Decision Support and Workflow Automation for the Development and Management of Hydrocarbon Assets Using Multi-Agent Systems

El-Bakry, Amr; Römer, Michael; Xu, Peng; Sundaram, Anantha; Usadi, Adam K.; Morehead, Hubert Lane; Crawford, Mark; Holloway, Bryce; Knight, Charles

doi:10.2118/150285-ms

All Days 2012

DOI: 10.2118/150285-ms

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Decision Support and Workflow Automation for the Development and Management of Hydrocarbon Assets Using Multi-Agent Systems

Amr El-Bakry

Michael Römer

Peng Xu

et al.

Abstract: Asset management teams face many challenges with increasing asset complexity, increasing data volumes, and staff in high demand while optimal asset performance remains paramount. More effective development and management of hydrocarbon assets may be achieved through an increased level of automation of work processes and decision-support technologies across the upstream value chain. Asset management workflow automation poses special challenges as such workflows are multi-disciplinary, cross-functional, and huma… Show more

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Cited by 3 publications

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“…ExxonMobil has a long history of applying a disciplined and value-driven approach to the deployment of advanced technologies in asset management. In order to effectively implement and sustain these advancements, we are concurrently working on addressing both the technical challenges mentioned above as well as organizational and change management concerns, and this has been reported in a number of prior publications (Shyeh et al, 2008;Killian et al, 2012;El-Bakry et al, 2012).…”

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Advanced Machine Learning Methods for Production Data Pattern Recognition

Subrahmanya

El-Bakry

et al. 2014

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Exxon Mobil Corporation has numerous subsidiaries, many with names that include ExxonMobil, Exxon, Esso and Mobil. For convenience and simplicity in this paper, the parent company and its subsidiaries may be referenced separately or collectively as "ExxonMobil." Abbreviated references describing global or regional operational organizations and global or regional business lines are also sometimes used for convenience and simplicity. Nothing in this paper is intended to override the corporate separateness of these separate legal entities. Working relationships discussed in this paper do not necessarily represent a reporting connection, but may reflect a functional guidance, stewardship, or service relationship. AbstractAn important challenge for asset management is to analyze large amounts of data in a short period of time to provide insightful information for decision making in a timely fashion. Analyzing all available data manually is impractical and inefficient. It is advantageous to develop pattern recognition algorithms to recognize events-of-interest to achieve effective asset management. SPE 167839building a statistical model to recognize the validity of rate measurement tests in a test separator. In this case, through their daily activities, the operators have labeled most of these tests as valid or invalid. The extensive amount of well test validation data provides sufficient information to assess the newer approaches under review. The plan then is to apply a similar approach to tasks such as equipment health monitoring to identify pump failures with limited expert input.Conceptually, reduction in labeled input can be achieved by combining the information from the labels and the statistical distribution of the data (e.g., clusters). As an extreme example, consider that the pump measurement data may show two distinct clusters and the operators have labeled a few data points in one cluster as pump failures when reports had to be made due to wells being shut in. This information is sufficient to label one of the clusters as healthy and the other one as faulty. For a new measurement, a prediction may be made by first determining the cluster to which the measurement belongs and then assigning it the corresponding label. While most real world problems are much more challenging than this example due to the number of data points, dimensionality of the data, lack of clear cluster structure and potential ambiguity of data structures, similar ideas can be used to develop highly accurate statistical models with a limited number of labels.

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Advanced Machine Learning Methods for Production Data Pattern Recognition

Subrahmanya

El-Bakry

et al. 2014

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Big Data in E&P: Real-Time Adaptive Analytics and Data-Flow Architecture

Brule

2013

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Big Data Analytics are most effective when data-in-motion are combined with data-at-rest. Stream computing is a new way of analyzing high-frequency data for real-time complex-event-processing (CEP) and for scoring data against a physics-based or empirical model for predictive analytics, without having to store the data. Hadoop Map/Reduce and other NoSQL approaches are a new way of analyzing massive volumes of data whether semistructured or unstructured, which can be used to support the E&P industry's many physics-based methods in modeling and simulation, over a wide range of disciplines from geology & geophysics to reservoir, production, & facilities engineering. High-volume data of many different types can be landed in Hadoop and analyzed without extensive transformation into a relational database model. Combining Stream computing with Hadoop Map/Reduce or massively parallel processing relational data warehousing (MPP DW) enables the analysis of high-frequency data scored against continuously updated models, known as "Real-Time Adaptive Analytics." This design pattern provides a low-latency "Real-Time Data-Flow Architecture," which enables decisions during operational events and also at the speed of the overall business. Big Data Analytics also depends on tooling for building modeling and simulation applications and for performing analytics, as much as it depends on infrastructure. Augmenting the industry's many decades of progress in physics-based modeling and simulation, powerful empirical models can be developed with tools for statistical analytics, text analytics, AI, and machine-learning and then used as scoring and data-validation models by implementing stream computing on-site where the data are generated in oilfield operations. This paper will explore Real-Time Adaptive Analytics and the Real-Time Data-Flow Architecture combining stream computing, Hadoop/NoSQL, and MPP data warehousing. Several compelling use cases in drilling and in production will be reviewed.

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Robust Derivative Estimation for Decline Analysis from Noisy Production Data

et al. 2016

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Efficient reservoir management requires regular analysis of large amounts of data to provide insights for decision making in a timely fashion. Well testing provides a key source of data for production surveillance and optimization by estimating oil, water and gas rates at irregularly sampled testing times. Although a significant amount of effort is spent on ensuring the quality of these well tests, the acquired data is still likely to have large uncertainties due to the complexity of flow dynamics, and challenges in multiphase flow separation and measurement. The resulting production history measurement for a well is therefore a noisy, unevenly sampled time series with potential significant outliers. A key point of interest is to perform decline analysis, which involves monitoring production trends (the derivative of the production time series) to ensure optimal well and reservoir performance. In addition to noise, the estimation of production trends is complicated by the effect of improved/enhanced recovery mechanisms (such as water flooding, steam injection etc.), well stimulation, and communication between wells, all of which may cause the production to deviate from expected parametric decline curves. As a result of this complexity, decline analysis typically requires significant manual effort in cleaning and segmenting the data and then fitting parametric curves to the extracted segments; this is challenging to do on a regular basis for all the wells. A robust and automated method to estimate production trends will enable continuous production surveillance and optimization in fields with hundreds to thousands of wells. In this paper, we report an effective method to address this challenge using a non-parametric approach based on robust regression for joint time series modeling and derivative estimation. Some key advantages of this approach over a conventional approach are a) it does not require manual data segmentation b) it is tolerant to a high amount of noise including some bad outliers c) it does not require manual choice of parametric decline curves. We compare results with conventional approaches and demonstrate benefits on synthetic production data.

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Decision Support and Workflow Automation for the Development and Management of Hydrocarbon Assets Using Multi-Agent Systems

Cited by 3 publications

References 9 publications

Advanced Machine Learning Methods for Production Data Pattern Recognition

Advanced Machine Learning Methods for Production Data Pattern Recognition

Big Data in E&P: Real-Time Adaptive Analytics and Data-Flow Architecture

Robust Derivative Estimation for Decline Analysis from Noisy Production Data

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