Geology topics
Emil D. Attanasi
Publications and source records attributed to Emil D. Attanasi.
Reconnaissance survey for potential energy storage and carbon dioxide storage resources of petroleum reservoirs in western Europe
Energy producers and utilities use oil and gas reservoirs for gas storage to meet peak seasonal demand or to supplement intermittent energy production. These reservoirs are also suitable for the long-term storage of carbon dioxide (CO 2 ), a greenhouse gas. This study reports on a reconnaissance analysis of the potential magnitude of storage resources in 9424 known oil and gas reservoirs from 24 countries within highly industrialized western Europe. To standardize the storage resources of the oil and gas reservoirs, their volumetric capacity is expressed in terms of metric tons (mass) of CO 2 . Estimates of recoverable oil and gas at the surface are converted to subsurface volumes and then converted to the equivalent mass of CO 2 at reservoir conditions. The results indicate 36.7 gigatons of CO 2 could be stored, with oil reservoirs accounting for 32% of that total and natural gas reservoirs comprising the remaining 68%. About four-fifths of the reservoir storage resource is offshore, with about three-fourths of that offshore resource at water depths of 200 m or less. Most countries do not have the reservoir storage resources to store 15 years of CO 2 at 2017 emission levels. With few exceptions the bulk of the storage is offshore for countries that do have at least 15 years of storage. The expansion of natural gas storage for strategic purposes in abandoned onshore gas reservoirs is not expected to seriously impact CO 2 storage. The contribution of this analysis is the description of the spatial distribution of potential storage and physical accessibility.
Visualization of petroleum exploration maturity for six petroleum provinces outside the United States and Canada
Outside the United States and Canada, most of the world’s supplies of oil and natural gas are recovered from conventional (or discrete) oil and gas accumulations. This type of hydrocarbon accumulation remains a target for exploration. In this report, exploration and discovery data are used to visually assist in describing the exploration maturity of selected petroleum provinces with respect to conventional oil and natural gas accumulations. The specific provinces are the Campos Basin (Brazil), the Santos Basin (Brazil), the North Sea Graben (northwestern Europe), the Middle Magdelena Basin (Colombia), the Sirte Basin (Libya), and the Kutei Basin (Indonesia). For each province, discovery data and well data through October 2019 are reported; from these data, depth distributions of the oil in oil fields and natural gas in gas fields were computed. The concepts of delineated prospective area and explored area include elements of geographic spatial information and statistical data analytics. Graphs showing dynamic growth of discoveries that are tied to the delineated prospective area provide a means of grading prospective area. Visualizations put the results of exploration in the context of geographic and geologic features of the play or basin and can be a tool to assist geologists with the appraisal of the number and sizes of undiscovered petroleum accumulations. Visualizations of exploration drilling and discoveries can (1) assist in conceptualizing a geologic model of the basin, (2) highlight relations among discovered accumulations in different plays or assessment units within the basin, and (3) allow the geologist to identify the missing information needed to complete the geologic model of a basin. Further, if visualization attributes can be quantified, they may be used for formulating quantitative models that predict numbers and sizes of undiscovered oil and gas accumulations. Such modeling approaches include discovery process models, Bayesian network models that characterize play or assessment unit dependencies, and innovative applications of machine learning to complement standard geologic assessments. The purpose of this report is to show how visualizations can further the understanding of exploration maturity for the six selected petroleum provinces. It also shows how the geologic framework, geologic data, and drilling and discovery trends can give context to the interpretation of the visualizations that lead to appraisal of exploration maturity.
National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Results
In 2020, the U.S. Geological Survey (USGS) completed a probabilistic assessment of the volume of technically recoverable oil resources available if current carbon dioxide enhanced oil recovery (CO 2 -EOR) technologies were applied to amenable oil reservoirs underlying the onshore and State waters areas of the conterminous United States. The assessment also includes estimates of the mass of CO 2 that could be stored (retained) as a result of CO 2 -EOR activities. The USGS assessment team evaluated more than 3,500 oil reservoirs that were miscible to injected CO 2 . The assessed reservoirs are in 185 previously defined USGS plays in 33 petroleum provinces of 7 national regions. The assessment team estimated that the technically recoverable oil associated with CO 2 -EOR ranges from approximately 25,000 million barrels (MMbbl) at the P 5 percentile to as much as 32,000 MMbbl at the P 95 percentile, with a mean of 29,000 MMbbl. The associated CO 2 retention ranges from approximately 7,400 million metric tons (Mt) at the P 5 percentile to as much as 9,500 Mt at the P 95 percentile, with a mean of 8,400 Mt. The West Texas and Eastern New Mexico region and the Gulf Coast region together contain 60 percent of the mean assessed CO 2 -EOR oil potential and 61 percent of the mean assessed CO 2 retention. Other regions with significant resource potential include the Midcontinent region and Rocky Mountains and Northern Great Plains region.
National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Summary
Introduction In 2020, the U.S. Geological Survey (USGS) completed a probabilistic assessment of the volume of technically recoverable oil resources that might be produced by using current carbon dioxide enhanced oil recovery (CO 2 -EOR) technologies in amenable conventional oil reservoirs underlying the onshore and State waters areas of the conterminous United States. The assessment also includes estimates of the mass of CO 2 that could be stored (retained) in the assessed oil reservoirs following the application of the CO 2 -EOR process. The USGS assessment team evaluated more than 3,500 oil reservoirs that were miscible to injected CO 2 . The assessed reservoirs are in 185 previously defined USGS plays in 33 petroleum provinces of 7 national regions. The team estimated that the total technically recoverable oil resulting from the application of the CO 2 -EOR process ranges from approximately 25,000 million barrels (MMbbl) at the P 5 percentile to as much as 32,000 MMbbl at the P 95 percentile, with a mean of 29,000 MMbbl. The associated CO 2 retention ranges from approximately 7,400 million metric tons (Mt) at the P 5 percentile to as much as 9,500 Mt at the P 95 percentile, with a mean of 8,400 Mt. The results are summarized in this fact sheet and are provided in more detail in the companion data release and circular. The West Texas and Eastern New Mexico region (primarily its Permian Basin) and the Gulf Coast region together contain 60 percent of the mean assessed CO 2 -EOR oil potential and 61 percent of the mean assessed CO 2 retention. Other regions with significant resource potential include the Midcontinent region and the Rocky Mountains and Northern Great Plains region.
Random forest
This entry defines and discusses the random forest machine learning algorithm. The algorithm is used to predict class or quantities for target variables using values of a set of predictor variables. It uses decision trees that are generated from bootstrap sampling of the training data set to create a "forest". The entry discusses the algorithm steps, the interpretative tools of the resulting model, current areas of research, and its limitations. Applications to the quantitative geosciences are reviewed as well as availability of software to implement the algorithm.
Machine learning can assign geologic basin to produced water samples using major ion geochemistry
Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a given geologic basin based on similarities to a given training dataset. Two datasets were derived from the PWGD: one with seven features but more samples (PWGD7), and another with nine features but fewer samples (PWGD9). The seven-feature dataset, prior to randomly generating a training and testing (i.e., validation) dataset, had 58,541 samples, 20 basins, and was classified based on total dissolved solids (TDS), bicarbonate (HCO 3 ), Ca, Na, Cl, Mg, and sulfate (SO 4 ). The nine-feature dataset, prior to randomly splitting into a training and testing (i.e., validation) dataset, contained 33,271 samples, 19 basins, and was classified based on TDS, HCO 3 , Ca, Na, Cl, Mg, SO 4 , pH, and specific gravity. Three supervised machine learning algorithms—Random Forest, k-Nearest Neighbors, and Naïve Bayes—were used to develop multi-class classification models to predict a basin of origin for produced waters using major ion chemistry. After training, the models were tested on three different datasets: Validation7, Validation9, and one based on data absent from the PWGD. Prediction accuracies across the models ranged from 23.5 to 73.5% when tested on the two PWGD-based datasets. A model using the Random Forest algorithm predicted most accurately compared to all other models tested. The models generally predicted basin of origin more accurately on the PWGD7-based dataset than on the PWGD9-based dataset. An additional dataset, which contained data not in the PWGD, was used to test the most accurate model; results suggest that some basins may lack geochemical diversity or may not be well described, while others may be geochemically diverse or are well described. A compelling result of this work is that a produced water basin of origin can be determined using major ions alone and, therefore, deep basinal fluid compositions may not be as variable within a given basin as previously thought. Applications include predicting the geochemistry of produced fluid prior to drilling at different intervals and assigning historical produced water data to a producing basin.
Implications of aggregating and smoothing daily production data on estimates of the transition time between flow regimes in horizontal hydraulically fractured Bakken oil wells
The level to which data are aggregated or smoothed can impact analytical and predictive modeling results. This paper discusses findings regarding such impacts on estimating change points in production flow regimes of horizontal hydraulically fractured shale oil wells producing from the middle member of the Bakken Formation. Change points that signal transitions in flow regimes are important because they subsequently affect estimates of ultimate recovery from wells producing from shale plays. Extending our earlier work, we employ two different statistical approaches, Bacon–Watts Bayesian regression and nonlinear constrained least squares regression, and a designed computational experiment to estimate the time of transition from the transient to the boundary-dominated flow regime for 14 different wells using daily production data rather than aggregated monthly data, as previously considered. The daily data were also smoothed to reduce noise. Computational experiments suggest that both statistical approaches can lead to plausible estimates of the transition point under different data aggregation or smoothing regimes, but that daily data are likely too granular to produce credible estimates. Although the expected value of transition points using smoothed daily data and monthly disaggregated data are generally comparable, the confidence intervals bounding the estimates based on smoothed daily data are generally wider. Our results not only inform the operational practices of oil producers engaged in economic evaluation of their shale resources and additional play development activities, but also the activities of petroleum research groups, government agencies, and financial organizations seeking to improve the trustworthiness of resource projections.
Comparison of machine learning approaches used to identify the drivers of Bakken oil well productivity
Geologists and petroleum engineers have struggled to identify the mechanisms that drive productivity in horizontal hydraulically fractured oil wells. The machine learning algorithms of Random Forest (RF), gradient boosting trees (GBT) and extreme gradient boosting (XGBoost) were applied to a dataset containing 7311 horizontal hydraulically fractured wells drilled into the middle member of the Bakken Formation from 2010 through 2017. The initial goal is to use these data‐driven machine learning algorithms to identify the most important explanatory predictors of well productivity within nine subareas and the composite area. Predictor variables representing initial gas production, the initial 180‐day water cut, and vertical depth vary spatially and are identified with geologically favorable areas. Well‐completion predictors include the well lateral length, number of fracture stages, volume of proppant per stage, and the volume of injected fluids per stage. The performance of methods is compared based on a common test sample. The analysis then examines the comparative predictive performance of the three algorithms for 1330 wells that had initiated production after the initial 7311 well sample had been producing. The computations of predictor importance identified the initial 180‐day water cut and the 30‐day initial gas production predictors as having a dominant influence in most subareas and for the composite area. The relative importance of well completion predictor variables, that is, the number of fracture stages per well, volume of injected proppant per stage, volume of injected fluids per stage, and lateral length, varied considerably across the subareas. For the common test or holdout sample, the models calibrated with the XGBoost algorithm had superior predictive power. The predictive power of all the algorithms trained on the data from the original sample suffered some loss when tested with a sample of wells that had started production after the end of that period. Implications of the empirical findings and strategies to mitigate loss of predictive power are discussed in the concluding section.
Well predictive performance of play-wide and Subarea Random Forest models for Bakken productivity
In recent years, geologists and petroleum engineers have struggled to clearly identify the mechanisms that drive productivity in horizontal, hydraulically-fractured oil wells producing from the middle member of the Bakken formation. This paper fills a gap in the literature by showing how this play’s heterogeneity affects factors that drive well productivity. It is important because understanding the relative strength of productivity drivers and how predictors vary spatially facilitates best-practices for well site selection and well completion design. The paper describes an application of the Random Forest (RF) machine learning technique to identify these mechanisms and to evaluate their importance across 9 subareas of the North Dakota portion of the Bakken play. The study examined productivity of 7311 wells initiating production from 2010 through 2017. Well productivity varied considerably across the 9 subareas within the play, so it was not surprising that the dominant predictors, the initial 180-day water cut and the 30-day initial gas production, vary spatially to mirror local conditions that strongly affect well productivity. The relative importance of well completion predictor variables, that is, the numbers of fractures stages per well, volume of injected proppant per stage, volume of injected fluids per stage, and lateral length, varied considerably across the subareas. Statistical permutation tests are presented that generally confirm the importance rankings. Subarea Random Forest models explained from 50 percent to 82 percent of the variation in productivity test samples while the play-wide model explained 73 percent of the test sample well productivity. Weakness in the predictive ability of the Random Forest models are traced to the limited variability in the training data. Implications of the empirical findings regarding the Bakken play for operators and for research and government institutions are discussed in the concluding section.
A probabilistic assessment methodology for carbon dioxide enhanced oil recovery and associated carbon dioxide retention
The U.S. Energy Independence and Security Act of 2007 authorized the U.S. Geological Survey (USGS) to conduct a national assessment of the potential volume of hydrocarbons recoverable by injection of carbon dioxide (CO 2 ) into known oil reservoirs with historical production. The implementation of CO 2 enhanced oil recovery (CO 2 -EOR) techniques could increase the U.S. recoverable hydrocarbon resource base. Use of anthropogenic CO 2 in the CO 2 -EOR process could reduce the amount of CO 2 released to the atmosphere by allowing a percentage of the injected CO 2 to remain in reservoir pore space once occupied by produced oil and water or by CO 2 dissolution in oil and water in the reservoir. The USGS has developed a new methodology for the national assessment of technically recoverable oil resources that may be produced by using current CO 2 -EOR technologies. The methodology relies on a proprietary reservoir-level database, the comprehensive resource database (CRD). The CRD incorporates commercially available geologic and engineering data, and USGS-defined play averages or province averages of reservoir data were used to populate incomplete records. Values from the CRD are used to estimate the original oil in place ( OOIP ) for each reservoir. The inputs are reviewed by USGS geologists, particularly when play or province averages have been used. Monte Carlo simulation is used to produce a numerical probability distribution for the OOIP for each reservoir, with the mean defined as the value of the OOIP in the CRD. A reservoir model (CO 2 Prophet, developed for the U.S. Department of Energy by Texaco, Inc.) is used to determine the incremental recovery factors for oil during the CO 2 -EOR process, on an individual reservoir basis. The model is also used to estimate the volume of CO 2 remaining in the reservoir after the CO 2 -EOR process is complete. Empirical decline curve analysis and comparison with data from published papers and reports on CO 2 -EOR projects are utilized to substantiate the simulation results. Numerical distributions of recovery factors are prepared for variations in the reservoir lithology (clastic or carbonate). The distribution of incremental oil is computed by multiplying the appropriate probability distribution of recovery factors by the individual reservoir distribution of the OOIP . A way to estimate the CO 2 remaining in the reservoir after the completion of the CO 2 -EOR process is also included in the methodology. Assessment results will be aggregated to play, petroleum province, regional, and national scales. This assessment methodology has been tested on the Horseshoe Atoll, Upper Pennsylvanian-Wolfcampian play in the Permian Basin Province in Texas; the play consists of 27 reservoirs having at least 2 billion barrels of OOIP that are amenable to the CO 2 -EOR process. The play was selected as a test case because CO 2 -EOR production data and published reports are available for several reservoirs within the play. Preliminary estimates of oil recoverable by implementation of miscible CO 2 -EOR are comparable to those reported in the literature and obtained by reservoir decline curve analysis.
Growth drivers of Bakken oil well productivity
This paper identifies the drivers of the phenomenal growth in productivity in hydraulically fractured horizontal oil wells producing from the middle member of the Bakken Formation in North Dakota. The data show a strong underlying spatial component and somewhat weaker temporal component. Drivers of the spatial component are favorable reservoir conditions. The temporal component of well productivity growth is driven by increasing the number of fracture treatments and by increasing the volume of proppant and injection fluids used on a per fracture treatment basis. Random Forest, a non-parametric modeling procedure often applied in the context of machine learning, is used to identify the relative importance of geologic and well-completion factors that have driven the growth in Bakken well productivity. The findings of this study suggest that a significant part of the well productivity increases during the period from 2010 to 2015 have been the result of improved well-site selection. For the more recent period, that is from 2015 through 2017, part of the improved well productivity has resulted from substantial increases in the proppant and injection fluids used per stage and per well.
Statistics of petroleum exploration in the world outside the United States and Canada through 2015
The world’s future oil and gas supplies depend on existing reserves and the additions to those reserves that may result, in part, from ongoing exploration and new discoveries. This Circular summarizes available oil and gas exploration data for the world outside the United States and Canada (the study area) through 2015. It updates U.S. Geological Survey Circulars 981, 1096, and 1288 (by D.H. Root, E.D. Attanasi, and R.L. Turner, 1987; E.D. Attanasi and D.H. Root, 1993; and E.D. Attanasi, P.A. Freeman, and J.A. Glovier, 2007). The exploration measures focus on the search for undiscovered conventional oil and gas accumulations. The goal of this compilation, presentation, and analysis of exploration and discovery data is to identify, at the reconnaissance level, the areas explored for oil and gas and to characterize their degree of exploration maturity. Maps and graphs provide a visual summary of the exploration maturity of an area. The maps include both land and offshore areas. The maps show delineated prospective areas, which are the industry-defined areas of interest in the search for undiscovered conventional oil and gas accumulations. The maps also show explored areas, which are areas where the density of exploration and development drilling rules out new discoveries of large conventional petroleum accumulations. Whereas the maps show the static state of oil and gas exploration, the dynamic measures of exploration progress are characterized graphically. The graphs show the growth in the delineated prospective and explored areas as a function of wildcat drilling. The relation between the expansion of the delineated prospective area and the rate of wildcat drilling is determined by the siting of the wildcat wells. Additional graphs show the magnitude of discoveries tied to specific delineated prospective areas. These graphs provide a way to evaluate the quality, in terms of discovered oil and gas, of areas identified by the dates when each area became prospective. From 2006 through 2015, the delineated prospective area within the study area expanded at a rate of about 48,100 square miles per year. This is slightly above the expansion rate of 46,200 square miles per year from 1996 through 2005. From 2006 through 2015, the explored area expanded at a rate of about 12,900 square miles per year, which is somewhat greater than the rate of 11,300 square miles per year for the period from 1996 through 2005. The delineated prospective area established by 1970 accounts for 35 percent of the delineated prospective area established through 2015 but contains 70 percent of the oil and 52 percent of the natural gas discovered through 2015. From 2006 through 2015, offshore discoveries accounted for 71 percent of the oil and 78 percent of the gas discovered in the study area and 40 percent of the offshore wildcat wells were drilled in deep offshore areas (deeper than 200 meters water depth). The delineated prospective area and explored area calculated with oil and gas wells and fields at depths of at least 10,000 feet are less than half of the respective areas calculated with all oil and gas wells and fields. The discovery histories of most regions indicate that average discovery sizes are generally larger in deeper geologic horizons. To correctly interpret the exploration maturity of a deep horizon, drilling and discovery data must be considered in the context of the geology of the area. Such analyses should be prepared at the level of the petroleum basin or subbasin.
Carbon dioxide enhanced oil recovery and residual oil zone studies at the U.S. Geological Survey
The U.S. Geological Survey (USGS) is preparing a national resource assessment of the potential hydrocarbons recoverable after injection of carbon dioxide (CO2) into conventional oil reservoirs in the United States. The implementation of CO2-enhanced oil recovery (CO2-EOR) techniques can increase hydrocarbon production, and lead to incidental retention of CO2 in reservoir pore space allowing long-term storage of anthropogenic CO2. A Comprehensive Resource Database (CRD) containing proprietary data on location, geologic, petrophysical, and reservoir parameters, plus production and well counts for major oil and gas reservoirs in onshore areas and State waters of the conterminous United States and Alaska, was developed to support the USGS assessment. Residual oil zones (ROZs) also can provide potential pore space for long-term storage of anthropogenic CO2. However, ROZs are not included in the upcoming USGS national CO2-EOR assessment because assessment methods for ROZs still are being developed. Additional ROZ CO2-EOR and CO2 retention data and reservoir simulations are needed to calibrate national ROZ assessment estimates.
Implications of aggregating daily production data on estimates of ultimate recovery from horizontal hydraulically fractured Bakken oil wells
The level to which data are aggregated can impact analytical and predictive modeling results. In this short paper we discuss some of our findings regarding the impacts of data aggregation on estimating change points in the production profiles of horizontal hydraulically fractured Bakken oil wells. Change points occur when production transitions from one flow regime to another. Change point determination is important because it governs calculation of ultimate recovery from these and similar wells drilled in shale plays.
Statistical detection of flow regime changes in horizontal hydraulically fractured Bakken oil wells
The application of horizontal and hydraulically fractured wells for producing oil from low permeability formations has changed the face of the North American oil industry. One feature of the production profile of many such wells is a transition from transient linear oil flow to boundary-dominated flow. The identification of the time of this transition is important for the calibration of models that forecast the well’s future production and the expected ultimate recovery. It is preferable that such models generally use data from the boundary-dominated flow regime for parameter calibration. Accurate well production forecasts are needed for operational decisions, long-term planning, commercial transactions, regulatory proceedings, and asset valuation. Petroleum engineers frequently make the call on the transition point based on subjective visual interpretations of log–log plots for individual wells. This is time-consuming and is generally not repeatable by other analysts. This note evaluates statistical approaches that can serve as alternatives to the subjective visual interpretations. Specifically, the predictive performance of production models calibrated with boundary-dominated data based on transition dates calculated with constrained nonlinear least squares and Bayesian regressions was very close to that obtained using the visual method, suggesting that statistical approaches may indeed be constructed to replace less objective visual approaches without loss of accuracy.
Overview of a comprehensive resource database for the assessment of recoverable hydrocarbons produced by carbon dioxide enhanced oil recovery
A database called the “Comprehensive Resource Database” (CRD) was prepared to support U.S. Geological Survey (USGS) assessments of technically recoverable hydrocarbons that might result from the injection of miscible or immiscible carbon dioxide (CO 2 ) for enhanced oil recovery (EOR). The CRD was designed by INTEK Inc., a consulting company under contract to the USGS. The CRD contains data on the location, key petrophysical properties, production, and well counts (number of wells) for the major oil and gas reservoirs in onshore areas and State waters of the conterminous United States and Alaska. The CRD includes proprietary data on petrophysical properties of fields and reservoirs from the “Significant Oil and Gas Fields of the United States Database,” prepared by Nehring Associates in 2012, and proprietary production and drilling data from the “Petroleum Information Data Model Relational U.S. Well Data,” prepared by IHS Inc. in 2012. This report describes the CRD and the computer algorithms used to (1) estimate missing reservoir property values in the Nehring Associates (2012) database, and to (2) generate values of additional properties used to characterize reservoirs suitable for miscible or immiscible CO 2 flooding for EOR. Because of the proprietary nature of the data and contractual obligations, the CRD and actual data from Nehring Associates (2012) and IHS Inc. (2012) cannot be presented in this report.
Using CO 2 Prophet to estimate recovery factors for carbon dioxide enhanced oil recovery
Introduction The Oil and Gas Journal’s enhanced oil recovery (EOR) survey for 2014 (Koottungal, 2014) showed that gas injection is the most frequently applied method of EOR in the United States and that carbon dioxide (CO 2 ) is the most commonly used injection fluid for miscible operations. The CO 2 -EOR process typically follows primary and secondary (waterflood) phases of oil reservoir development. The common objective of implementing a CO 2 -EOR program is to produce oil that remains after the economic limit of waterflood recovery is reached. Under conditions of miscibility or multicontact miscibility, the injected CO 2 partitions between the gas and liquid CO2 phases, swells the oil, and reduces the viscosity of the residual oil so that the lighter fractions of the oil vaporize and mix with the CO 2 gas phase (Teletzke and others, 2005). Miscibility occurs when the reservoir pressure is at least at the minimum miscibility pressure (MMP). The MMP depends, in turn, on oil composition, impurities of the CO 2 injection stream, and reservoir temperature. At pressures below the MMP, component partitioning, oil swelling, and viscosity reduction occur, but the efficiency is increasingly reduced as the pressure falls farther below the MMP. CO 2 -EOR processes are applied at the reservoir level, where a reservoir is defined as an underground formation containing an individual and separate pool of producible hydrocarbons that is confined by impermeable rock or water barriers and is characterized by a single natural pressure system. A field may consist of a single reservoir or multiple reservoirs that are not in communication but which may be associated with or related to a single structural or stratigraphic feature (U.S. Energy Information Administration [EIA], 2000). The purpose of modeling the CO 2 -EOR process is discussed along with the potential CO 2 -EOR predictive models. The data demands of models and the scope of the assessments require tradeoffs between reservoir-specific data that can be assembled and simplifying assumptions that allow assignment of default values for some reservoir parameters. These issues are discussed in the context of the CO 2 Prophet EOR model, and their resolution is demonstrated with the computation of recovery-factor estimates for CO 2 -EOR of 143 reservoirs in the Powder River Basin Province in southeastern Montana and northeastern Wyoming.