Model-Based Integration and Optimization—Gas-Cycling Benchmark

Juell, Aleksander; Whitson, Curtis Hays; Hoda, Mohammad Faizul

doi:10.2118/121252-pa

Cited by 7 publications

(2 citation statements)

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“…A particular interesting target for model-based optimization is the typical development plan for green fields. The reason is that such plans usually include a wide range of decisions, e.g., well placement, reservoir production strategy and facility layout, that may be treated simultaneously for a significant gain in profit (see Juell et al, 2010, for a discussion of integrated optimization (I-OPT), though that work uses a larger field operation perspective than the one taken here). In this work, we regard development plans of petroleum assets (and subsequent problem formulations based on these) as "integrated" in the sense that these plans and problems represent systems that are composed of interdependent subsystems.…”

Section: Motivation For Optimizationmentioning

confidence: 99%

Joint optimization of oil well placement and controls

et al. 2012

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In this thesis we optimize the drilling location and operational controls of wells in a joint manner to improve the overall development strategy for a petroleum field. In particular, in this thesis we treat the integrated problem of searching for an improved well placement configuration while also taking into account the control settings of the production and/or injector wells planned for the development of the hydrocarbon asset. In oil field development, the well placement and well control problems are commonly performed in a sequential manner. However, this type of sequential approach cannot be expected to yield optimal solutions because it relies on handling well production controls using heuristic techniques during the well placement part of the procedure. In this work, we develop a nested (joint) optimization approach that seeks to capture the interdependency between the well configuration and the associated controls during the optimization search.This thesis summarizes the development of the joint approach; from establishing the methodology while using relatively simple cases and performing thorough comparisons against sequential approaches, to further extending and finally testing the methodology using a real field case model. This progression naturally divides the work in this thesis into two parts with different research focus. The first part of this work (Chapter 2) focuses chiefly on creating proper definitions and on establishing the proposed methodology against common approaches. The second part of this thesis (Chapters 3 and 4), on the other hand, focuses mainly on applying the developed methodology within a real field case scenario involving the North Sea Martin Linge oil reservoir. The dual aim of this application work is both to further develop the methodology, and to produce and test optimization solutions that may serve as decision-support to engineering efforts within the development work process of the Martin Linge field.Chapter 2 establishes the core of the methodology followed in this thesis. This chapter introduces the joint and sequential approaches as different ways to solve for the coupled well placement and control problem. The joint approach embeds the well control optimization within the search for optimum well placement configurations. Derivativefree methods based on pattern search are used to solve for the well-positioning part of the problem, while the well control optimization is solved by sequential quadratic programming using gradients efficiently computed through adjoints. Compared to reasonable sequential approaches, the joint optimization yields a significant increase in net present value of up to 20%. Compared to the sequential procedures, though, the joint approach requires about an order of magnitude increase in the total number of reservoir simulations performed during optimization. This increase, however, is somewhat mitigated by i the parallel implementation of some of the pattern search algorithms used in this work.Chapter 3 focuses on extending and applying ...

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Section: Motivation For Optimizationmentioning

confidence: 99%

Joint optimization of oil well placement and controls

et al. 2012

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“…Juell et al (2009) and Rahmawati et al (2010) discussed integrated asset modeling and evaluation from the reservoirs to the surface using gas condensate and oil reservoirs. Pipe-It® software was used as an integrated asset software to connect the SENSOR® reservoir simulator and the Hysys® surface process model.…”

Section: Introductionmentioning

confidence: 99%

The Benefit of Integrated Asset Modeling and Optimization in Evaluating Production Strategies under various Oil and Gas Price Scenarios

Rahmawati¹,

Hoda²

2015

Day 2 Tue, November 10, 2015

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The decrease in oil and gas prices results in a huge impact in the oil and gas company. This paper discusses the importance of integrated asset modeling and optimization of oil and gas fields under various oil and gas price scenarios. The integrated model is defined as simulation runs from the reservoir up to the surface process and continues with an economic evaluation.Various field models under different production strategies are presented in the paper and evaluated based on the integrated asset model. The reservoir models are a gas condensate reservoir under gas cycling and an oil reservoir under water alternating gas (WAG) injection production strategies taken from SPE 3 and SPE 5 comparative projects. Derivative free optimization methods are used to find the best operating conditions for the field. The optimization objective is to maximize economic key performance indicators, such as net present value, and to minimize cost, such as gas injection cost. The decision variables are gas injection fraction, gas injection time, water injection rate and WAG time.Using integrated system modeling, the operating parameters can be determined accurately considering reservoir and surface process capabilities. The optimization results showed that optimum gas injection time is longer during high oil prices compared to low oil prices for the gas condensate reservoir under gas injection. The oil and gas prices are fluctuating in a certain cycle over time, and the dynamic fluctuations are accommodated in the economic model. Many reasons are behind the dynamic fluctuations of oil and gas prices. The fluctuations will have an impact on company production strategies. The economic model will be simulated under very high, high, intermediate, low and very low price scenarios. For each scenario, the model will be run in the integrated modeling and reservoir only modeling. A scenario of varying the ratio of oil to gas price is also included in the analysis. Different oil and gas prices will lead to different management decisions during the field lifetime. A conservative production strategy, meaning when the field is simulated separately part-by-part, could mislead field production decisions. Based on the reservoir simulation, the liquid production might flow easily from the reservoir to the surface. However, based on the surface simulation, the well might be shut᎑down due to flow restriction on the surface. This paper presents the important concept of the integrated model and optimization under dynamic oil and gas prices. The oil and gas prices have an influence in determining production parameters. The integrated model that is used in the paper is flexible and can be replaced with another reservoir model, surface process model, or economic models.

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