Duane A. McVay

Quantifying the Reliability and Value of 3D Land Seismic

Seismic data provide essential information for guiding reservoir development. Improvements in data quality hold the promise of improving performance even further, provided that the value of these data exceeds their cost. Previous work has demonstrated value-of-information (VOI) methods to quantify the value of seismic data. In these examples, seismic accuracy was obtained by means of expert assessment instead of being based on geophysical quantities. In addition, the modeled seismic information was not representative of any quantity that would be observed in a seismic image. Here we apply a more general VOI model that includes multiple targets, budgetary constraints, and quantitative models relating poststack seismic amplitudes and amplitude-variation-withoffset (AVO) parameters to the quantities of interest for reservoir characterization, such as porosity and reservoir thickness. Also, by including estimated changes in data accuracy based on signal-tonoise ratio, the decision model can provide objective estimates of the reliability of measurements derived from the seismic data. We demonstrate this methodology within the context of a west Texas 3D land survey. This example demonstrates that seismic information can improve reservoir economics and that improvements in seismic technology can create additional value.

Using an Expert System To Identify a Well-Test-Interpretation Model

Presentation of a new approach that uses artificial intelligence to identify the well-test-interpretation model that best describes the behavior of a reservoir. This knowledge-based (expert) system is composed of carefully extracted rules and facts for buildup and drawdown test analysis. The elaborate control needed to simulate a humans reasoning process was simplified with a blackboard architecture. An application of the expert system, to a field-case is presented

Evaluation of a Statistical Infill Candidate Selection Technique

Evaluation of a Statistical Infill Candidate Selection Technique. (May 2003) Linhua Guan, M.S., University of Petroleum of China, P.R. China Chair of Advisory Committee: Dr. Duane A. McVay Quantifying the drilling or recompletion potential in producing gas basins is often a challenging problem, because of large variability in rock quality, well spacing, and well completion practices and the large number of wells involved. Complete integrated reservoir studies to determine infill potential are often too time-consuming and costly for many producing gas basins. In this work we evaluate the accuracy of a statistical moving-window technique that has been used in tight-gas formations to assess infill and recompletion potential. The primary advantages of the technique are its speed and its reliance upon well location and production data only. We used the statistical method to analyze simulated low-permeability, 100-well production data sets, then compared the moving-window infill-well predictions to those from reservoir simulation. Results indicate that moving-window infill predictions for individual wells can be off by more than 50%; however, the technique accurately predicts the combined infill-production estimate from a group of infill candidates, often to within 10%. We found that the accuracy of predicted infill performance decreases as heterogeneity increases and increases as the number of wells in the project increases. The cases evaluated in this study included real-world well spacing and production rates and a significant amount of depletion at the infill locations. Because of its speed, accuracy and reliance upon readily available data, the moving window technique can be a useful screening tool for large infill development projects.

Quantification of Uncertainty in Reserve Estimation From Decline Curve Analysis of Production Data for Unconventional Reservoirs

Decline curve analysis is the most commonly used technique to estimate reserves from historical production data for the evaluation of unconventional resources. Quantifying the uncertainty of reserve estimates is an important issue in decline curve analysis, particularly for unconventional resources since forecasting future performance is particularly difficult in the analysis of unconventional oil or gas wells. Probabilistic approaches are sometimes used to provide a distribution of reserve estimates with three confidence levels (P10, P50, and P90) and a corresponding 80% confidence interval to quantify uncertainties. Our investigation indicates that uncertainty is commonly underestimated in practice when using traditional statistical analyses. The challenge in probabilistic reserve estimation is not only how to appropriately characterize probabilistic properties of complex production data sets, but also how to determine and then improve the reliability of the uncertainty quantifications. In this paper, we present an advanced technique for the probabilistic quantification of reserve estimates using decline curve analysis. We examine the reliability of the uncertainty quantification of reserve estimates by analyzing actual oil and gas wells that have produced to near-abandonment conditions, and also show how uncertainty in reserve estimates changes with time as more data become available. We demonstrate that our method provides a more reliable probabilistic reserve estimation than other methods proposed in the literature. These results have important impacts on economic risk analysis and on reservoir management.

Inverted Hockey Stick Method Quantifies Price Uncertainty in Petroleum Investment Evaluation

Abstract In this paper, we present a new method for quantifying the uncertainty of economic projections due to uncertainty in future oil prices. Traditionally, the petroleum industry has employed what are known as “hockey stick” price forecasts, i.e., monotonically increasing price profiles, in economic calculations to evaluate investment opportunities. Calculations are often run using most-likely, optimistic, and pessimistic price profiles in an attempt to quantify the uncertainty in the resulting economic indicators. These conventional hockey stick methods significantly underestimate uncertainty because they do not reproduce the volatility inherent in oil prices. Stochastic methods that attempt to model price volatility have been used successfully and indicate that there is considerably more uncertainty in oil and gas development projects than has been previously recognized. However, many operators do not use stochastic methods for modeling oil prices, most likely because they require more time and effort to implement than conventional methods. The Inverted Hockey Stick method presented herein is similar to conventional methods in that only three price realizations are run to quantify uncertainty. However, the high and low cases are designed to better capture the range of possible future price paths. Uncertainty ranges for economic indicators predicted by the new method are comparable to 70–95% probability ranges predicted by the stochastic bootstrap method, significantly greater than the 32–42% ranges predicted by conventional methods. This new method can be easily incorporated into existing economic modeling systems. Recognition of the greater uncertainty in oil and gas investment opportunities, both upside as well as downside, should improve investment decision making.

PRISE: Petroleum Resource Investigation Summary and Evaluation

PRISE: Petroleum Resource Investigation Summary and Evaluation. (August 2008) Sara Old, B.S., Texas A&M University Chair of Advisory Committee: Dr. Stephen Holditch As conventional resources are depleted, unconventional gas (UG: gas from tight sands, coal beds, and shale) resources are becoming increasingly important to U.S and world energy supply. The volume of UG resources is generally unknown in most international basins. However, in 25 mature U.S. basins, UG resources have been produced for decades and are well characterized in the petroleum literature. The objective of this work was to develop a method for estimating recoverable UG resources in target, or exploratory, basins. The method was based on quantitative relations between known conventional and unconventional hydrocarbon resource types in mature U.S. basins. To develop the methodology to estimate resource volumes, we used data from the U.S. Geological Survey, Potential Gas Committee, Energy Information Administration, National Petroleum Council, and Gas Technology Institute to evaluate relations among hydrocarbon resource types in the Appalachian, Black Warrior, Greater Green River, Illinois, San Juan, Uinta-Piceance, and Wind River basins. We chose these seven basins because they are mature basins for both conventional and unconventional

Quantification of Uncertainty by Combining Forecasting with History Matching

Abstract Quantifying uncertainty in production forecasts is critical to making good reservoir management decisions, particularly for many current investment opportunities that require intensive technology and large investments, and that may have marginal profitability indicators. Reservoir studies are conducted to support decision making, but reservoir management decisions must often be made before completion of these studies. This paper presents a new approach to reservoir studies that combines production forecasting with history matching. The approach provides preliminary production forecasts much earlier in reservoir studies. More importantly, the approach provides estimates of uncertainty associated with the forecasts. This is accomplished by using the mismatch of history match runs to weight corresponding forecast runs. We illustrate application of the method to the 8-Sand reservoir in the Green Canyon 18 field, Gulf of Mexico. We observed that, as the accuracy of the model increased during the history match, the uncertainty of forecasted reserves decreased and the distribution of reserves stabilized. Early forecasts and associated estimates of uncertainty provided by our new method can be quite valuable to management in making investment decisions.

A Practical Nonlinear Regression Technique for Horizontal Well Test Interpretation

Abstract A practical nonlinear regression technique for analysis of horizontal well pressure transient tests is presented. This technique can provide accurate and reliable estimation of well-reservoir parameters if downhole flow rate data are available. In situations without flow rate measurement, reasonably reliable parameter estimation can be achieved by using the detected flow rate from blind deconvolution. This technique has the advantages of eliminating the need for estimation of the wellbore storage coefficient and providing reasonable estimates of effective wellbore length. It provides a practical tool for enhancement of horizontal well test interpretation, and its practical applicability is illustrated by synthetic and actual field cases.

Calibration improves uncertainty quantification in production forecasting

Despite recent advances in uncertainty quantification, the petroleum industry continues to underestimate the uncertainties associated with reservoir production forecasts. This paper describes a calibration process that can improve quantification of uncertainties associated with reservoir performance prediction. Existing methods underestimate uncertainty because they fail to account for all, and particularly unknown, factors affecting reservoir performance and because they do not investigate all combinations of reservoir parameter values. However, the primary limitation of existing methods is that their reliability cannot be verified because the testing of an estimate of uncertainty from existing methods yields only one sample for what is inherently a statistical result. Verification and improvement of uncertainty estimates can be achieved with calibration – comparison of actual performance with previous uncertainty estimates and then using the results to scale subsequent uncertainty estimates. Calibration of uncertainty estimates can be achieved with a more frequent, if not continuous, process of data acquisition, model calibration, model prediction and uncertainty assessment, similar to the process employed in weather forecasting. Improved ability to quantify production forecast uncertainty should result in better investment decision making and, ultimately, increased profitability.

The Impact of Gravity Segregation on Multiphase Non Darcy Flow in Hydraulically Fractured Gas Wells

The Impact of Gravity Segregation on Multiphase Non-Darcy Flow in Hydraulically Fractured Gas Wells. (August 2008) Mark Dickins, B.S., The University of Texas at Austin Chair of Advisory Committee: Dr. Duane McVay Multiphase and non-Darcy flow effects in hydraulically fractured gas wells reduce effective fracture conductivity. Typical proppant pack laboratory experiments are oriented in such a way such that phase segregation is not possible, which results in mixed flow. Tidwell and Parker (1996), however, showed that in proppant packs, gravity segregation occurs for simultaneous gas and liquid injection at laboratory scale (1500 cm 2 ). Although the impact of gravity on flow in natural fractures has been described, previous work has not fully described the effect of gravity on multiphase non-Darcy flow in hydraulic fractures. In this work, reservoir simulation modeling was used to determine the extent and impact of gravity segregation in a hydraulic fracture at field scale. I found that by ignoring segregation, effective fracture conductivity can be underestimated by up to a factor of two. An analytical solution was developed for uniform flux of water and gas into the fracture. The solution for pressures and saturations in the fracture agrees well with reservoir simulation. Gravity segregation occurs in moderate-to-high conductivity fractures.