Optimizing the Utilization of Miscible Injectant at the Greater Prudhoe Bay Fields

Ning, Samson; Jhaveri, Bharat S.; Fueg, Esther; Stechauner, G..; Jemison, J. L.; Hoàng, Thái

doi:10.2118/180420-ms

Cited by 7 publications

(1 citation statement)

References 22 publications

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“…At the Prudhoe Bay field in Alaska, one of the largest oil fields in North America, operators have increased the recovery factor substantially due to gas injection, together with other techniques (see e.g. Ning et al (2016) or Szabo & Meyers (1993)). The associated gas being produced together with the oil is reinjected into the reservoir.…”

Section: Introductionmentioning

confidence: 99%

Switching from oil to gas production in a depleting field

Støre

Fleten

Hagspiel

et al. 2018

European Journal of Operational Research

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We derive an optimal decision rule with regards to making an irreversible switch from oil to gas production. The approach can be used by petroleum field operators to maximize the value creation from a petroleum field with diminishing oil production and remaining gas reserves. Assuming that both the oil and gas prices follow a geometric Brownian motion we derive an analytical solution for the exercise threshold. We also propose an explicit solution for the option value that is new to the literature. Numerical examples are used to demonstrate the threshold and option value for a generic petroleum field. Both the threshold and option value solutions are relevant for application to other real options cases with similar features (e.g. other types of switching options or a perpetual spread option).

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Section: Introductionmentioning

confidence: 99%

Switching from oil to gas production in a depleting field

Støre

Fleten

Hagspiel

et al. 2018

European Journal of Operational Research

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Application of Dynamic Allocation Factors for Waterflood and EOR Performance Management

McElhaney¹,

Jemison²,

Jhaveri³

2016

SPE Western Regional Meeting

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Field-wide displacement and EOR floods become increasingly difficult to monitor and analyze as a system matures. Dynamic effects such as slug size, well work, pressure management, and concurrent injection can all influence performance in a way that is not captured using geometric static allocation factors. By calculating dynamic allocation factors for each time step, the dynamics of field operations can be attributed to the corresponding wells. Proper allocations can influence field operating strategies by using past performance to predict future returns in the field. Building on the concept presented in 2006 by Bharat Jhaveri, the dynamic allocation factors are calculated by an iterative process that includes all injection and production data in a time-step. Production-centered allocation factors are dependent on the surrounding volumes of injection supporting the well. Injector-centered allocation factors are dependent on the surrounding production volumes that the injector supports. Interactions are monitored and tracked using a matrix style catalog for each time step. Total injection allocation factors can then be modified for EOR application by taking into account the delayed production enhancing effects caused by solvent injection. Dynamic allocation factor analysis was completed using real field data on a system with two injectors and fourteen producers. These injectors injected both water and miscible gas on regular WAG cycles. The results of the analysis were used to evaluate waterflood and EOR performance. For the waterflood, maturity, recovery, and pressure management are the metrics required to evaluate field performance. For the EOR performance, maturity, recovery, and utilization are used to optimize oil recovery. Dynamic allocation factors were able to capture lag time in injector-producer interactions, highlight non-interactions between wells owing to geological influence, and changes in reservoir streamlines during periods of shut-in wells. Traditional well allocation factors between injectors and producers are assigned based on the geometry of that pattern. This method provides a static, unchanging view of the reservoir system, regardless of the operational dynamics. A dynamic allocation factor is derived iteratively to accurately depict reservoir interactions through time. Field management can be improved using this view of how fluids in the reservoir have historically interacted. Injection targets, infill drilling, and changes to injector conformance can all be optimized through analysis employing dynamic allocation factors.

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Delivering Drilling Automation II – Novel Automation Platform and Wired Drill Pipe Deployed on Arctic Drilling Operations

Israel¹,

McCrae²,

Sperry³

et al. 2018

SPE Annual Technical Conference and Exhibition

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This paper presents a case history of drilling automation system pilot deployment, inclusive of wired drill pipe on an Arctic drilling operation. This builds on the body of work that BP (the operator) previously presented in 2017 related to the deployment of an alternate drilling automation system. The focus will be on the challenges and lessons learned during this deployment over a series of development wells. Two major aspects of technology were introduced during this pilot, the first being a drilling automation software platform that allowed secure access to the rig's drilling control system. This platform hosts applications that interpret the activity on the rig and issue control setpoints to drive the operation of the rig's top drive, mud pumps, auto driller, drawworks, and slips. The second component introduced was a wired drill string, which provides access to high speed delivery of downhole data from a series of distributed downhole sensors, providing an opportunity to improve both automated control and real-time interpretation of downhole phenomena. The project team identified several key performance indicators both at the project level and for each well. The project level key performance indicators (KPIs) were designed to give the operator an understanding of the reliability and robustness of the hardware and software components of the automation system. The KPIs for the well were designed to assess the impact of the technology on drilling efficiency through aspects of invisible lost time reduction (connection and survey times). The well level KPIs also fed into the project KPIs by capturing uptime, reliability, and repeatability of the hardware and software components of the system. The paper describes several specific examples of where the benefits of the technology were realized as related to the KPIs above and describes some of the technical challenges encountered and fixes employed during the pilot campaign. The paper also gives an insight into some of the non-technical challenges related to deployment of this system, around human behavioral characteristics. It discusses how focused collaboration and communication from all the stakeholders was managed and directed towards a successful deployment. The work delivered on this project incorporates several technological innovations that were deployed for the first time on an active drilling operation. Delivery of these were important milestones for both the operator and the automation technology provider as part of their collaboration to increase the capability and reliability of these systems. The operator believes that this effort is key to allowing its drilling operations to realize longer term and sustainable benefits from automation.

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Optimizing the Utilization of Miscible Injectant at the Greater Prudhoe Bay Fields

Cited by 7 publications

References 22 publications

Switching from oil to gas production in a depleting field

Switching from oil to gas production in a depleting field

Application of Dynamic Allocation Factors for Waterflood and EOR Performance Management

Delivering Drilling Automation II – Novel Automation Platform and Wired Drill Pipe Deployed on Arctic Drilling Operations

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