Maximizing the Value of Real-Time Operations for Diagnostic and Optimization at the Right Time (KwIDF Project)

Al-Jasmi, Ahmad; Goel, H. K.; Al-Abbasi, Adel; Nasr, Hatem N.; Velasquez, Guillermo; Carvajal, Gustavo; Cullick, A.S.; Rodríguez, José Antonio Alonso; Scott, Mike

doi:10.2118/167397-ms

Cited by 11 publications

(1 citation statement)

References 14 publications

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“…Appendix 1 shows the results of automation on resource allocation on electric submersible pump workflows in the Middle East (Al Jasmi et al 2013). Situations where highly skilled (and scarce) engineers and operators spend the majority of their time on routine operational tasks such as gathering, moving and combining data for analysis, take time and focus away from higher value projects and increase the level of operational risk.…”

Section: Automationmentioning

confidence: 99%

Improving Situational Awareness To Prevent Conflicts in Simultaneous Offshore Operations

Lochmann¹,

Darling²,

Pemberton³

et al. 2015

Day 2 Wed, September 16, 2015

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Every year, conflicting activities in congested offshore producing fields cost operators millions of dollars in lost time-both from operational inefficiencies and incidents caused by too many parties trying to work in the same area at the same time. Examples of these activities include, but are not limited to: divers and remotely operated vehicles (ROVs) performing routine maintenance and repairs, workovers, fishing and supply vessels coming and going unpredictably, anchor-handling operations, seismic surveys, and oil tankers arriving without sufficient warning at a floating production-storage and offloading (FPSO) vessel.In congested areas around active drilling rigs and producing platforms, failures of a person's understanding of a situation compared to actual conditions (known as situational awareness (SA)), and conflicts among simultaneous operations (SIMOPS) are, sadly, fairly common. In many cases, SA/SIMOPS incidents are due to human error. Because people are still in control of oil and gas operations, human error and operational risk can never be entirely eliminated, but SA/SIMOPS management systems can minimize reduction or loss of SA, which is responsible for as much as 88% of human error. This technology helps to manage risk and keep it at an acceptable level while significantly improving operational efficiencies. As a result, operators can benefit from higher margins of safety and improved performance while no longer having to accept the current level of SIMOPS-related inefficiencies or incidents as simply the cost of doing business.Traditional manual processes, whiteboards, email, Gantt charts, and spreadsheet programs are no longer sufficient to prevent or control costly SIMOPS conflicts in increasingly chaotic offshore fields. To optimize the execution of critical activities, an SA/SIMOPS management system must provide all relevant parties with timely, near real-time updates during rapidly changing operations.SA/SIMOPS management introduces new principles, work processes, information technology tools, and changes in behavior to accomplish its safety and efficiency goals. Initiatives with similar characteristics can be found in the digital oilfield (DOF) pilots now being implemented around the world. By applying what has been learned in these programs, the installation and use of SA/SIMOPS solutions can be accelerated. This paper presents an innovative approach that directly addresses these challenges.

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Section: Automationmentioning

confidence: 99%

Improving Situational Awareness To Prevent Conflicts in Simultaneous Offshore Operations

Lochmann¹,

Darling²,

Pemberton³

et al. 2015

Day 2 Wed, September 16, 2015

View full text Add to dashboard Cite

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Building Neural-Network-Based Models Using Nodal and Time-Series Analysis for Short-Term Production Forecasting

Rebeschini

Querales

Carvajal

et al. 2013

Day 2 Tue, October 29, 2013

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Implementing asset-wide intelligent digital oilfield (iDOF) solutions, aiming to optimize oil and gas production system in an "intelligent" manner, requires integrating concepts from different disciplines, such as artificial intelligence. Neural network- (NN-) based models are a form of artificial intelligence, which is a branch of computer science that generates mathematical models that can be "trained" to determine relationships between inputs and outputs, recognize patterns, and perform reliable short-term predictions. NN models using real-time data are proven tools for short-term well production forecasting with acceptable accuracy. An oilfield is a hostile environment for even the most robust instrumentation. As a result, technical outages or anomalies can result in lost or poor quality data. Experience shows that many samples in a real-time database are frozen, missing, corrupted, or incorrect. This fact represents the biggest challenge to creating a reliable NN model. However, the models can be trained to correct or estimate missing real-time data. This paper presents a case study where nodal analysis was used to populate missing data used to train NN model, thus improving the reliability of the model. Because nodal analysis is not suitable for prediction, time-series analysis was used to assess the impact of historical events, and operational conditions were used to forecast trends. The NN trained with nodal analysis can cover a wide variability spectrum and, when trained with a time-lapse series, can predict short-term (30-day) production scenarios by changing highly correlated parameters, such as tubing head pressure (THP) or frequency (Freq). This paper describes training NNs using nodal analysis and time-series analysis to predict short-term water cut (WC or BS&W) and liquid flow rate. This technique was applied in over 20 wells with electronic submersible pumps (ESPs) and gas lifts (GLs). The NNs made robust estimates of production rate and an acceptable prediction trend for 30 days, even when confronted with flow meter instrumentation failure, lost signals, and out-of-calibration instruments. Hence, the NN served as a "virtual meter," providing instantaneous and accurate estimation of production data.

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ESP "Smart Flow" Integrates Quality and Control Data for Diagnostics and Optimization in Real Time (Part of KwIDF Project)

Al-Jasmi

Nasr

Goel

et al. 2013

Day 2 Tue, October 29, 2013

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Intelligent digital oilfield (iDOF) operations include the transfer, monitoring, visualization, analysis, and interpretation of real-time data. Enabling this process requires a significant investment to upgrade surface, subsurface, and well instrumentation and also the installation of a sophisticated infrastructure for data transmission and visualization. Once upgraded, the system then has the capability to transfer massive quantities of data, converting it into real information at the right time. The transformation of raw data into information is achieved through intelligent, automated work processes, which are referred to here as "smart flows," which assist engineers in their daily well surveillance activities, helping make them more productive and improve decision making. A major oil and gas operator in the Middle East has invested in such an infrastructure and is developing a set of smart flows for key activities and work flows for its production operations, with the ultimate goal of improved asset performance. The project includes a smart flow to monitor, diagnose, and optimize wells that include electric submersible pumps (ESP); 50% of the wells in this field use ESPs. The ESP smart flow includes leading-edge technologies, such as: variable speed drive controller, subsurface equipment and sensors, advanced diagnostics based on artificial-intelligence agents, analysis of sensors signals, and automatically identifying ESP optimum operating conditions. Using a steady-state nodal-analysis model combined with an artificial intelligent technique, the ESP smart flow is designed to provide rapid diagnostics and optimization in real time, generating actions, such as decreasing and increasing the pump frequency and choke setting. The ultimate benefit is to detect the signals that foresee unexpected well downtime and predict ESP system pump failures. The paper describes the main functionalities of the ESP smart flow as a powerful optimization tool that is capable of providing an interactive monitoring system that can assist operations personnel in managing ESP-operated wells.

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Maximizing the Value of Real-Time Operations for Diagnostic and Optimization at the Right Time (KwIDF Project)

Cited by 11 publications

References 14 publications

Improving Situational Awareness To Prevent Conflicts in Simultaneous Offshore Operations

Improving Situational Awareness To Prevent Conflicts in Simultaneous Offshore Operations

Building Neural-Network-Based Models Using Nodal and Time-Series Analysis for Short-Term Production Forecasting

ESP "Smart Flow" Integrates Quality and Control Data for Diagnostics and Optimization in Real Time (Part of KwIDF Project)

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