About

Duane McVay is a Professor in Petroleum Engineering at Texas A&M University and holds the Albert B. Stevens Chair. He serves as Associate Director of the Crisman Institute for Petroleum Research. His educational background includes a Ph.D., M.S., and B.S. in Petroleum Engineering from Texas A&M University, completed in 1990, 1982, and 1980 respectively. His research interests encompass risk and uncertainty assessment, unconventional resource assessment, petroleum reservoir simulation, and integrated reservoir characterization and management. He has been recognized as a Distinguished Member of the Society of Petroleum Engineers since 2007 and served as a Distinguished Lecturer for the same society during 2015-2016. Additionally, he is a member of the Petroleum Engineering Academy of Distinguished Graduates of Texas A&M University and received the Practice Award from the Decision Analysis Society of the Institute for Operations Research and the Management Sciences in 2006.

Research topics

• Actuarial science
• Psychology
• Economics
• Statistics
• Econometrics
• Business
• Geology
• Geography
• Geomorphology
• Paleontology
• Environmental science

Selected publications

• Continuous Lookback, Calibration, and Adjustment Reduces Biases and Improves Reliability of Production Forecasts

  Proceedings of the 9th Unconventional Resources Technology Conference · 2021-01-01

  articleSenior author
  PublisherDOI

• Assessment of Oil and Gas Resources in the Vaca Muerta Shale, Neuquén Basin, Argentina

  SPE Latin American and Caribbean Petroleum Engineering Conference · 2020 · 1 citations

  • Geology
  • Environmental science
  • Geography

  Abstract According to the 2015 Energy Information Administration (EIA) global assessment (EIA 2015), Argentina ranks third among countries in shale-gas resources and fourth in shale-oil resources. The Vaca Muerta formation in the Neuquén basin holds most of these resources. After the first EIA assessment of these resources in 2011, there has been a huge increase in investment, given the interest of the Argentine government to recover the energy autonomy it lost in the 1990s. The objectives of this study were to update estimates of oil and gas reserves and resources with the four years of production data available, and to quantify the uncertainty in these estimates. The Vaca Muerta formation was subdivided into sub-areas based on the fluid type. The most appropriate production decline model was determined for each sub-area and coupled with Markov-Chain-Monte-Carlo (MCMC) methodology to analyze and forecast production of existing wells to calculate reserves. The analyses of individual wells in each sub-area were used to create probabilistic type-decline curves. These curves were combined with the estimated acreage-per-well distributions and the remaining drillable areas per sub-area to estimate contingent and prospective resources. The difference between contingent and prospective resources was based on the distance from existing wells. As of January 2018, the total reserves (P90–P50–P10) for the Vaca Muerta shale in Argentina associated with existing wells are estimated to be 8.5–17.5–38.4 MMm3 of oil and 9.5–27.2–74.6 Bm3 of gas. Estimated contingent resources are 8.8–50.6–181.1 MMm3 of oil and 2.6–16.4–51.5 Bm3 of gas. Estimated prospective resources are 424–2,464–8,771 MMm3 of oil and 211–1,279–3,483 Bm3 of gas. Resources and reserves estimates were combined to estimate technically recoverable resources (TRR). The estimated TRR are 443–2533–8992 MMm3 of oil and 223–1223–3609 Bm3 of gas. The results of this work should provide a more reliable assessment of the reserves and resources in the Argentine Vaca Muerta shale than previous estimates based on volumetric methodologies and analogies.

  PublisherDOI

• Technical Revisions Reveal Overconfidence in US and Canadian Reserves Estimates

  SPE Reservoir Evaluation & Engineering · 2020 · 2 citations

  • Actuarial science
  • Business
  • Econometrics

  Summary In this paper, we present methodology to quantify biases in reserves estimates using technical revisions (TRs) listed in reserves-reconciliation reports filed with regulators in the US and Canada. Using this methodology, we assessed the reliability of reserves estimates for 34 companies filing in Canada and 32 companies filing in the US from 2007 to 2017. Filers in both Canada and the US overestimated proved (1P) reserves, and US filers overestimated 1P reserves (51% positive TRs instead of 90%) more often than Canadian filers (72% positive TRs). Canadian filers underestimated proved-plus-probable (2P) reserves slightly (54% positive TRs instead of 50%). Considering the entire reserves distribution, Canadian filers were moderately overconfident (underestimated uncertainty) and slightly pessimistic. US filers, who report only 1P, were somewhere between the combination of extreme overconfidence and neutral directional bias (DB) and the combination of moderate overconfidence and extreme optimism. Three groups of professionals can benefit from this study: estimators, who can use the methodology to track their TRs over time, calibrate them, and use this information to improve future estimation procedures; investors, who can analyze reported reserves estimates to compare volumes fairly; and regulators, to whom the paper provides quantitative methodology to suggest to filers to help them ensure compliance with appropriate criteria for 1P and 2P reserves and avoid significant reserves write-downs later.

  PublisherDOI

• Improved Framework for Measuring the Magnitude and Impact of Biases on Project Evaluation

  SPE Reservoir Evaluation & Engineering · 2019-08-15 · 1 citations

  articleSenior author

  Summary Several authors over several decades (Capen 1976; Brashear et al. 2001; Rose 2004) have observed that the petroleum industry has consistently performed below expectations. Although this has been painfully obvious during the industry downturn beginning in 2014, available evidence suggests that even when the industry is profitable (e.g., during the decade before the most-recent downturn), it still performs substantially below expectations and its potential (Nandurdikar 2014). Many attribute this underperformance to cognitive biases in project evaluation, resulting in poor project valuation and selection. McVay and Dossary (2014) presented a simplified framework to estimate the cost of underestimating uncertainty. They demonstrated that chronic overconfidence and optimism (estimated distributions of project value are too narrow and shifted positively), which are common in the industry, produce substantial disappointment (realized portfolio values being less than estimated values), also common in the industry. In this work, we generalized the McVay and Dossary (2014) framework to include full estimated distributions (e.g., normal or log-normal), instead of the truncated distributions they used. In addition, we extended their framework to model underconfidence (estimated distributions too wide) and demonstrate that underconfidence is just as detrimental to portfolio performance as overconfidence. Decision error will be minimized and portfolio value will be maximized only when there is no bias in project estimation (i.e., neither overconfidence nor underconfidence and neither optimism nor pessimism). We compared the value gained from reducing biases with that from reducing uncertainty and found that reducing biases consistently generates more value than reducing uncertainty. Using either framework, operators can quantitatively measure biases—overconfidence, underconfidence, optimism, and pessimism—from lookbacks (comparing actual performance with probabilistic forecasts) and calibration plots. Once aware of the direction and magnitude of biases, operators have means for eliminating these biases in new forecasts through a combination of internal adjustment of uncertainty assessments, by means of training or ongoing feedback, and external adjustment of assessments, using measurements of bias from calibration results.

  PublisherDOI

• Assessment of the Reliability of Reserves Estimates of Public Companies in the US and Canada

  Proceedings of the 7th Unconventional Resources Technology Conference · 2019-01-01 · 1 citations

  articleSenior author

  Estimation of reserves is a process used to quantify the volumes of hydrocarbon fluids that can be recovered economically from a reservoir, field, area or region, from a given date forward. A considerable level of uncertainty is involved throughout the reserves-estimation process. Unfortunately, individuals are poor at assessing uncertainty, with a common tendency for overconfidence (underestimation of uncertainty) and optimism. \nThere are a few studies that address the reliability of reserves estimates, but none of them quantify the reliability of these estimates. This research aims to assess quantitatively the reliability of reserves estimates of public companies filing in the U.S. and Canada. To do this I measured biases in reported reserves estimates for 34 companies filing in Canada and 32 companies filing in the U.S. over the time period 2007 to 2017. \nCanadian companies explicitly report technical revisions of proved (1P) and proved-plus-probable (2P) reserves. U.S. companies do not report “technical revisions,” but instead report “revisions of previous estimates” and revisions due to price changes of proved (1P) reserves separately. I calculated Revisions Other Than Price (ROTP) by subtraction for U.S. companies and assumed the difference was the same as “technical revisions.” \nBased on probabilistic reserves definitions, it is reasonable to assume that proved reserves estimates are expected to have positive technical revisions 90% of the time, while proved- plus-probable reserves estimates are expected to have positive revisions 50% of the time. The reliability of proved and proved-plus-probable reserves estimates was assessed using calibration plots, in which the frequency of positive technical revisions is plotted against the estimate probability. Calibration plots can be used to measure confidence bias, ranging from underconfidence to complete overconfidence, and directional bias, ranging from complete pessimism to complete optimism. \n“Technical revisions” reported by 34 Canadian companies for the 11-year period were positive an average of 72% for 1P reserves and an average of 54% for 2P reserves, whereas the expected values were 90% and 50%, respectively. Thus, on average over this time period, filers in Canada overestimated 1P reserves and underestimated 2P reserves. Considering the entire reserves distributions, bias measurements indicate that filers in Canada were moderately overconfident and slightly pessimistic. Revisions Other Than Price (ROTP) calculated for 32 U.S. companies for the 11-year period were positive an average of only 51% for 1P reserves, compared to an expected 90%. Thus, on average over this time period, filers in the U.S. overestimated 1P reserves significantly. Considering the entire reserves distributions, bias measurements indicate that filers in the U.S. were somewhere between complete overconfidence and neutral directional bias, and moderate overconfidence and complete optimism. The biases in reserves estimates filed in both Canada and the U.S. suggest that adjustments in reserves estimation procedures are warranted. \nThree groups of professionals can benefit from this study: (1) estimators, who can use the methodology to track their technical revisions over time, calibrate them, and use this information to adjust future estimation procedures; (2) investors, who can analyze reported reserves estimates to compare volumes fairly; and (3) regulators, who can ensure that filers are complying with appropriate criteria for 1P and 2P reserves.

  PublisherDOI

• A Model for Optimizing Energy Investments and Policy under Uncertainty

  SPE Annual Technical Conference and Exhibition · 2016-09-08

  articleSenior author

  Abstract An energy producer must determine optimal energy investment strategies in order to maximize the value of its energy portfolio. Determining optimal energy investment strategies is challenging. One of the main challenges is the large uncertainty in many of the parameters involved in the optimization process. Most existing large-scale energy models are deterministic and so have limited capability for assessing uncertainty. Modelers usually use scenario analysis to address model input uncertainty. In this paper, we describe a coarse probabilistic model developed for optimizing energy investments and policies from an energy producer's perspective. The model uses a top-down approach to probabilistically forecast primary energy demand. Distributions rather than static values are used to model uncertainty in the input variables. The model can be applied to a country-level energy system. It maximizes portfolio expected net present value (ENPV) while ensuring energy sustainability. The model is built in MS Excel® using the @RISK add-in, which is capable of modeling uncertain parameters and performing stochastic simulation optimization. The model was applied to synthetic data for a typical fossil-fuel-dependent country to determine its optimum energy strategy. For this synthetic case, the model suggests that the subject country should increase its oil production capacity slightly higher than its current level, increase its gas production, and meet most of its future power generation (electricity) demand using alternative energy sources—nuclear, solar, and wind. A primary contribution of this work is rigorously addressing uncertainty quantification in energy modeling. The model could be applied, with minor modification, by either companies or countries to assist in determining optimal energy investment strategies.

  PublisherDOI

• Improved Framework for Measuring the Magnitude and Impact of Biases in Project Evaluation

  SPE Annual Technical Conference and Exhibition · 2016-09-26 · 1 citations

  articleSenior author

  Abstract Several authors over several decades (Capen 1976; Brashear et al. 2001; Rose 2004) have observed that industry performance has been consistently below expectations. While this is painfully obvious during the current industry downturn, available evidence suggests that even when the industry is profitable, e.g., during the decade prior to the most recent downturn, it still performs substantially below its expectations and its potential (Nandurdikar 2014). Many attribute this underperformance to cognitive biases in project evaluation, resulting in poor project selection and valuation. McVay and Dossary (2014) presented a simplified framework to estimate the cost of underestimating uncertainty. They demonstrated that chronic overconfidence and optimism (estimated distributions of project value too narrow and shifted positively), common in industry, produce substantial disappointment (difference between estimated and realized portfolio values), also common in industry. In this work, we generalized their framework to include full estimated distributions (e.g., normal or lognormal), instead of the truncated distributions they employed. In addition, we extended their framework to model underconfidence (estimated distributions too wide), and demonstrate that underconfidence is just as detrimental to portfolio performance as overconfidence. Decision error will be minimized and portfolio value will be maximized when there is no bias in project estimation—i.e., neither overconfidence nor underconfidence and neither optimism nor pessimism. Using either framework, we demonstrate that operators can quantitatively measure biases—overconfidence, underconfidence, optimism and pessimism—from lookbacks (comparing actual performance to probabilistic forecasts) and generation of calibration plots. Once aware of the direction and magnitude of biases, operators have means for eliminating these biases in subsequent forecasts through a combination of internal correction of uncertainty assessments, via training or ongoing feedback, and external correction of forecasts using measurements of bias from calibration results.

  PublisherDOI

• Industry Needs Re-Education in Uncertainty Assessment

  Journal of Petroleum Technology · 2015-02-01 · 5 citations

  article1st authorCorresponding

  Management It is clear the oil and gas industry recognizes the large uncertainty in which it operates. A search in the OnePetro technical paper database using the keywords “uncertainty” or “risk” returns more than 53,000 conference and journal papers. Yet, it is also clear that the industry does not know how to reliably assess uncertainty and that this inability negatively affects industry performance. Capen (1976) described the difficulty of assessing uncertainty. He pointed to massive capital overruns and low industry returns due to an almost universal tendency to underestimate uncertainty. Brashear et al. (2001) and Rose (2004) later documented the dismal performance of the industry in the last 10 to 20 years of the 20th century due to chronic bias and evaluation methods that do not account for the full uncertainty. Although industry profitability may have improved in the past decade because of high oil prices, Neeraj Nandurdikar in an October 2014 JPT article, “Wanted: A New Type of Business Leader to Fix E&P Asset Developments,” showed that the oil and gas industry continues to perform significantly below estimations and expectations. He cited three ways that assets erode value, all of which relate to unreliable assessments of uncertainty: (1) production and reserves estimates are overestimated, (2) capital costs are underestimated, and (3) development times are underestimated. And these do not include price estimations; the surprising oil price slide at the time of this writing has the potential to move industry performance from below expectations to below profitability. While project evaluations can be affected by many different types of biases, these can be reduced fundamentally to two primary biases: overconfidence and directional bias. Overconfidence is underestimation of uncertainty; i.e., our estimated distributions of uncertain quantities, such as reserves, are too narrow. They are too narrow because we do not consider all the possible outcomes. There is considerable evidence in the literature, both inside and outside the petroleum industry, of our general human tendency for overconfidence. Directional bias results when the subset of possible outcomes considered is shifted in either the optimistic or pessimistic direction. There is also evidence that we are usually optimistic in our overconfidence; i.e., we fail to consider some possible negative outcomes, or we give greater weight to possible positive outcomes than possible negative outcomes. As a result of the two primary biases, we make decisions with incorrect estimated distributions rather than true distributions (Fig. 1).

  PublisherDOI

• Probabilistic Assessment of World Recoverable Shale-Gas Resources

  SPE Economics & Management · 2015-01-15 · 14 citations

  article

  Summary Many shales previously thought of as only source rocks are now recognized as self-sourcing reservoirs that contain large volumes of natural gas and liquid hydrocarbons that can be produced by use of horizontal drilling and hydraulic fracturing. However, shale-gas resources and development economics are uncertain, and these uncertainties beg for a probabilistic solution. Our objective was to probabilistically determine the distribution of technically recoverable resources in highly uncertain and risky shale-gas reservoirs for seven world regions. To assess technically recoverable resources, we used the Unconventional Gas Resource Assessment System, which integrates Monte Carlo simulation with an analytical reservoir simulator, to derive a representative probability distribution of 25-year recovery factors (RFs) from five shale-gas plays in the US: the Barnett, Eagle Ford, Marcellus, Fayetteville, and Haynesville shales. The RFs for the five shale-gas plays follow a general beta-distribution with a mean value of 25%. Finally, we extended the distribution of RFs gained from the five shale-gas plays in the US to estimate technically recoverable shale-gas resources for the seven world regions. World technically recoverable shale-gas resources were estimated to range from 4,400 (P90) to 24,000 (P10) Tcf.1 This work provides important statistics for the five shale-gas plays in the US. Results of this work verify the existence of significant technically recoverable shale-gas resources and can help the industry better target its exploitation efforts in shale-gas plays worldwide.

  PublisherDOI

• Bayesian Probabilistic Decline-Curve Analysis Reliably Quantifies Uncertainty in Shale-Well-Production Forecasts

  SPE Journal · 2014-05-15 · 76 citations

  article

  Summary Several analytical decline-curve models have been developed recently for shale-gas wells (Ilk et al. 2008; Anderson et al. 2010; Valko and Lee 2010). However, these authors did not quantify the uncertainty in production forecasts and reserves estimates. This is important because most shale plays are in the early stages of production and virtually any method will have large uncertainty when there are limited production data available. Jochen and Spivey (1996) and Cheng et al. (2010) developed bootstrap methods that can generate probabilistic decline forecasts and quantify reserves uncertainty. Hindcasts with the modified bootstrap method (MBM) (Cheng et al. 2010) provide good coverage of the true cumulative production. However, the authors did not show they can quantify reserves uncertainty with limited production data in unconventional plays. In this paper, we introduce a Bayesian probabilistic methodology using Markov-chain Monte Carlo (MCMC) combined with Arps' decline-curve analysis. We tested this model on two data sets: Barnett shale horizontal-well gas production with more than 7 years of history and Eagle Ford shale horizontal-well oil production with more than 1 year of history. In both cases, P50 hindcasts were very close to true cumulative production and P90 and P10 hindcasts quantified the cumulative production uncertainty reliably with as little as 6 months of production available for matching. In this Bayesian methodology, the decline-curve parameters qi, Di, and b are assumed to be random variables instead of parameters to be modified to obtain a best fit. A Markov chain of the decline-curve parameters is constructed by use of MCMC with the Metropolis algorithm (random walk). We developed the model by performing hindcasts with the Barnett case study consisting of 197 horizontal gas wells with more than 7 years of production. The prior distribution, proposal distribution, and likelihood function were calibrated so the probabilistic decline curves quantified the cumulative-production uncertainty reliably with as little as 6 months of data. The same model was then tested with analysis of Eagle Ford shale oil production from 536 wells; the probabilistic decline curves quantified the cumulative-production uncertainty reasonably well by changing only the prior distribution. The proposed Bayesian methodology provides a means and a workflow to generate probabilistic decline-curve forecasts and quantify reserves uncertainty in shale plays quickly and reliably. This Bayesian methodology can also be applied with other analytical decline-curve models if desired.

  PublisherDOI

Frequent coauthors

• Walter B. Ayers

  Texas A&M University

  78 shared

• S. A. Holditch

  Mitchell Institute

  39 shared

• Yating Cheng

  Chinese Academy of Sciences

  33 shared

• Yueming Cheng

  32 shared

• Stephen A. Holditch

  29 shared

• W. J. Lee

  26 shared

• Wenyan Wu

  20 shared

• W. John Lee

  Oregon State University

  18 shared

Awards & honors

• Distinguished Member, Society of Petroleum Engineers (2007)
• Petroleum Engineering Academy of Distinguished Graduates of…
• Distinguished Lecturer, Society of Petroleum Engineers (2015…
• Practice Award, Decision Analysis Society of the Institute f…