Probabilistic Aggregation of Oil and Gas Field Resource Estimates and Project Portfolio Analysis

Elk, Jan van; Gupta, Ritu; Wann, D.

doi:10.2118/116395-pa

Cited by 12 publications

(4 citation statements)

References 16 publications

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“…In this method the distributions of the field reserves are normal, thus the aggregated project reserves is also normal. According to van Elk et al (2008), this represents a disadvantage, once all the distributions have to be symmetrical. The authors state that for fields in the early stage of appraisal and for undiscovered prospects symmetry of the resources distribution is not often a good approximation.…”

Section: Reserves Distribution Estimation Methodsmentioning

confidence: 99%

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Probabilistic Reserves and Resources Estimation: A Methodology for Aggregating Probabilistic Petroleum Reserves and Resources

et al. 2012

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The oil industry strives to create an international standard for classification and estimation of resources since the 1930s. The goal is to provide investors with information obtained under the same assumptions, as to facilitate the comparison between petroleum companies. In 2007 the four major international organizations, Society of Petroleum Engineers (SPE), American Association of Petroleum Geologists (AAPG), World Petroleum Council (WPC) and Society of Petroleum Evaluation Engineers (SPEE), jointly released a single set of guidelines for classification and evaluation of oil and gas resources, the Petroleum Resources Management System (PRMS, 2007).Several methodologies for estimating reserves can be employed within the PRMS's (2007) premises, which can be classified as deterministic or probabilistic. Unconventional resources emerge as a new frontier for the oil industry, thus implying high uncertainty levels in both technical and economic assessments. The main purpose of this paper is to explore this issue and to propose a correlation-based probabilistic methodology for aggregating oil and gas reserves of conventional and unconventional resources. The methodology is in accordance with the guidelines of the PRMS (2007) and with the new rules of the Securities Exchange Commission 2009 (SEC). The correlation assessment evaluates technical and operational features, and the probabilistic aggregation is performed by Monte Carlo Simulation (MCS).Besides the introductory section, this paper comprises four other sections. A literature review presents definitions of conventional and unconventional resources, and an examination of classification, estimation and aggregation of reserves, important for better understanding the following sections. The third section describes the proposed correlation-based probabilistic methodology. Afterward, a case study presents an application of the methodology. Finally, the last section synthesizes the main conclusions. IntroductionThe estimation of petroleum reserves entails complex assumptions and calculations, once there is uncertainty associated with volumes, recovery, development, and marketability of resources. Unconventional resources bring new challenges to the industry, reinforcing that uncertainty analyses are paramount for estimating reserves.Yet, uncertainty based assessments are not trivial assignments. There must be a thorough understanding of the models to be employed, in order not to overestimate proved reserves.When reserves are determined by probabilistic methods, the arithmetic addition of individual accumulations within an integrated project will understate the aggregated reserves at the proved (P90) level. On the other hand, if full independency is assumed and the accumulations share common risks, the probabilistic addition of reserves will overstate the proved (P90) reserves (Carter and Morales, 1998).This paper introduces a practical correlation-based methodology for aggregating probabilistic reserves from both conventional and unconventional resources. As long as ...

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Section: Reserves Distribution Estimation Methodsmentioning

confidence: 99%

“…For example, "weakmedium -strong" correlations are associated with "0.1 -0.3 -0.5" correlation coefficients (van Elk et al, 2000);  Tornado diagrams analysis (van Elk et al, 2008).…”

Section: Correlation Assessmentmentioning

confidence: 99%

Probabilistic Reserves and Resources Estimation: A Methodology for Aggregating Probabilistic Petroleum Reserves and Resources

et al. 2012

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“…The idea is using experimental design method to create a set of simulation inputs resulting in a response equation. Monte Carlo simulation can be peformed on the response equation to gives a probability distribution of the parameters (Anggraini, Hereyanto., & Bhakti, 2017;El-Ghalayini, 2016;Van Elk, Gupta, Wann, et al, 2010). The P ield LLP initiative can be classi ied as an engineering or technical project (Larson & Gray, 2011).…”

Section: Figure 1 P Ield Pipeline Network Mapmentioning

confidence: 99%

Managing production profile uncertainties in P field LLP project economic evaluation using factorial design

2019

J. adm. bus. stud.

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This study evaluates a new approach to perform an economic evaluation for the Low Low Pressure (LLP) project on the different platforms. P ield is a Gas ield in the Mahakam area, East Kalimantan, Indonesia. Since 1999, its production has decreased and is forecasted to fall below economical cut-off starting at the end of 2020.project may extend the life of the ield. In 2016, An economic evaluation had been performed for the LLP pilot project on Platform 4 and Platform 5 but did not properly integrate the uncertainties behind the production pro ile. The new method proposed in this study develops a model from platform 2 data to capture production pro ile uncertainties by using factorial design. Monte Carlo simulation is applied to the model to obtain a production pro ile range. Economic analysis is then performed to calculate Net Present Value (NPV), Internal Rate of Return (IRR), and Payout Time (POT). Eventually, the new method concludes that the LLP project on Platform 2 will generate cumulative cash low between 32.9 to 60.4 MUSD (NPV0) or 11.7 to 21.8 MUSD (NPV11) with IRR ranging between 27 to 34%. Therefore it is economical. Information gathered from Platform 2 evaluation hinted that only Platform 1 has the potential to be economical. Additional work is required to have a complete economic evaluation. However, the management should decide to go on with the platform 2 LLP project.

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“…Direct probabilistic aggregation of a collection of basic unit magnitudes into a sum of all unit magnitudes requires specification of a joint probability distribution incorporating probabilistic dependencies among basic unit magnitudes, a daunting exercise when the number of units is large. Many resource assessment studies do specify a joint probability distribution of basic unit magnitudes (Carter and Morales 1998;Schuenemeyer 2005;Delfiner and Barrier 2008;Pike 2008;Schuenemeyer and Gautier 2010;Van Elk and Gupta 2010;Blondes et al 2013a and2013c; U.S. Geological Survey Carbon Dioxide Storage Resources Assessment Team 2013). Crovelli and Balay (1991) characterize dependencies among basic units and between aggregates in terms of covariances and correlations.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Aggregation of Uncertain Geological Resources

et al. 2018

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Commodities such as oil and gas occur in isolated reservoirs or accumulations, more generically called basic units here. To understand a study area's economic potential and to craft plans for exploration and development, resource analysts often aggregate (sum, accumulate) basic unit magnitudes in distinct spatial subsets of the study area and then appraise the total area's potential by summing these intermediate sums. In a probabilistic approach, magnitudes are modeled as random variables. Some have asked, "Do different methods of partitioning basic units into subsets lead to different probability distributions for the sum of all basic unit magnitudes?" Any method of aggregation of basic unit magnitudes which obeys the rules of probability leads to the same probability distribution of the sum of all unit magnitudes as that computed by direct summation of all basic unit magnitudes. A Monte Carlo simulation of a synthetic example in which the magnitude of resource in each unit is marginally lognormal and pairwise correlations among basic unit magnitudes are specified illustrates key features of probabilistic aggregation. The joint distribution of certain pairs of aggregates are closely approximated by a bivariate lognormal distribution.

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Probabilistic Aggregation of Oil and Gas Field Resource Estimates and Project Portfolio Analysis

Cited by 12 publications

References 16 publications

Probabilistic Reserves and Resources Estimation: A Methodology for Aggregating Probabilistic Petroleum Reserves and Resources

Probabilistic Reserves and Resources Estimation: A Methodology for Aggregating Probabilistic Petroleum Reserves and Resources

Managing production profile uncertainties in P field LLP project economic evaluation using factorial design

Probabilistic Aggregation of Uncertain Geological Resources

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