics are mentioned here.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Formation Damage During Fluid Injection into Fractured, Low Permeability Reservoirs via Neural Networks

Nikravesh¹,

Kovscek²,

Johnston³

et al. 1996

Proceedings of SPE Formation Damage Control Symposium

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Richardson, TX 75083-3836 U.S.A., fax 01-214-952-9435. AbstractThe coupled, nonlinear and dynamic mechanisms that affect fluid injection for pressure maintenance or displacement, and oil production, are not well understood in low permeability fractured reservoirs. Thus, it is difficult to select an injection policy which maximizes oil recovery while minimizing formation damage caused by fluid injection and withdrawal. Here, we show that neural network models can be developed and used to predict, on a well-by-well basis, the dynamics of low permeability, fractured reservoirs undergoing fluid injection. The networks are trained using historical data from field operations.We present an example from (i) a water and (ii) a steam injection project where over-pressurization has lead to unwanted extensions of fractures. First, using data from a waterflood project in the South Belridge Diatomite (Kern County, CA), we have built a neural network to predict wellhead pressure as a function of injection rate, and vice versa. The resulting model provides an excellent correlation between the inputs and outputs and recognizes major patterns in the input data structure, even though the behavior of the waterflood is complex. Second, using data from a dual injector steamdrive pilot in the same field, we have created neural networks which correlate the injection 351 pressures and rates, and temperature responses in seven observation wells.· Assuming a future injection pressure policy, the neural networks predict the injection rate and growth of heated reservoir volume. These predictions are then combined with a historymatching reservoir simulator to demonstrate how predictive simulation can be achieved even when mechanisms of steam injection and oil displacement into a tight fractured rock are not fully understood.

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Model identification of nonlinear time variant processes via artificial neural network

Cited by 37 publications

References 13 publications

Neural Networks for Field-Wise Waterflood Management in Low Permeability, Fractured Oil Reservoirs

Neural Networks for Field-Wise Waterflood Management in Low Permeability, Fractured Oil Reservoirs

Neural Networks for Field-Wise Waterflood Management in Low Permeability, Fractured Oil Reservoirs

Prediction of Formation Damage During Fluid Injection into Fractured, Low Permeability Reservoirs via Neural Networks

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