A New Approach for Fast Evaluations of Large Portfolios of Oil and Gas Fields
- David Castineira (Quantum Reservoir Impact LLC) | Anirban Mondal (Quantum Reservoir Impact) | Sebastien Matringe (Quantum Reservoir Impact)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 27-29 October, Amsterdam, The Netherlands
- Publication Date
- Document Type
- Conference Paper
- 2014. Society of Petroleum Engineers
- Portfolio, Event Detection, Decline Curve Analysis, Reserves
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This paper presents a new systematic process for the evaluation of large portfolios of oil and gas fields where the performance and economic value of an entire portfolio is very rapidly derived. The automation of this workflow relies on some key technological developments, namely an automated algorithm for decline-curve analysis, and data mining studies of workovers and new well performance.
The automated decline-curve analysis tool presented here uses an event detection algorithm combined with quantile regression technique design to provide a robust probabilistic estimate of future PDP (proved-developed-producing) reserves on a well-by-well basis. Individual well behaviors are then aggregated stochastically to provide expected field and portfolio declines, with uncertainty ranges. Future well trends are estimated using probabilistic type-curves computed by data mining algorithms. Wells and fields are then individually assessed and ranked in terms of reserves and production metrics and financial information can be used to assess the value of the portfolio with a high-level of granularity.
A large portfolio of oil and gas fields in Texas and Louisiana is analyzed in this paper using the proposed methodology. For this portfolio computed decline rates, PDP reserves and cash flows are provided. Analysis of expected production from new wells and estimates of workover performance are also presented.
The analytical approach presented in this work is being used daily for comprehensive portfolio evaluations in the US and represents a significant change in the way divestitures and acquisitions evaluations are currently performed in the industry.
Organizations are increasingly recognizing that portfolio evaluations and management can help them make the decisions that will set them apart from their competitors . In particular Divestitures and Acquisitions (D&A) decisions are commonplace for the business development groups of most operators but surprisingly, they are rarely discussed in the petroleum literature. In order to limit the acquisition risks and ensure sound financial decisions, some essential engineering and geosciences analyses need to be performed within a limited timeframe. When an entire portfolio of assets is under review, the analysis should be consistent across all fields, which poses a further challenge.
Multiple methods to obtain optimal portfolios have been presented in the financial community for several decades. In fact the first methods were published by Markowitz in the 50's and the literature has been vastly expanded since these days . Most discussions and publications to date have been centered on the methods to perform portfolio optimization. However very little emphasis has been put on using analytical and data mining methods to very rapidly evaluate the current status of a given portfolio. The crux of the matter is that computing key portfolio parameters (e.g., total number of active wells and fields in the portfolio, field and portfolio decline rates, recent well performance, well production response to workovers, estimated PDP reserves, cash flows and net present values at field and portfolio levels, field group comparisons, etc…) require a significant amount of time that is typically not available for speedy D&A operations.
|File Size||3 MB||Number of Pages||15|