Optimizing Well Delivery in Real-Time from Spud to Completion through Intelligent Use of Applied Data Management and Standards

Regan, Matthew; Igland, Jan Kare; Andresen, Per-Arild

doi:10.2118/166686-ms

All Days 2013

DOI: 10.2118/166686-ms

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Optimizing Well Delivery in Real-Time from Spud to Completion through Intelligent Use of Applied Data Management and Standards

Matthew Regan¹,

Jan Kare Igland²,

Per-Arild Andresen³

Abstract: Today, even relatively basic drilling operations generate substantial amounts of real-time data, both at surface and downhole, all paid for by the Operator. This data can stagnate as a commodity, archived in a database or a drawer. Or it can be transformed into an asset enabling optimized well delivery through immediate, informed, collaborative decision-making. This transformation from data object to value-adding asset is made by intelligent use of real-time data management that drives ergonomic data display -… Show more

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Cited by 3 publications

References 6 publications

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Case Study: Tapping into Corporate Domain Expertise Using a Standard Realtime Data Solution

Regan¹,

Kucs

2015

Day 3 Thu, March 19, 2015

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This paper describes how a major European independent Operator, with a wide range of operating assets spread across the world, each with its own set of unique drilling and logistical challenges, has made the strategic decision to implement a realtime data management architecture supporting their entire global organisation by partnering with a vendor-neutral data management specialist to deliver a bespoke enterprise-wide solution. The Operator mandated that at all stages the solution needed to be a common framework fit for purpose from Norway to New Zealand, from pastoral fields to desert war zones. The paper will cover all aspects of the project through conceptualisation and functional needs assessment, technical qualification, infrastructure at rigsites and offices, data hosting and quality assurance, use of industry standards, change management and workflow design, benchmarking and key performance indicators (KPIs), and finally deployment and integration into daily operation. Historically, the Operator had been reliant on realtime decision-making in local offices being made almost autonomously, with secondary input from the Operator's head office referenced secondarily, occasionally, and often after the event. The absence of a standard realtime data exchange solution was recognised as an impediment to efficient, collaborative use of the substantial drilling domain expertise distributed throughout the organisation. Having invested in a corporate realtime drilling data infrastructure, the Operator now derives full value of their global knowledge base being made immediately available to any operation at any time, irrespective of location. Truly collaborative, timely, informed operational decision-making now drives performance improvement, risk and cost reduction, and optimised well delivery.

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Case Study: Tapping into Corporate Domain Expertise Using a Standard Realtime Data Solution

Regan¹,

Kucs

2015

Day 3 Thu, March 19, 2015

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Statistical Method for Error Prediction in Decision Support and Control Systems

Aghito

2017

Day 1 Tue, March 14, 2017

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A statistical method for improving the accuracy of predictive mathematical models used in drilling operations is proposed. The scheme can be applied in drilling automation, for example for improving accuracy in automated control systems such as managed pressure drilling (MPD), and for more reliable real-time monitoring and decision support systems. The proposed method is applied to a simulated case of MPD. Hydraulic model predictions of BHP are continuously compared to PWD readings. A short sequence with varying pump rate is run to train the statistics, which are collected at each pump rate, and refined considering also the pump rate derivative. This is visualized as a 3D surface giving a clear illustration of the error signature of the simulated case. The estimated error is added to the set point of the choke control system, demonstrating a clear improvement of BHP control. We proposed a new statistical method that uses real-time sensor data to perform automatic fingerprinting of the predictive performance of a given model. The principle is simple but powerful and can be applied to many cases. It can be combined with smart visualization tools in decision support systems, helping to build confidence in the users, or even utilized to adjust the initial prediction for improved accuracy in control systems. The tool is independent of the internal calculations in the mathematical model, and it can therefore be utilized in combination with any model.

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Importance of Real-Time Data to Improve Operational Efficiency for Unconventional Drilling in Argentina

Fernandes¹,

Natera²,

Barros³

et al. 2023

Day 2 Tue, March 21, 2023

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YPF is the most active drilling operator in Argentina, and in the last few years, its drilling and workover activities have been growing significantly. In 2018, as part of the digitalisation journey of the company, a Monitoring and Control Centre was established with a long-term vision to standardise and change the way unconventional wells are drilled in Argentina. This paper describes an initiative to build infrastructure that manages real-time data across multiple unconventional assets, by using a real-time monitoring platform which follows the WITSML 1.4.1.1 industry standard. This project was divided into three strategic stages: implementation of a tool to acquire real-time data through WITSML, implementation of the tool and new workflows to improve data quality, by working closer with data providers, and improvements in drilling performance with the help of real-time data. Implementing the above strategies improved operational efficiencies and provided lessons learnt for each stage of implementation.

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Optimizing Well Delivery in Real-Time from Spud to Completion through Intelligent Use of Applied Data Management and Standards

Cited by 3 publications

References 6 publications

Case Study: Tapping into Corporate Domain Expertise Using a Standard Realtime Data Solution

Case Study: Tapping into Corporate Domain Expertise Using a Standard Realtime Data Solution

Statistical Method for Error Prediction in Decision Support and Control Systems

Importance of Real-Time Data to Improve Operational Efficiency for Unconventional Drilling in Argentina

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