Smart Custom Well Design Based On Automated Offset Well Analysis
- Anand Selveindran (University of Houston) | Avinash Wesley (Halliburton) | Nitish Chaudhari (Halliburton) | Helmut Pirela (Halliburton)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 26-29 October, Virtual
- Publication Date
- Document Type
- Conference Paper
- 2020. Society of Petroleum Engineers
- 7.6.7 Neural Networks, 6.1.5 Human Resources, Competence and Training, 6 Health, Safety, Security, Environment and Social Responsibility, 0.2 Wellbore Design, 2.7.1 Completion Fluids, 2 Well completion, 1.6 Drilling Operations, 7 Management and Information, 7.2.1 Risk, Uncertainty and Risk Assessment, 7.6 Information Management and Systems, 1.14 Casing and Cementing, 7.2 Risk Management and Decision-Making, 1.14.1 Casing Design, 6.1 HSSE & Social Responsibility Management, 2.7 Completion Fluids
- recommendation, offset well analysis, well planning, well design, machine learning
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- 89 since 2007
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Offset well analysis is the process of investigating and integrating historical drilling performance from neighboring wells into prospect well design. Traditional offset analysis is a time and resource intensive process that requires a lot of manual input and analysis to make various design decisions. Our proposed workflow automates much of this analysis, while allowing the user to customize designs based on operating metrics resulting in a quicker and more comprehensive offset well analysis that provides the user with intelligent offset recommendations and a base design for the prospect well. Historical data is queried from a structured engineering and operation database comprising of data ranging from subsurface geology, drilling to production and end-of-well. We gather this historical data from nearby wells and learn from those in order to produce a first pass design for the prospect well to begin with. With this data, our proposed workflow implements algorithms to identify representative offset wells with similar geology, trajectory and other characteristics. A similarity analysis based on various geological sequences across the wells is conducted by a deep neural network algorithm, which is trained to analyze sequential patterns within. Trajectory similarity is performed using well surveys (dogleg severity, inclination, and azimuth). A recurrent neural network is employed to learn the well survey patterns and classify wells with similar trajectories. Finally, drilling performance metrics are used to rank the offsets and aid selection of the best offset design to be used as a base template for the prospect well. The proposed workflow significantly reduces analysis time; the software analysis time is less than five minutes. The user can almost instantaneously query the database and obtain wells that are similar in terms of trajectory and geology, and rank those wells on certain pre-defined metrics. The engineer is also able to view key events and hazards history for the offset wells. This provides the engineer with a compilation of hazards and risks by depth and cause, allowing the engineer to focus on mitigating risks and designing better and safer wells. The engineer can accept the top ranked offset and automatically select casing design based on default metrics of cost, time, and NPT, or can implement other metrics and create a composite casing design from different hole-sections from different offsets.
|File Size||2 MB||Number of Pages||12|
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