Applications of a Field Surface  &amp;  Production Network Simulator Integrated With a Reservoir Simulator

Hepguler, G.; Dutta-Roy, Kunal; Bard, Wade

doi:10.2523/38007-ms

Cited by 2 publications

(2 citation statements)

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“…Most of the published reservoir/surface network integration systems are:  Commercially available products employing parallel, open server computing architecture with full or partial coupling of the two simulators ( Figure 2) 12,13,14,15 ,  Incorporated in the reservoir simulators through implicit, full or partial, coupling of the subsurface/surface network with the reservoir, or  Specially developed programs for specific application within an organisation that have not been made generally (commercially) available.…”

Section: Literature Reviewmentioning

confidence: 99%

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Successful Application of a Robust Link To Automatically Optimise Reservoir Management of a Real Field

Al-Khelaiwi¹,

Davies²,

Tesaker

et al. 2007

All Days

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1. Abstract Realistic modelling is essential for both the planning and the optimal operation of Oil and Gas Fields. Such a model for modern well or field development architecture requires coupling of the reservoir simulator with the well/surface facility network model when making choices as to the reservoir and production management strategies to be employed. Such close coupling is not, currently, readily available; particularly when the reservoir simulator, the well/surface facility simulator and, potentially, the optimiser programs are provided by different suppliers. We have had the opportunity to test a newly developed "link tool" to integrate the reservoir simulation model with a subsurface/surface network model, allowing (automatic) optimisation of the full network performance. The tool supplies the simulation results to the surface network simulator/optimiser, which in turn, reconfigures the intelligent well completion zones by use of Individual Control Valves (ICVs) and wellhead or manifold in order to maximise the total production against the well and facility constraints. The network targets are then returned to the reservoir simulator in a simple manner at each time step. The authors have used the S-Field over a number of years which proved to be a very suitable case study for illustration of the value of "Intelligent" Field development techniques. This paper discusses the automatic, optimal control of the S-Field's five intelligent production wells by application of a "link tool" (Supplier 1) to couple the reservoir simulator (Supplier 2) with a surface network modeller and optimiser (Supplier 3). N.B. The latter two suppliers are among the market leaders within their segments. 2. Introduction The integration of the reservoir simulator with the wellbore and surface facility models forms an essential part of the "intelligent" field concept. It allows accurate management of the reservoir(s) potential under specified well, facility or other constraints. The full value of such an integrated modelling workflow is only realised when a flow network optimisation capability that maximizes oil production (or other measures of value) is included in the software package. The optimiser works by making adjustments to the production strategy throughout the field life through its close coupling with the reservoir, wellbore and surface facility models. Many commercial software packages offer the capability to integrate subsurface and surface models. However, not only are there differences in the degree of coupling between the individual software programs, but these links usually place high demands in terms of computing power, network architecture and, frequently, manual intervention of the engineer. In this paper we will illustrate a successful application of a robust and efficient linking tool developed to couple a commercial reservoir simulator with a surface network simulator and Sequential Quadratic Programming (SQP) optimiser 1,2. The S-Field (a case study based on redevelopment of a real field with an Advanced or Intelligent Well development strategy) has been previously studied in-depth. It has been found to be a very useful case history to illustrate the potential advantages of implementing an Advanced Well development scheme 2,3. We have been able to show how the SQP optimiser can be used to increase the recovery compared to manual optimisation. This was achieved with a limited engineering manpower compared to the previous manual optimisation approach while the computer power requirements were less than that anticipated with other commercial optimisation software packages.

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Section: Literature Reviewmentioning

confidence: 99%

“…al. 12 of a tightly coupling interface between the reservoir simulator provided by Supplier 2 and the network simulator and optimiser provided by Supplier 4. The coupling was based on a Parallel Virtual Machine (PVM) interface and required convergence of the surface and reservoir simulators results at every time step.…”

Section: Literature Reviewmentioning

confidence: 99%

Successful Application of a Robust Link To Automatically Optimise Reservoir Management of a Real Field

Al-Khelaiwi¹,

Davies²,

Tesaker

et al. 2007

All Days

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Multi-Field Asset Integrated Optimization Benchmark

Rahmawati¹,

Whitson²,

Foss³

et al. 2010

SPE EUROPEC/EAGE Annual Conference and Exhibition

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Integrated modeling of multi-field assets, from subsurface to market, is challenging due to the complexity of the problem. This paper is an extension of the SPE 121252, model based integration and optimization gas cycling benchmark [Juell, et al., 2009], extending two gas-condensate fields to two full-field multi-well models. Additionally, a full-field model is added to the Juell benchmark, introducing an oil field undergoing miscible WAG injection, where most data are taken from the SPE 5 Reservoir Simulation Comparative Project. All reservoir models are compositional, but using different EOS representations. A base case scenario is defined with fixed numbers and locations of producers and injectors. A common field-wide surface processing facility is modeled with emphasis on water handling, NGL extraction, sales-gas spec, and gas reinjection. The surface process model interacts with the three reservoir models through two main mechanisms-(1) water-and gas-handling constraints, and (2) distribution of available produced gas for reinjection into the three reservoirs. The field asset model provides long-term production forecasts of gas, oil, and NGL revenue. Cost functions are introduced for all major control variables (number of wells, surface facility selection and operating conditions, injection gas composition). Net present value is used as the target objective function. This paper will evaluate optimal production strategies for the base case benchmark problem, using several key control variables and field operational constraints. Optimization performance will be tested with a few solver algorithms. The benchmark will be provided to the industry through application data files, network infrastructure, and results from our integrated optimization model.

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Applications of a Field Surface & Production Network Simulator Integrated With a Reservoir Simulator

Cited by 2 publications

References 0 publications

Successful Application of a Robust Link To Automatically Optimise Reservoir Management of a Real Field

Successful Application of a Robust Link To Automatically Optimise Reservoir Management of a Real Field

Multi-Field Asset Integrated Optimization Benchmark

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