Field Development Optimization of Siberian Giant Oil Field under Uncertainties

Volz, Richard Franklin; Burn, Karam; Litvak, Michael; Thakur, Subhash C.; Skvortsov, S. V.

doi:10.2118/116831-ms

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. [1] Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. The Fig.…”

Section: Ga Methodsmentioning

confidence: 99%

“…These approaches use optimization methods to maximize the oil recovery for reservoirs under waterflooding stage. It can be found applications implemented in the Gulf of México [1][2][3] solving nonlinear programming problems, or methodologies similar to the work proposed by Jansen et al [4] which include a regulatory layer and history matching by using Ensemble Kalman Filter. The two-level closed Loop Reservoir Control strategy is detailed in works developed by Van Essen et al [5], A. Razapour [6] and E. Gildin [7].…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Waterflooding Process for Recovery Using Nonlinear Model Predictive Control: Case Yariguí - Cantagallo

Mejía

Suárez

Navarro³

2014

SPE Latin America and Caribbean Petroleum Engineering Conference

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This paper describes the results of applying a strategy of nonlinear model predictive control (NMPC) for closed-loop optimization of the water flooding process simulation on a reversed pattern of 5 wells in the field YARIGUÍ -CANTAGALLO operated by Ecopetrol SA in Colombia.Field modeling and predictions are made through the use of a commercial reservoir simulator. The solution of the optimization problem of nonlinear control loop is determined using an approach that uses the reservoir model as a black-box by looking for patterns and sequential algorithms.To manage the information of operational variables required by the optimization strategy, an interface was developed by managing online the output files of the commercial simulator. NMPC algorithm stability is achieved by finding sub-optimal solutions of the optimization problem.The robustness and performance of the NMPC strategy is illustrated by its implementation to optimize the water flooding process simulation represented by a model with 814.226 cells of which 352.034 are active, 199 layers, 38 failures, 26 areas of balance and heterogeneous distributions of permeability and porosity. Thus, by appropriate adjustment of the water flow rates, it is proposed to increase the production of oil for the case study. Finally the main findings, conclusions and recommendations from this study are submitted.

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Section: Ga Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Waterflooding Process for Recovery Using Nonlinear Model Predictive Control: Case Yariguí - Cantagallo

Mejía

Suárez

Navarro³

2014

SPE Latin America and Caribbean Petroleum Engineering Conference

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A New Well-Pattern-Optimization Procedure for Large-Scale Field Development

Onwunalu

Durlofsky

2011

SPE Journal

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Summary The optimization of large-scale multiwell field-development projects is challenging because the number of optimization variables and the size of the search space can become excessive. This difficulty can be circumvented by considering well patterns and then optimizing parameters associated with the pattern type and geometry. In this paper, we introduce a general framework for accomplishing this type of optimization. The overall procedure, which we refer to as well-pattern optimization (WPO), includes a new well-pattern description (WPD) incorporated into an underlying optimization method. The WPD encodes potential solutions in terms of pattern types (e.g., five-spot, nine-spot) and pattern operators. The operators define geometric transformations (e.g., stretching, rotating) quantified by appropriate sets of parameters. It is the parameters that specify the well patterns and the pattern operators, along with additional variables that define the sequence of operations, that are optimized. A technique for subsequent well-by-well perturbation (WWP), in which the locations of wells within each pattern are optimized, is also presented. This WWP represents an optional second phase of WPO. The overall optimization procedure could be used with a variety of underlying optimization methods. Here, we combine it with a particle-swarm-optimization (PSO) technique because PSO methods have been shown recently to provide robust and efficient optimizations for well-placement problems. Detailed optimization results are presented for several example cases. In one case, multiple reservoir models are considered to account for geological uncertainty. For all examples, significant improvement in the objective function is observed as the algorithm proceeds, particularly at early iterations. The use of well-by-well perturbation (following determination of the optimal pattern) is shown to provide additional improvement. Limited comparisons with results using standard well patterns of various sizes demonstrate that the net present values (NPVs) achieved by the new algorithm are considerably larger. Taken in total, the optimization results highlight the potential of the overall procedure for use in practical field development.

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Brownfield Development Optimization under Uncertainty – A Structured Workflow Design for Complex Case Scenarios

Schulze-Riegert

Magdeyev

Komin

et al. 2017

Day 3 Wed, October 18, 2017

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An increasing number of field development projects include rigorous uncertainty quantification workflows based on parameterized subsurface uncertainties. Reservoir model calibration workflows for simulation studies including historical production data, also called history matching, deliver non-unique solutions and remain technically challenging. This work presents an integrated workflow design for brownfield development optimization projects under uncertainty. Challenges related to complex simulation models with long run times are addressed. Proxy modeling techniques are introduced for performance improvement with application to history matching and optimizing field developing scenarios. Selection strategies of multiple history-matched prediction candidates for estimating prediction uncertainties are presented. Workflow designs for history matching require scalable and efficient optimization techniques to address project needs. A structured workflow design is introduced for addressing complex project requirements and to define intermediate milestones for project reviews. Experimental design techniques are introduced for sensitivity analysis and dimension reduction. History matching processes are built on a series of parameter screening and derivative-free optimization techniques. We introduce Markov Chain Monte Carlo (MCMC) techniques for optimization and uncertainty quantification. Rejection filtering is used for selecting alternative prediction candidates in a multi-objective solution space. Field development optimization workflows define challenges due to the nature of a high-dimensional discrete option space. We present a combined usage of experimental design techniques for generating training data sets and proxy modeling techniques for extensively screening a multi-dimensional control parameter space. Visualization techniques are used to present a solution-frontier of optimized field development scenarios meeting multi-objective optimization criteria, e.g., economic field performance criteria as well as field production targets. The structured workflow was designed and applied to an undisclosed field development optimization project. In this paper we describe the motivation and benefit of experimental design and optimization techniques with application in various phases of the structured workflow from history matching to field development optimization under uncertainty. Complexities in prioritizing selection criteria of cross-disciplinary static and dynamic uncertainty parameters included in a multi-dimension uncertainty matrix are discussed under practical integration criteria. Performance criteria for workflow deliveries in time and quality are critical in decision processes for moving forward within the project phases and for delivering supportive results for reservoir project management. We present decision criteria at which workflow stages added computation resources are beneficial and which methods are scalable and support distributed computing-technology for reducing elapsed project time.

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Field Development Optimization of Siberian Giant Oil Field under Uncertainties

Cited by 3 publications

References 2 publications

Optimizing Waterflooding Process for Recovery Using Nonlinear Model Predictive Control: Case Yariguí - Cantagallo

Optimizing Waterflooding Process for Recovery Using Nonlinear Model Predictive Control: Case Yariguí - Cantagallo

A New Well-Pattern-Optimization Procedure for Large-Scale Field Development

Brownfield Development Optimization under Uncertainty – A Structured Workflow Design for Complex Case Scenarios

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