Efficient Assessment and Optimization of a Deepwater Asset Using Fixed Pattern 
Approach

Özdogan, Umut; Sahni, Akshay; Yeten, B.; Güyagüler, Baris; Chen, Wen

doi:10.2523/95792-ms

Cited by 7 publications

(7 citation statements)

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“…Other researchers combined finite difference with various proxy models or response surface models (RSM) to reduce necessary reservoir simulations (e.g. least squares by Pan and Horne (1998), kriging by Pan and Horne (1998), Guyaguler and Horne (2000) and Ozdogan et al (2005), artificial neural networks by Centilmen et al (1999) and Yeten et al (2003), statistical proxy model by Onwunalu et al (2008), and neuro-fuzzy proxy model by Zarei et al (2008)). Many researchers have focused on addressing this problem by studying alternative stand-alone reservoir response models for well placement application.…”

Section: Reservoir Response Modelsmentioning

confidence: 99%

“…multi-well and nonconventional well optimization). Due to this limitation, in field application, the use of some acceleration methods to reduce the number of simulations (as in hybrid GA introduced by Yeten et al (2003) and Ozdogan et al (2005)) becomes a necessity. However, this may come with the possible cost of some reduction in the method's reliability.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

“…They applied their method to optimize the number, type, trajectory, and location of wells. GA was used by Ozdogan et al (2005) for well placement optimization in pattern waterflooding projects in a new fixed-pattern Approach. In this GA application, the decision variables (for a line-pattern waterflooding) were reduced to: 1) distance of the wells to reservoir boundary, and 2) well spacing.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Well placement optimization: A survey with special focus on application for gas/gas-condensate reservoirs

Nasrabadi

Morales

Zhu

2012

Journal of Natural Gas Science and Engineering

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Section: Reservoir Response Modelsmentioning

confidence: 99%

Section: Genetic Algorithmsmentioning

confidence: 99%

Section: Genetic Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Well placement optimization: A survey with special focus on application for gas/gas-condensate reservoirs

Nasrabadi

Morales

Zhu

2012

Journal of Natural Gas Science and Engineering

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“…An alternative parameterization of the search space could be a "fixed pattern" approach, Ozdogan (2005). In that approach well locations are discretized along predefined search paths.…”

Section: Parameterization Of Search Spacementioning

confidence: 99%

Multiple-Objective Optimization Applied to Well Path Design under Geological Uncertainty

Schulze-Riegert¹,

Bagheri²,

Krosche³

et al. 2011

SPE Reservoir Simulation Symposium

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Well placement and design under geological uncertainties defines a major risk for field development processes. Including alternative geological realizations in a manually optimized development plan has been cumbersome and time consuming. In practical cases, a single reference model was often used as a basis for the flow simulation model which included a major risk on underestimating geological uncertainties. Optimization approaches and related optimization methods applied in this field previously included an objective function definition which aggregates multiple objectives, e.g. cumulative well rates or economical indicators. These optimization methods do not allow distinguishing easily between correlated and uncorrelated objectives which defines a major limitation for efficiency. This paper presents a novel approach individually linking objectives and design parameters to multiple optimization processes which operate in parallel on a single simulation case. This approach increases the efficiency of stochastic optimization processes for complex application scenarios with multiple objectives. The extension to an uncertainty workflow including multiple geological models in the optimization processes is discussed.Well placement optimization of horizontal wells in independent target formations of the gas condensate field is used to verify the optimization methodology. Alternative geological models are included in the optimization workflow representing geological uncertainties. The specific objective of the case study is to find optimum well trajectories by maximizing cumulative gas production over the life cycle of the field. Performance indicators for all well candidates (design parameters) are related to individual objectives (regional production). Robustness criteria for well candidates are implicitly fulfilled on the basis of risk weighted geological realizations.

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“…However, proxy-model seems to be more accepted in the petroleum industry and will be used in this paper (Zubarev, 2009). Typical application areas in reservoir simulation include Sensitivity analysis of uncertainty variables, Probabilistic forecasting and risk analysis (see Yeten et al, 2005), Conditioning of a simulation model to historically observed data (history matching; see Cullick et al, 2006;Junker et al, 2006;Slotte and Smorgrav, 2008), and Field development planning and production optimization (see Pan and Horne, 1998;Guyaguler et al, 2000;Badru and Kabir, 2003;Ozdogan et al, 2005;Yeten, 2007).…”

Section: Introductionmentioning

confidence: 99%

A New Approach for the Development of Fast-analysis Proxies for Petroleum Reservoir Simulation

Sefat

Salahshoor

Jamialahmadi

et al. 2012

Petroleum Science and Technology

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The bottleneck of all processes that are using field-scale numerical simulators is the computationally expensive objective function evaluation. Hence, always a gap exists between simulation runs and real-time processing. In this study, a new approach is presented that uses online-adaptive artificial neural networks to develop proxies that mimic the behavior of the actual reservoir simulator. In this approach, initially Latin hypercube sampling is used and then an intelligent sample selection algorithm is developed to improve the online network prediction. The cited approach improves the surrogate model development in two directions. First, proxies can be used while they are developing and, second, samples are selected intelligently and this reduces computational cost.

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Efficient Assessment and Optimization of a Deepwater Asset Using Fixed Pattern Approach

Cited by 7 publications

References 0 publications

Well placement optimization: A survey with special focus on application for gas/gas-condensate reservoirs

Well placement optimization: A survey with special focus on application for gas/gas-condensate reservoirs

Multiple-Objective Optimization Applied to Well Path Design under Geological Uncertainty

A New Approach for the Development of Fast-analysis Proxies for Petroleum Reservoir Simulation

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