ESP "Smart Flow" Integrates Quality and Control Data for Diagnostics and Optimization in Real Time

Al-Jasmi, Ahmad; Nasr, Hatem N.; Goel, H. K.; Moricca, Giuseppe; Carvajal, Gustavo; Dhar, J.; Querales, M.; Villamizar, M.; Cullick, A.S.; Rodríguez, José Antonio Alonso; Velasquez, Guillermo; Zhai, Yikui; Bermúdez, Fátima; Kain, Jeffrey Perring

doi:10.2118/163809-ms

Cited by 27 publications

(3 citation statements)

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“…The digital oilfield pilot implementation discussed in this paper is extensively documented in the literature (Al-Abbasi et al 2013;Al-Jasmi et al 2013a, 2013b, 2013c, 2013d, where various technical aspects are discussed; however, this is one of the few papers related to that particular project that discuss the change management aspects and, in particular, the effect that change has on people, or more succinctly, the people side change. Al-Abbasi et al (2013) note that "the ultimate purpose of collaborative IDOF workflows is to provide a more effective working environment for asset teams, allowing better communication and analysis to solve operational and management problems."…”

Section: Paper Discussionmentioning

confidence: 99%

Intelligent Digital Oilfield Implementation: A Case Study of Change Management Strategies to Ensure Success

Al-Jasmi

Goel

Cerda

et al. 2013

Day 3 Wed, October 30, 2013

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The successful implementation of a world-leading integrated digital oilfield (IDOF) with a national oil company (NOC) required extremely careful and innovative management of the large-scale change required for the project. In particular, the "people side" of that change management was key for embedding the project in the NOC organization and, in turn, making the USD multi-million project a success.This paper outlines the NOC expectations and describes the steps and processes required to initiate and mobilize the people side of change in the IDOF project and also details the change management challenges experienced before the peopleside-focused strategy was determined to be the way forward. Using a simple people-side-focused change methodology not only ensured that the project was successful in terms of return on capital employed (ROCE) but also began the process of ownership and eased the adoption of the IDOF into the current NOC organization. The change management methodology described in this paper was designed to cover and mitigate important challenges that were relevant in driving the people side of change, particularly the following:• WIIFM-"what's in it for me" scenarios.• Culture-the way things are done around here.• Success-collaboration, technology leap frogging, people, and oil gains.This paper describes the main successes, key lessons learned, observations, and progress made against the clearly defined NOC expectations. VisionThe IDOF project was initially created as a visionary state-of-the-art prototype pilot for the NOC. From the outset of the project, expectations were at a very high level, both in terms of the technology that was envisioned to be used for oil production optimization and the caliber of people involved in the IDOF.This IDOF is seen as a technological game changer, one of the NOC's most strategically important projects in terms of optimizing oil production. In the IDOF collaborative work environment (CWE), a suite of advanced smartflow workflows provide smart functionalities, coupling geological and reservoir subsurface models with production operation workflows. The workflows monitor real-time data to help improve well performance, reduce well downtime, and increase field performance.The NOC executives and the leadership of the management in the research and technology (R&T) department were clear in their vision and aspirations-this project should lead not only the oil and gas Gulf Cooperation Council (GCC) countries but also in the field of IDOF concepts and technology worldwide. In addition, it should position the NOC as the preeminent world leader in IDOF technology. The internal design concepts for the new collaboration center (Fig. 1) were also fully maximized to match the NOC's expectations. This helped to deliver one of the most elaborate and comfortable workplaces within the oil and gas sector. The combination of a clear vision and high expectations whilst working in partnership with a major oil service organization immediately attracted the best technology talent in the fie...

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Section: Paper Discussionmentioning

confidence: 99%

Intelligent Digital Oilfield Implementation: A Case Study of Change Management Strategies to Ensure Success

Al-Jasmi

Goel

Cerda

et al. 2013

Day 3 Wed, October 30, 2013

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“…The recommendation is to reduce the choke setting to increase the pressure intake (flowing bottomhole pressure (BHP)). Another interesting example is discussed in Al-Jasmi et al (2013a, 2013b. Each condition is associated with a set of verification as well as correcting actions; the former attempts to confirm what the degree of truth suggests, and the latter presents mostly corrective actions to help prevent the persistence of the conditions after the equipment has been replaced and ensures that measures are taken to help prevent the recurrence of the potential failure mechanism.…”

Section: Fig 7-esp Wells Advanced Diagnosticsmentioning

confidence: 99%

“…Recently, Al-Jasmi et al (2013a, 2013b showed the essential components of an automated workflow model that provides monitoring, diagnostic, and optimization in real time. In these publications, the authors describe the general aspect of FzL applied for predicting undesirable effects on ESP production.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Application to Monitor and Predict Unexpected Behavior in Electric Submersible Pumps (Part of the KwIDF Project)

et al. 2014

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Implementing asset-wide intelligent digital oilfield (iDOF) solutions is more complex than simply "integrating technology." For iDOF to be truly successful and achieve expectations, concepts from different fields, such as artificial intelligence (AI) or soft computing techniques, must be used to help orchestrate the technology and required functionality to deliver the "intelligence" required to meet the challenges associated with optimizing oil production. Fuzzy logic (FzL) is a type of probabilistic logic that is approximate as opposed to being precise or exact. FzL can have a range of truth values or determine a likelihood of being true.The process of diagnosis for artificial lift systems has always relied solely on the engineer's criteria when analyzing data. It is not uncommon for two engineers with similar backgrounds to have, on the basis of the same data, differing opinions of a well problem. It is, however, uncommon that both engineers are either completely incorrect or correct in their judgment, which begs the question as to why this occurs. When traditional binary logic (true or false) is used for well analysis, the outcomes must be either this or that, simply because of the nature of binary logic. Unfortunately, most of the time, the results of well diagnoses, especially when attempting to use a predictive approach, cannot be properly represented with a yes-no approach, which means both engineers would be correct to a certain degree; in FzL, this is called the "degree of truth." FzL incorporates the rationale of having different degrees of truth into the world of well diagnosis and adds tremendous value by raising the issue of different conditions having certain degrees of truth of being in development.The FzL in this solution was built on a set of worldwide expert rules and converts these rules to their mathematical equivalents. Production operation's expert rules are used to formulate the conditional statements that comprise FzL (e.g., if the motor temperature exceeds a certain limit, the pump can burn out). This paper discusses how the AI tool determines trends by calculating a slope for a predetermined period of time for the different sensor signals and compares these sets of trends with pre-established sets of rules. The closeness of the measured trends to the models is expressed as "conditioners" that represent the likelihood of each trend occurring over a period of time.This technique has proven to be very useful in real-time monitoring for production engineers, helping them to perform fast predictions and pump diagnostics to predict unexpected problems, such as pump wear, gas interference, tubing leaks, viscous forces, and solids-plugged intakes. Several case histories are discussed in this paper. The ultimate goal of this smart flow is to deliver field-level actions that extend pump life and reduce pump downtime.

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Maximizing the Value of Real-Time Operations for Diagnostic and Optimization at the Right Time

et al. 2013

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Although oilfield workflow automation processes have been implemented in the oil and gas industry during the last decade, the full impact and value of these technologies has yet to be realized. Tremendous potential still exists to implement new technology to maximize production, increase operational efficiency, and reduce down time/non-productive time. The Kuwait Integrated Digital Field project (KwIDF) is a novel intelligent digital oilfield operation for Kuwait Oil Company, the operator of the Sabriyah field in North Kuwait. The project has successfully implemented improvements in field's operational efficiency by reducing the down time in artificial lift completion, increasing well productivity, and preventing water production. This paper provides an overview of the project from the perspective of field data acquisition, data analysis, model analysis, diagnostics, and operational optimization conducted in "right time" in the operator's state-of-the-art collaboration center. The automated workflow system orchestrates integrated solutions that not only provide monitoring in real time, but also enable diagnostics and optimization for individual wells. The automated workflow integration and orchestration conducted by the production team within a collaborative work environment covers the following aspects described in this paper: production surveillance (including alarming and alert events), well performance, production losses, artificial lift optimization, and key performance monitoring.

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ESP "Smart Flow" Integrates Quality and Control Data for Diagnostics and Optimization in Real Time

Cited by 27 publications

References 8 publications

Intelligent Digital Oilfield Implementation: A Case Study of Change Management Strategies to Ensure Success

Intelligent Digital Oilfield Implementation: A Case Study of Change Management Strategies to Ensure Success

Fuzzy Logic Application to Monitor and Predict Unexpected Behavior in Electric Submersible Pumps (Part of the KwIDF Project)

Maximizing the Value of Real-Time Operations for Diagnostic and Optimization at the Right Time

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