Tracking Procedures for Reserves and Resources Other than Reserves ROTR for Internal Reporting Processes
- Nefeli Moridis (Texas A&M University) | W. John Lee (Texas A&M University) | Wayne Sim (Aucerna) | Thomas Blasingame (Texas A&M University)
- Document ID
- Unconventional Resources Technology Conference
- SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, 18-19 November, Brisbane, Australia
- Publication Date
- Document Type
- Conference Paper
- 2019, Unconventional Resources Technology Conference (URTeC)
- Reserves tracking, reporting processes
- 29 in the last 30 days
- 31 since 2007
- Show more detail
|SPE Member Price:||USD 9.50|
|SPE Non-Member Price:||USD 28.00|
This paper provides planners with a methodology (or tool) that will allow evaluators to progress resources from classifications with lower chances of commercially (COC) to classes with higher chances of commercially (top sub-classes of Reserves) and also to progress resources from categories with large uncertainty to categories with less uncertainty of eventual recovery. This is important to entities of all sizes for planning purposes because companies should track their resources regardless of project stage or size. Our methodology provides continuous tracking of volumes when moving from Prospective Resources to Contingent Resources to Reserves throughout the life of the project, and allows for more accurate Reserves reporting.
We begin this work with the relationship between the Reserves categories in the PRMS matrix, modeled using the Gaussian Quadrature (GQ) presented in SPE 195480 by Moridis et al. (2019). To do this, the authors "selected 38 wells from a Permian Basin dataset, and performed probabilistic decline curve analysis (DCA) using the Arps Hyperbolic model and Monte Carlo simulation (MCS) to obtain a probability distribution of the 1P, 2P, and 3P volumes. We considered this information to be our "truth case," to which we compared relative weights of different Reserves categories from the GQ and SM methodologies. We also performed probabilistic rate transient analysis (RTA) using the IHS Harmony software to obtain the 1P, 2P, and 3P volumes, and calculated the relative weights of each Reserves category. Once we completed these first two steps, we implemented a 3-point GQ to obtain the weights and percentiles for each well. We analyzed the GQ results by calculating the percentage differences between the probabilistic DCA, RTA, and GQ results."
We then develop functional relationships across the vertical elements of the PRMS matrix by simulating event-variant movement across categories. Resources move on a time basis, and the rate of movement differs for different classes and categories. We implement the COC presented by Etherington et al. (2010) to develop relationships between the vertical elements of the PRMS matrix using the GQ weights, however this value can also be user-defined. Etherington's values are used purely as an example to produce results for this work.
Based on our results, the uncertainty of the relative weights of Contingent and Prospective Resources categories increases as we move down the PRMS matrix, so as we incorporate this uncertainty and the weights differ slightly from those estimated for Reserves (presented in SPE 195480). We also note that the COC is user-defined for every project, so the proposed relationships will differ for every project The time-rate of movement between categories also differs for every project; there is no "one-size-fits-all" solution. The COC changes for each project because the risks differ in each project and it is at the engineer's discretion to use the appropriate COC
|File Size||1 MB||Number of Pages||15|
Bikel, J.E., Lake, L.W., Lehman, J. 2011. Discretization, Simulation, and Swanson's (Inaccurate) Mean. J Pet Technol 3 (3): 128-140. SPE 148542. https://dx.doi.org/10.2118/148541-PA
Elliott, D.C. 2008: The Evaluation, Classification and Reporting of Unconventional Resources. SPE Unconventional Reservoirs Conference, Keystone, Colorado, USA, 10-12 February. SPE 114160. https://dx.doi.org/10.2118/114160-MS
Etherington, J.R., Stabell, C.B. 2010: Building on PRMS to Quantify Risk and Uncertainty in Resource Reconciliations. SPE Annual Technical Conference and Exhibition, Florence, Italy, 19-22 September. SPE 134057. https://dx.doi.org/10.2118/134057-MS
Hurst, A., Brown, G.C., Swanson, R.I. 2000: Swanson's 30-40-30 rule. AAPGBulletin 84 (12): 18831891. https://dx.doi.org/10.1306/8626C70D-173B-11D7-8645000102C1865D
Moridis, N., Lee, W.J., Sim, W., Blasingame, T.A. 2019: Guassian Quadrature Accurately Approximates the Relative Weights of Each Reserves Category of the PRMS Matrix Through a Cumulative Distribution Function. SPE Europec featured at 81st EAGE Conference & Exhibition, London, UK, 3-6 June. SPE 195480. https://dx.doi.org/10.2118/195480-MS
Nederlof, M.H. 2018. Lognormal Distribution. https://www.mhnederlof.nl/lognormal.html (accessed on February 10, 2019)
PRMS AG. 2011. Guidelines for Application of the Petroleum Resources Management System (PRMS), http://www.spe.org/industry/docs/PRMS_Guidelines_Nov2011.pdf (accessed 28 November 2017).
PRMS. 2018. Petroleum Resources Management System (PRMS). SPE. https://www.spe.org/en/industry/petroleum-resources-management-system-2018/
University of Maryland Institute for Advanced Computer Studies. Lecture 16 - Gaussian Quadratures. http://www.umiacs.umd.edu/~ramani/cmsc460/Lecture16_integration.pdf (accessed on November 8 2017)
Wikipedia. Log-normal Distribution. https://en.wikipedia.org/wiki/Log-normal_distribution (accessed on November 8 2017)
Wikipedia. Gaussian Quadrature. https://en.wikipedia.org/wiki/Gaussian_quadrature (accessed on November 8 2017)