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How Not to Squander Billions on Your Next Unconventional Venture

Authors
Creties Jenkins (Rose and Associates, LLP) | Mark McLane (Rose and Associates, LLP)
DOI
https://doi.org/10.15530/AP-URTEC-2019-198318
Document ID
URTEC-198318-MS
Publisher
Unconventional Resources Technology Conference
Source
SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, 18-19 November, Brisbane, Australia
Publication Date
2019
Document Type
Conference Paper
Language
English
ISBN
978-1-61399-673-7
Copyright
2019, Unconventional Resources Technology Conference (URTeC)
Keywords
aggregation, stages, probabilistic, risk, uncertainty
Downloads
58 in the last 30 days
60 since 2007
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SPE Member Price: USD 9.50
SPE Non-Member Price: USD 28.00
Abstract

Over the past decade, hundreds of unconventional oil and gas projects have failed to deliver the value promised to shareholders. While most of the companies involved in these failures would like investors to believe they were unforeseen and caused by external factors, a great many occurred because of the way opportunities have been appraised and developed. Two of the most common mistakes have been focusing on production attainment instead of value creation, and incorrectly thinking that enough was understood about a given reservoir to push ahead with development.

To mitigate these errors, unconventional opportunities need to be evaluated in a series of stages. In each stage, there is a need to (1) identify the key uncertainties and risks, (2) collect the data needed to quantify these, and (3) generate a probabilistic assessment of potential outcomes and their associated value. A key aspect in this evaluation is not only using rock and fluid data to identify the area with the greatest potential, but also drilling enough wells to understand the production variance and whether the average well will be commercial. This includes quantifying the range of average well performance and the likelihood of achieving some minimum rate.

Multiple tools are needed for this work including aggregation (i.e. trumpet) plots, confidence curves, and sequential aggregation plots, which can all be derived from Monte Carlo simulation modeling. Key outputs from such models include a project expected value and a chance of success associated with each project stage. Most importantly, the model can be modified to help ensure that if the project fails, it does so in the early stages so that capital losses are minimized.

This process requires discipline, including maintaining consistent drilling and completion practices so variations in reservoir quality can be understood in the early stages of the project. To help ensure a disciplined approach is followed, it is important to implement an assurance process consisting of 1) clear guidelines and workflows, 2) peer reviews and assists, and 3) periodic performance lookbacks. The time to do this is now, so companies can live within their cash flow and return more value to investors.

File Size  1 MBNumber of Pages   19

Olson, B., Elliott, R. and Matthews, M. 2019. Fracking's Secret Problem-Oil Wells Aren't Producing as Much as Forecast. Wall Street Journal, January 2, 2019.

Dodge, E. 2014. NorthAmericanEnergyPolicyIntegration, http://www.edwardtdodge.com/2014/10/02/north-american-energy-policy-integration/(accessed 3 August 2019).

Berg, B. 2013. Characterizing Shale Plays: The Importance of Recognizing What You Don't Know, https://www.spe.org/d1/docs/2014/Berg-English.pdf (accessed 3 August 2019).

Gouveia, J. 2017. Fooled by Randomness: Improving Decision Making with Limited Data, Society of Petroleum Engineers Distinguished Lecture Series.

McLane, M. and Gouveia, J., 2015. Validating Analog Production Type Curves for Resource Plays. Presented at the SPE Liquids-Rich Basins Conference - North America, Midland, Texas, 2-3 September. SPE Paper 175527.

Tryggestad, M. and Dobbs, W. 2017. Session 25, Niobrara Carbonate Case Study. Unconventional Reservoir Summit It Building and Applying the Universal Workflow, Houston, 21-24 August.

Miller, P. and Gouveia, J., 2019. Applying Decision Trees to Improve Decision Quality in Unconventional Resource Development. Presented at the SPE Annual Technical Conference and Exhibition, Calgary, September 30-October 2. SPE Paper 195811.

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