Establishing a Digital Oil Field Data Architecture Suitable for Current and Foreseeable Business Requirements

The paper discusses some reported shortcomings of state-of-the-start IT technologies currently employed in the data management of Oil & Gas production operations. Most current IT infrastructures connect historian databases, production databases and application servers. This creates complex issues of data consistency between these systems. In the discussion, a particular focus is put on the geographically-distributed nature of the network which suffers from low-bandwidth limitations and un-reliabilities, e.g. due to satellite communication links. Taking the production engineers' viewpoint, an example of production allocation using Data Validation and Reconciliation (DVR) serves to stress the malicious impacts of the described architecture. Production allocation represents one of the various monitoring and analysis tasks that are performed, on a daily basis, at the centralized level of data management systems. A quantitative study shows that the problem of mis-synchronization of databases is of great practical importance. We propose solutions to improve the robustness to communication outages. To improve data consistency across sites in a decentralized manner, the paper exposes the key concepts of distributed storage, message-based communication, and clustering. More generally, the paper proposes to shine a light on the potential relevance of several recent advances in the scientific field of "big-data" to the world of Oil & Gas upstream industry. These off-the-shelf technologies must be specifically tailored to geographically-distributed networks. The specificities are detailed, the necessary development work is outlined, and the potential qualitative benefits are estimated. A possible implementation is sketched.

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“…A more exhaustive list of desirable properties of such a system can be found in Cramer et al (2012).…”

Section: A Proposition: Geographically-distributed Architecture and Rmentioning

confidence: 99%

Geographically-Distributed Databases: A Big Data Technology for Production Analysis in the Oil & Gas Industry

Preveral¹,

Trihoreau²,

Petit

2014

All Days

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“…There are two major drives behind this trend. One drive is the increased level of human expertise needed to handle the increasingly complex problems of deeper and more-difficult wells, calling for innovative and reliable support tools (Cramer et al 2012). The other drive is the "expert crisis," that is, the drain of competence as a result of the retirement of people with years-long and valuable experience, calling for methods to capture and continue to use their experience (Irgens 2008).…”

Section: Background and Related Researchmentioning

confidence: 99%

Detection of Symptoms for Revealing Causes Leading to Drilling Failures

Skalle

Aamodt

Gundersen³

2013

SPE Drilling &Amp; Completion

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When a diagnosis of a problem is known, the problem can usually be solved efficiently. This paper presents a method that helps reveal the most probable cause of a drilling-process failure immediately after occurrence. Normally, it takes some time to investigate and evaluate all available information before the correct cause can be determined. The method presented is targeted at reducing this time and at the same time improving the quality of the interpretation. The method relies on input parameters from the ongoing drilling process, parameters that behave irregularly and are referred to as symptoms or errors when the irregularity is severe. Recognized symptoms are used as input parameters in a knowledge-modeling method that relates symptoms to failures. The method was verified on the basis of historical drilling data, and its ability to point out the correct causes with high probability was demonstrated. Field testing the method is yet to be performed.

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Upstream/Downstream Real Time Surveillance and Optimization Systems: Two Sides of the Same Coin, or Never the Twain Shall Meet

et al. 2012

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Downstream Refineries and Chemical Plants have benefited from real time optimization systems (RTO) for the last 30 years. Downstream RTO is a well established and permanent fixture in many plants – the "way we do things ‘round here!". Upstream E&P operations have "come to this party" much more recently and are using RTO more sparingly, even though the economic and HSSE benefits can be very significant. There are key differences between downstream and upstream. For example, downstream facilities do not deal with sub-surface uncertainties, multiphase flow and isolated/harsh environments; while upstream operations do not usually have to deal with complex chemical processes. Integrated Oil Companies run upstream and downstream operations and integration of tools/practices across both regimes is often perceived to be of significant value. Hence, the purpose of this paper is to compare and contrast downstream and upstream RTO learnings with a view to identifying and describing: similarities in production unit operations e.g. fluid separation, compression etc.; key differences between production unit operations; cultural differences between operations; RTO activities from a technical perspective; RTO business benefits and how these might be leveraged and sustained in both directions. What will emerge from this analysis will be a comparison, highlighting points of commonality and differences, leading to a better understanding of how RTO can be more effectively exploited in the upstream business – the cheapest oil available! Specifically, it is concluded that RTO in upstream operations is feasible and lucrative, but is relatively rare with sustainability a challenge. Downstream RTO is more common and sustainable, significantly less lucrative, but a "must do" to compete in a highly competitive, margin constrained business.

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Establishing a Digital Oil Field Data Architecture Suitable for Current and Foreseeable Business Requirements

Cited by 4 publications

References 3 publications

Geographically-Distributed Databases: A Big Data Technology for Production Analysis in the Oil & Gas Industry

Geographically-Distributed Databases: A Big Data Technology for Production Analysis in the Oil & Gas Industry

Detection of Symptoms for Revealing Causes Leading to Drilling Failures

Upstream/Downstream Real Time Surveillance and Optimization Systems: Two Sides of the Same Coin, or Never the Twain Shall Meet

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