Real-Time Optimization of an Integrated Gas System

Gobel, Derek; Cramer, Ron; Briers, Jan; Boer, Frank de; Lai, Kok-Lam; Hooimeijer, Martijn

doi:10.2118/145723-ms

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Currently the tools used to provide such framework are brought together by combining multiple special purpose simulation technologies. (2) This type of end to end combination through multiple tools can be useful in some cases. But these network simulations incorporating well, reservoir and process simulators etc., tend to be very slow.…”

Section: Introductionmentioning

confidence: 99%

Role of Flow Assurance in Integrated Asset Modelling-A Comparitive Case Study

Sankararaj¹,

Memon²,

Jagadip³

et al. 2016

Day 2 Wed, March 23, 2016

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Integrated Asset Management (IAM) solutions provides an excellent platform for combining data from multiple sources (subsurface to markets). The level of detail incorporated in such models determines the usefulness of such tools in reservoir management. The integrated modelling tools can be as simple as combining decline profiles to very detailed 3D numerical simulation models coupled to thermodynamic pipelines and facility network with risk management logic represented externally. This study makes an effort to address the issue of differentiating flow assurance models from forecasting tools. The advantages of using IAM tools with optimal detail for questions faced by an engineer are discussed through a real field application.IAM solutions are becoming the norm in the industry to assist engineers and planners formulate sound investment decisions. Modern IAM tools have been documented to increase annual revenues in excess of 100 million USD. In this paper, we discuss the inputs required to build such IAM tools and the types of analysis typically conducted in such tools.The role of flow assurance is inevitable in field development but increasingly we see the misuse of the technology under different circumstances. Here a case study is conducted on a real field Integrated Asset Model comprising of more than 20 gas/gas condensate reservoirs in Oceania. A typical network modelling tool is utilized to construct the entire field model with fields represented through pressure based type curves/ material balance tanks connecting platforms to facilities through pipelines.The IAM model is used to conduct flow assurance study, field development optimization, short-term and long term forecasting. In each case, the effect of pipeline flow formulations, temperature tracking, velocity constraints etc., are discussed and its implications on model runtime and accuracy is presented. The understanding is important to make IAM more efficient and effective to carry out the different scenario analysis.The results show the flow assurance model comprised of detailed representation of flow behavior is useful in field development planning but they tend to be very slow due to large amount of details required. The forecasting model with necessary inputs tends to produce similar results and proves to be effective in conducting large number of scenario analysis.Recommendations are made to an engineer when looking to employ a detailed IAM model aimed at flow assurance as compared to forecasting studies. Guidelines are presented to planners/engineers on awareness of computational overhead when considering an IAM study.

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Section: Introductionmentioning

confidence: 99%

Role of Flow Assurance in Integrated Asset Modelling-A Comparitive Case Study

Sankararaj¹,

Memon²,

Jagadip³

et al. 2016

Day 2 Wed, March 23, 2016

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What are We Going to Do With All These Wells Then?

Cramer

2013

All Days

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Advances in drilling technology (e.g. fracing, horizontal wells) has led to successful development of onshore shale gas, tight gas, coal bed methane, shale oil and burgeoning well numbers. Some Majors have indicated that they will drill up to 20,000 new wells by the end of the decade. To achieve well numbers and cope with the work load and manpower shortages, drilling automation technologies are being introduced that will allow multiple rigs to be operated remotely and downmanned.It is premised that well production operations will also have to be automated to cope with increasing work load as resource will not be available to operate these wells in the traditional, manual way. Associated production processes that will have to be automated include, well surveillance/optimization, well testing, sampling, chemical injection and hydrocarbon accounting. It is desirable to bring the wells onstream as soon as possible after drilling and to maintain production rates at as high a level as possible and to simultaneously account for production of oil, gas and water. It also is imperative to ensure the highest level of safety and environmental factors.Hence, the purpose of this paper is to describe well automation business requirements/benefits and potential system solutions to optimize production over the life cycle for the ever increasing number of onshore wells that will be drilled in the near future.

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Best Practices and Lessons Learned After 10 Years of Digital Oilfield (DOF) Implementations

Saputelli¹,

Bravo

Nikolaou

et al. 2013

All Days

Self Cite

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In the last decade, upstream oil industry faced an exponential increase of the use of real-time data, which lead to numerous digital oilfield (DOF) implementations. These have demonstrated the value to drive operations efficiency, optimize production, and maximize hydrocarbon recovery with better, faster decisions while reducing health, environmental and safety risks.Since the appearance of computers and the internet, many enabling technologies entered the oil-patch. Over the years, various areas improved as a result of significant commercial, corporate and academic efforts. However, some specific concerns remain be the same as a decade ago: data, value proposition, work processes, people skills and other aspects of change management. This paper focuses on the best practices that have made DOF implementations successful and the hard lessons learned. Many DOF implementations failed to deliver the expected value because of poor practices and misconceptions. These are presented in four interrelated areas: people, automated workflows, processes and technologies.

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Real-Time Optimization of an Integrated Gas System

Cited by 4 publications

References 7 publications

Role of Flow Assurance in Integrated Asset Modelling-A Comparitive Case Study

Role of Flow Assurance in Integrated Asset Modelling-A Comparitive Case Study

What are We Going to Do With All These Wells Then?

Best Practices and Lessons Learned After 10 Years of Digital Oilfield (DOF) Implementations

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