Engineering Workflows Provide Integrated Operations and Streamline Real-Time Production Optimization
Abstract: This paper explores the results of one operator's successful implementation of a workflow-based solution for real-time Production Surveillance and Optimization of a large on-shore field in Mexico. We will also examine the fundamental aspects of large-scale workflow delivery in a real-world environment – in this case, involving over 200 wells producing over 200,000 STBD, 50+ users, multiple and often conflicting sources of data (ex. Field vs. SCADA), and integration of production modeling applications from mult… Show more
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Cited by 3 publications
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As defined by Andrew Grove, strategic inflection points (SIPs) represent what happens to a business when a major change occurs in its competitive environment (Grove 1999) or when major market changes significantly impact business fundamentals. Oil and gas case studies have documented new ways of working and a more than tenfold improvement to individual productivity, demonstrating that new, more effective ways of operating oil and gas assets are both possible and practical. This evidence suggests that oil and gas production has experienced an SIP. Throughout the last few years globally, nearly 400 workflows representing more than 50 digital oilfield automation projects have been implemented successfully. Many have focused on improving efficiency and all have gone far beyond SCADA implementation. These projects encompass nearly every asset type, demonstrating that intelligent or digital energy (I/DE) solutions are not restricted to a single operating environment. Many papers have been published on this topic at SPE conferences. Examples cited by Barbarino 2011, Dutra 2010, Moisés 2008, Sankaran 2011, Al-Jasmi 2013, Vignati 2013 and Van den Berg 2010 provide descriptions of representative projects. All of these projects experienced significant engineering capacity gains. This implies that when companies respond to the SIP, engineers and operators no longer must devote the majority of their time to routine, low-value tasks. Instead, they can concentrate on activities that produce meaningful performance improvements and business value. In other words, the resulting expansion of effective engineering capacity, the maximum amount of high value work that an engineer is capable of completing in a given period, can change the way oil and gas companies operate (Holland and Crompton 2014). Although specific work processes are prioritized differently amongst various companies and assets, the underlying I/DE principles of operational excellence, efficiency and automation on which solutions are based remain constant. The application of these principles is what makes business transformation possible (Lochmann 2012). Presently, significantly higher levels of production performance are achievable within the oil and gas industry. Companies that choose to fully implement I/DE principles are likely to follow the steep upward performance trend predicted in the SIP model, where employees focus on such business imperatives as increasing production, reducing cost, and managing risk, rather than routine, mandatory, low-value assignments. The consequences of a strategic inflection point within production are similar to those encountered in exploration 25 to 30 years ago when the introduction of information technology made the routine application of 3D seismic interpretation a reality and brought profound business changes to the upstream geoscience sector.
As defined by Andrew Grove, strategic inflection points (SIPs) represent what happens to a business when a major change occurs in its competitive environment (Grove 1999) or when major market changes significantly impact business fundamentals. Oil and gas case studies have documented new ways of working and a more than tenfold improvement to individual productivity, demonstrating that new, more effective ways of operating oil and gas assets are both possible and practical. This evidence suggests that oil and gas production has experienced an SIP. Throughout the last few years globally, nearly 400 workflows representing more than 50 digital oilfield automation projects have been implemented successfully. Many have focused on improving efficiency and all have gone far beyond SCADA implementation. These projects encompass nearly every asset type, demonstrating that intelligent or digital energy (I/DE) solutions are not restricted to a single operating environment. Many papers have been published on this topic at SPE conferences. Examples cited by Barbarino 2011, Dutra 2010, Moisés 2008, Sankaran 2011, Al-Jasmi 2013, Vignati 2013 and Van den Berg 2010 provide descriptions of representative projects. All of these projects experienced significant engineering capacity gains. This implies that when companies respond to the SIP, engineers and operators no longer must devote the majority of their time to routine, low-value tasks. Instead, they can concentrate on activities that produce meaningful performance improvements and business value. In other words, the resulting expansion of effective engineering capacity, the maximum amount of high value work that an engineer is capable of completing in a given period, can change the way oil and gas companies operate (Holland and Crompton 2014). Although specific work processes are prioritized differently amongst various companies and assets, the underlying I/DE principles of operational excellence, efficiency and automation on which solutions are based remain constant. The application of these principles is what makes business transformation possible (Lochmann 2012). Presently, significantly higher levels of production performance are achievable within the oil and gas industry. Companies that choose to fully implement I/DE principles are likely to follow the steep upward performance trend predicted in the SIP model, where employees focus on such business imperatives as increasing production, reducing cost, and managing risk, rather than routine, mandatory, low-value assignments. The consequences of a strategic inflection point within production are similar to those encountered in exploration 25 to 30 years ago when the introduction of information technology made the routine application of 3D seismic interpretation a reality and brought profound business changes to the upstream geoscience sector.
Summary As defined by Andrew Grove (one of the founders and the chief executive officer of Intel Corporation, the world's largest manufacturer of semiconductors), strategic inflection points (SIPs) represent what happens to a business when a major change occurs in its competitive environment (Grove 1999) or when major market changes significantly affect business fundamentals. Oil-and-gas case studies have documented new ways of working and a more-than ten-fold improvement to individual productivity, demonstrating that new, more-effective ways of operating oil-and-gas assets are possible and practical. This evidence suggests that oil-and-gas production has experienced an SIP in recent years, driven by individual productivity and operational efficiency. Throughout the last few years globally, approximately 400 work flows representing more than 50 digital oilfield automation projects have been implemented successfully. Many have focused on improving efficiency, and all have gone far beyond basic SCADA implementation. These projects encompass nearly every asset type, demonstrating that intelligent or digital energy (I/DE) solutions are not restricted to a single operating environment. Many papers have been published on this topic at SPE conferences. Examples cited by Moisés et al. (2008), Dutra et al. (2010), Van den Berg et al. (2010), Barbarino et al. (2011), Sankaran et al. (2011), Al-Jasmi et al. (2013), and Vignati et al. (2013) provide descriptions of representative projects. All these projects experienced significant engineering-capacity gains. This implies that when companies respond to the SIP, engineers and operators no longer must devote the majority of their time to routine, low-value tasks. Instead, they can concentrate on activities that produce meaningful performance improvements and business value. In other words, the resulting expansion of effective engineering capacity and the maximum amount of high-value work that an engineer can complete in a given period can change the way oil-and-gas companies operate (Holland and Crompton 2014). Although specific work processes are prioritized differently among various companies and assets, the underlying I/DE principles of operational excellence, efficiency, and automation on which solutions are based remain constant. The application of these principles is what makes business transformation possible (Lochmann 2012). Presently, significantly higher levels of production performance are achievable within the oil-and-gas industry. Companies that choose to fully implement I/DE principles are likely to follow the steep upward performance trend predicted in the SIP model, whereas employees focus on such business imperatives as increasing production, reducing cost, and managing risk, rather than routine, mandatory, low-value assignments. Although I/DE concepts are not routinely used within the industry, there is little doubt that such technology needs to advance and support fundamental shifts in production operations and operational excellence to assist producers, especially through low-price periods. The recent price decline clearly puts additional pressure on oil companies to operate more efficiently and at lower costs. As this paper is being prepared in 2015, oil price has been reduced by more than USD 100 per barrel since its 2008 high. Many observers might consider this an SIP. However, as historical oil-price data illustrate, there have been approximately 10 sharp oil-price changes since 1973, none of which has resulted in long-term changes to business structures. With the exception of the market impact of the Organization of Oil Exporting Countries (OPEC) in 1973–1974 (clearly, an SIP), subsequent price volatility has caused dramatic expansions and contractions, but the industry seems to consistently fall back into its established ways of doing business. The challenge for oil-and-gas management is to recognize whether a situation such as the drop in oil price is an SIP or a normal variation (although severe) that routinely affects an exploration-and-production (E&P) business and to act accordingly. The consequences of an SIP within production are similar to those encountered in exploration 25 to 30 years ago when the introduction of information technology made the routine application of 3D seismic interpretation a reality and brought profound business changes to the upstream geoscience sector.
Automated Real Time Anomaly Detection Model for Operation and Production Data at Scale
Oil and gas industry have evolved towards digitalization and data are fully utilized for decision making, cost optimization, improve in efficiency, and increase productivity. Upstream sector in oil & gas produce a huge number of operation and production data in a real-time platform. It is tedious process that somehow impractical and inefficient to quality check and analyze all available data manually (Subrahmanya et al., 2014). By using machine learning algorithm, this can be improved to automate data quality check at scale. On top of that, imputation can also be implemented to substitute on missing data and future forecast in real-time. In a case of this study, a huge data was collected from more than 30,000 tags/sensors in real-time. The real-time data were collected up to seconds and quality check need to be done up to each data collected. Firstly, each equipment tags/sensors had been checked and arranged with P&ID drawing. Then, API was developed with the real-time platform. In this project, percentile of machine learning was applied and developed to quality checked the operation and production time-series data at scale. Lastly, the process was customized to other offshore platforms in the field. In addition to automated data quality checking, machine learning algorithms were also used to calculate missing information based on the underlying relationship between data points. These approaches would reduce time needed to maintain quality and reliable data for further analysis and usage. As a result, percentile in machine learning successfully automate the process of data quality check for more productivity and efficiency. The percentile was applied to understand, validate, and monitor data at scale. Anomalies were detected in real time that allows operators to analyze further on any possibility in faulty, damage, or loss. All the outliers, missing or wrong data were also recorded and visualized in a dashboard. The model also provides additional statistic to define stale and bad data on top of automated define parameters. These features have improved efficiency of data acquisition and preparation. As conclusion, the model assists operator in monitoring daily operation and production data efficiently. Data quality and reliability is the key factor in asset management to ensure operator trust on produced data. The quality checked data could be utilized for further analysis, troubleshooting, and decision making.
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