Geology topics

Emil Attanasi

Publications and source records attributed to Emil Attanasi.

At least 19 recordsLinked to original sources

USGS2026

Inference of pattern-based geological CO2 sequestration and oil recovery potential in a commingled main pay and residual oil zone CO2-EOR flood

Several detailed studies have shown that residual oil zones (ROZs) can present significant resources for additional hydrocarbon recovery as well as subsurface carbon dioxide (CO 2 ) sequestration via enhanced oil recovery by injecting CO 2 (CO 2 -EOR). Field development strategies included new wells drilled dedicated to main pay zones (MPZ) and ROZs, or existing wells in MPZs deepened to ROZs for commingled injection-production using different well patterns. The latter presented a challenge when discerning the injection and production from each of the zones, and for subsequent quantification of CO 2 sequestration and EOR potential from different patterns and from the field. In this paper, an innovative method for analyzing commingled injections and productions from MPZs and ROZs, with application to pattern-based data from four staggered line drive patterns in Wasson Field's Denver Unit, Texas, USA, was developed. Decline curve and ratio-trend methods were used as means of history-matching and forecasting. Cumulative production-time and cumulative production-rate data for oil, gas, and water, as well as water-oil ratio (WOR) and gas-oil ratio (GOR), were analyzed along with injection data for time intervals covering major injection events in MPZ, or MPZ and ROZ combined. A combined analysis enabled inference of allocation of fluids into different zones during WAG (water alternating gas) injection and thereby estimation of CO 2 storage, utilization, and retention in different zones as a function of total injection. Results show that ROZs generally present higher CO 2 sequestration potential compared to MPZs, and a comparable incremental oil recovery factor of ∼20%, on average. Results based on ratio analysis further show that while the WOR trend of the pattern production is mostly dominated and controlled by ROZ, GOR is controlled by both intervals. Although the method relying on decline curves and the approach used in zonal fluid allocations are subject to their limitations, this study presents a practical and innovative well-pattern-based method to infer and forecast CO 2 sequestration and oil recovery quantities and fluid ratios from MPZs and ROZs in commingled operations and highlight the added potential offered by ROZs.

Texas↗

USGS2026

Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations

Recently, the demand for battery-grade lithium has substantially increased, largely due to electrification of the transportation sector. The search for new lithium sources has turned to produced waters (frequently brines), a large-volume wastewater by-product of oil and gas extraction. Geochemical analysis indicates the presence of varying concentrations of lithium from produced water samples collected across the United States and represented in the U.S. Geological Survey’s National Produced Water Geochemical Database, as well as mixtures of Marcellus Shale produced water included in the Pennsylvania Department of Environmental Protection’s Oil and Gas Well Waste Reports. We first examined whether the geochemical signature of the lithium-bearing produced waters is sufficiently distinct so that machine learning (ML) can be used to correctly classify samples to the formation of origin. The produced water sample data used to assess classification accuracy were from the Marcellus Shale, Utica Shale and Point Pleasant Formation (Utica), and Smackover Formation oil and gas wells. Further, we evaluated the potential for ML to accurately classify Marcellus Shale produced water spatially (i.e., northeast versus southwest Pennsylvania). We then investigated whether ML algorithms applied to a suite of geochemical concentration data (i.e. Ba, Br, Cl, K, Mg, Sr) may be used to predict the lithium concentration of an unknown sample. Finally, we applied an estimated economic lithium grade cutoff of 150 milligrams per liter (mg/l) and assessed the utility of ML to predict whether a produced water sample would fall above or below the grade cutoff based on the suite of geochemical parameters. Four machine learning algorithms—Random Forest (RF), Gradient Boosting Trees (GBT), Extreme Boosting (XGBoost), and Deep Neural Networks (DNN) were assessed. This study successfully demonstrates that all four machine learning methods can precisely and accurately estimate lithium concentrations and geologic formation classification. The products of this study contribute to the growing body of knowledge aimed at expanding the lithium resource base within the United States.

Alabama, Arkansas, Florida, Georgia, Louisiana, Mi↗

USGS2025

Review and synthesis of the applications of machine learning to coalbed methane recovery

Over the last 30 years, a substantial literature has evolved on the use of machine learning (ML) to assess, predict, and improve the efficiency of coalbed methane (CBM) recovery. In the United States, the production of CBM declined as shale gas production matured, but CBM continues to be an important energy resource in other parts of the world. ML applications that have the potential to improve CBM reservoir management and production forecasts, and to increase exploration and operational efficiency, are still of significant interest. The integration of geostatistical techniques into the CBM ML applications has been largely absent but represents an opportunity for improvement. The literature demonstrates the widespread interest in, and applicability of, ML algorithms applied to CBM problems, and that they continue to result in improvements in predictive performance. However, (1) much of the research is more academic than operational, (2) many results are based on simulations, or small or proprietary datasets, (3) ML performance information can be inconsistent and sometimes entirely omitted, (4) most methodologies are unique to the specific CBM situation and likely not generalizable, (5) no standard data repositories are available to directly compare the performance of competing algorithms, and (6) the spatial component is often omitted. Finally, relatively new ML protocols involving causality analysis and reinforced learning, as well as hybrid workflows combining both supervised and unsupervised learning, are anticipated to dominate the future investigations. Integration of geostatistical and geospatial analysis with ML should enhance performance.

Book chapter↗

USGS2025

Machine learning provides reconnaissance-type estimates of carbon dioxide storage resources in oil and gas reservoirs

Oil and gas reservoirs represent suitable containers to sequester carbon dioxide (CO 2 ) in a supercritical state because they are accessible, reservoir properties are known, and they previously contained stored buoyant fluids. However, planners must quantify the relative magnitude of the CO 2 storage resource in these reservoirs to formulate a comprehensive strategy for CO 2 mitigation. Even reconnaissance-type estimates of CO 2 storage resources of known oil and gas reservoirs may require complicated calculations involving 1) estimates of recoverable oil and gas, 2) reservoir properties (depth, temperature, pressure, etc.), and 3) the physical qualities of the retained fluids. We demonstrate the application of machine learning (ML) algorithms to bypass these computations to yield more rapid estimates of CO 2 storage resources in reservoirs capable of hosting CO 2 in a supercritical state. ML algorithms are computationally efficient because they do not impose the strong assumptions on the data-generating process that standard statistical or engineering procedures require. Further, ML algorithms can capture highly complex, particularly nonlinear, relationships among predictor variables. We demonstrate the application of four different ML algorithms using data from onshore and offshore oil and gas reservoirs in Europe, and show they perform well when predictions are compared to engineering estimates. The proposed methods and models provide an effective and novel way to more rapidly and directly determine the subsurface CO 2 storage capacity of oil and gas reservoirs around the world, information that operators, researchers, and policymakers alike require to meet energy transition and decarbonization goals.

Frontiers in Enviornmental Science↗

USGS2024

Machine learning approaches to identify lithium concentration in petroleum produced waters

Prices for battery-grade lithium have increased substantially since 2020, which is propelling the search for additional sources of this important element. Battery-grade lithium is predominately recovered from continental brines. Most crude oil and natural gas wells recover briny formation water, which may represent an additional source. Chemical analysis of these waters has been shown to indicate the presence of varying concentrations of lithium and related elements. This paper briefly reviews developments and literature supporting the presence of lithium in petroleum reservoir brines. It also describes the coverage and distribution of lithium data analyses in the United States Geological Survey National Produced Waters Geochemical Database (PWGD). It then addresses the question as to whether a lithium concentration can be accurately predicted using constituents of ion chemistry in produced brines from specific geologic formations. Four machine learning algorithms are employed to classify the commercial potential of lithium in oil field brines using data from oil wells recovering formation water from the Smackover Formation. The calibrated classification models are further applied to new (out-of-sample) data from the Marcellus Formation in the Appalachian Basin. Among the approaches considered, the predictive performance and wider applicability of the gradient boosted tree and the deep neural network models are determined to be the most promising. Finally, we discuss how the calibrated models could be applied to assure the quality of the data reported from chemical laboratory analysis and for imputation when lithium values are missing.

Mineral Economics↗

USGS2022

Decision analysis and CO2–Enhanced oil recovery development strategies

This paper analyzes the relationship between actual reservoir conditions and predicted measures of performance of carbon dioxide enhanced oil recovery (CO 2 –EOR) programs. It then shows how CO 2 –EOR operators might maximize the value of their projects by approaching implementation using a “flexible selective” pattern development strategy, where the CO 2 –EOR program patterns are selectively developed based on site-specific reservoir properties. It also analyzes performance measures and economic consequences of utilizing a continuous CO 2 injection strategy intended to maximize CO 2 retention for a defined time period. “Net CO 2 utilization,” calculated as difference between the volumes of CO 2 injected and CO 2 recovered in the production stream divided by the oil produced, is a standard measure of CO 2 –EOR carbon utilization, but it can be a misleading predictor of the actual CO 2 retained in the reservoir. Asset value can be added to a CO 2 –EOR project by recognizing effects of variations in reservoir parameter values and basing incremental development decisions on those data. For policy analysts, the consequences of ignoring geologic variability within a reservoir that is a candidate for CO 2 –EOR will likely be to substantially overestimate predicted adoption of CO 2 –EOR in response to economic incentives. This result holds true whether the CO 2 –EOR program objective is to maximize net value by maximizing oil production or maximize CO 2 storage with oil recovery.

Natural Resources Research↗

USGS2022

Assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources of the United States

In 2020, the U.S. Geological Survey (USGS) completed a probabilistic assessment of the volume of technically recoverable oil resources that could be produced using current carbon dioxide enhanced oil recovery (CO2-EOR) technologies in amenable conventional oil reservoirs underlying onshore and State-owned offshore waters (herein after, onshore and State waters areas) of the conterminous United States. Our assessment also includes estimates of the mass of CO2 that could be stored (retained) in the assessed oil reservoirs following the application of the CO2-EOR process. The USGS assessment team evaluated more than 3,500 oil reservoirs that were amenable to miscible CO2 injection. The assessed reservoirs are located in 185 plays previously defined by a USGS National Oil and Gas Assessment in 33 petroleum provinces of seven national regions. The team estimated that the total technically recoverable oil resulting from the application of the CO2-EOR process ranges from approximately 25,000 million barrels (MMbbl) at the P5 probability percentile to as much as 32,000 MMbbl at the P95 probability percentile, with a mean of 29,000 MMbbl. The associated CO2 retention ranges from approximately 7,400 million metric tons (Mt) at the P5 percentile to as much as 9,500 Mt at the P95 percentile, with a mean of 8,400 Mt.

conterminous United States↗

USGS2016

A strategy for low cost development of incremental oil in legacy reservoirs

The precipitous decline in oil prices during 2015 has forced operators to search for ways to develop low-cost and low-risk oil reserves. This study examines strategies to low cost development of legacy reservoirs, particularly those which have already implemented a carbon dioxide enhanced oil recovery (CO 2 EOR) program. Initially the study examines the occurrence and nature of the distribution of the oil resources that are targets for miscible and near-miscible CO 2 EOR programs. The analysis then examines determinants of technical recovery through the analysis of representative clastic and carbonate reservoirs. The economic analysis focusses on delineating the dominant components of investment and operational costs. The concluding sections describe options to maximize the value of assets that the operator of such a legacy reservoir may have that include incremental expansion within the same producing zone and to producing zones that are laterally or stratigraphically near main producing zones. The analysis identified the CO 2 recycle plant as the dominant investment cost item and purchased CO 2 and liquids management as a dominant operational cost items. Strategies to utilize recycle plants for processing CO 2 from multiple producing zones and multiple reservoir units can significantly reduce costs. Industrial sources for CO 2 should be investigated as a possibly less costly way of meeting EOR requirements. Implementation of tapered water alternating gas injection schemes can partially mitigate increases in fluid lifting costs.

Conference Paper↗

USGS2016

Play-level distributions of estimates of recovery factors for a miscible carbon dioxide enhanced oil recovery method used in oil reservoirs in the conterminous United States

In a U.S. Geological Survey (USGS) study, recovery-factor estimates were calculated by using a publicly available reservoir simulator (CO 2 Prophet) to estimate how much oil might be recovered with the application of a miscible carbon dioxide (CO 2 ) enhanced oil recovery (EOR) method to technically screened oil reservoirs located in onshore and State offshore areas in the conterminous United States. A recovery factor represents the percentage of an oil reservoir’s original oil in place estimated to be recoverable by the application of a miscible CO 2 -EOR method. The USGS estimates were calculated for 2,018 clastic and 1,681 carbonate candidate reservoirs in the “Significant Oil and Gas Fields of the United States Database” prepared by Nehring Associates, Inc. (2012). This report presents distributions of estimated recovery factors organized by plays in seven U.S. regions. The distributional parameters for plays containing at least three candidate reservoirs are presented in tables, and parameters for plays containing at least six candidate reservoirs are presented in boxplots. Over all the reservoirs evaluated, 90 percent of the recovery-factor estimates for clastic reservoirs fell within the range from 8.7 to 16.2 percent, and the median value of the distribution was 9.5 percent. Similarly, 90 percent of the recovery-factor estimates for carbonate reservoirs were within the range from 11.8 to 27.5 percent, and the median value of the distribution was 13.6 percent. Both distributions were right skewed. The retention factor is the percentage of injected CO 2 that is naturally retained in the reservoir. Retention factors were also estimated in this study. For clastic reservoirs, 90 percent of the estimated retention factors were between 21.7 and 32.1 percent, and for carbonate reservoirs, 90 percent were between 23.7 and 38.2 percent. The respective median values were 22.9 for clastic reservoirs and 26.1 for carbonate reservoirs. Both distributions were right skewed. The recovery and retention factors that were calculated are consistent with the corresponding factors reported in the literature.

Open-File Report↗

USGS2014

Appalachian basin bituminous coal: sulfur content and potential sulfur dioxide emissions of coal mined for electrical power generation

Data from 157 counties in the Appalachian basin of average sulfur content of coal mined for electrical power generation from 1983 through 2005 show a general decrease in the number of counties where coal mining has occurred and a decrease in the number of counties where higher sulfur coals (>2 percent sulfur) were mined. Calculated potential SO 2 emissions (assuming no post-combustion SO 2 removal) show a corresponding decrease over the same period of time.

Appalachian basin↗

USGS2013

Commercial possibilities for stranded conventional gas from Alaska's North Slope

Stranded gas resources are defined for this study as gas resources in discrete accumulations that are not currently commercially producible, or producible at full potential, for either physical or economic reasons. Approximately 35 trillion cubic feet (TCF) of stranded gas was identified on Alaska’s North Slope. The commercialization of this resource requires facilities to transport gas to markets where sales revenue will be sufficient to offset the cost of constructing and operating a gas delivery system. With the advent of the shale gas revolution, plans for a gas pipeline to the conterminous US have been shelved (at least temporarily) and the State and resource owners are considering a liquefied natural gas (LNG) export project that targets Asian markets. This paper focuses on competitive conditions for Asian gas import markets by estimating delivered costs of competing supplies from central Asia, Russia, Indonesia, Malaysia, and Australia in the context of a range of import gas demand projections for the period from 2020 to 2040. These suppliers’ costs are based on the cost of developing, producing, and delivering to markets tranches of the nearly 600 TCF of recoverable gas from their own conventional stranded gas fields. The results of these analyses imply that Alaska’s gas exports to Asia will likely encounter substantial competitive challenges. The sustainability of Asia’s oil-indexed LNG pricing is also discussed in light of a potentially intense level of competition.

Alaska↗

USGS2013

Meeting Asia's future gas import demand with stranded natural gas from central Asia, Russia, Southeast Asia, and Australia

This analysis shows the important contribution that stranded gas from central Asia, Russia, Southeast Asia, and Australia can make in meeting the projected demand for gas imports of China, India, Japan, and South Korea from 2020 to 2040. The estimated delivered costs of pipeline gas from stranded fields in Russia and central Asia at Shanghai, China, are generally less than delivered costs of liquefied natural gas (LNG). Australia and Malaysia are initially the lowest-cost LNG suppliers. In the concluding section, it is argued that Asian LNG demand is price sensitive, and that current Asian LNG pricing procedures are unlikely to be sustainable for gas import demand to attain maximum potential growth. Resource volumes in stranded fields evaluated can nearly meet projected import demands.

SPE Economics & Management↗

USGS2013

Role of stranded gas in increasing global gas supplies

This report synthesizes the findings of three regional studies in order to evaluate, at the global scale, the contribution that stranded gas resources can make to global natural gas supplies. Stranded gas, as defined for this study, is natural gas in discovered conventional gas and oil fields that is currently not commercially producible for either physical or economic reasons. The regional studies evaluated the cost of bringing the large volumes of undeveloped gas in stranded gas fields to selected markets. In particular, stranded gas fields of selected Atlantic Basin countries, north Africa, Russia, and central Asia are screened to determine whether the volumes are sufficient to meet Europe’s increasing demand for gas imports. Stranded gas fields in Russia, central Asia, Southeast Asia, and Australia are also screened to estimate development, production, and transport costs and corresponding gas volumes that could be supplied to Asian markets in China, India, Japan, and South Korea. The data and cost analysis presented here suggest that for the European market and the markets examined in Asia, the development of stranded gas provides a way to meet projected gas import demands for the 2020-to-2040 period. Although this is a reconnaissance-type appraisal, it is based on volumes of gas that are associated with individual identified fields. Individual field data were carefully examined. Some fields were not evaluated because current technology was insufficient or it appeared the gas was likely to be held off the export market. Most of the evaluated stranded gas can be produced and delivered to markets at costs comparable to historical prices. Moreover, the associated volumes of gas are sufficient to provide an interim supply while additional technologies are developed to unlock gas diffused in shale and hydrates or while countries transition to making a greater use of renewable energy sources.

Open-File Report↗

USGS2012

Assessment of remaining recoverable oil in selected major oil fields of the San Joaquin Basin, California

The U.S. Geological Survey (USGS) recently completed an estimate of volumes of technically recoverable, conventional oil that could eventually be added to reserves in nine selected major oil fields in the San Joaquin Basin in central California. The mean total volume of potential oil reserves that might be added in the nine fields using improved oil-recovery technologies was estimated to be about 6.5 billion barrels of oil.

California↗

USGS2012

Assessment of remaining recoverable oil in selected major oil fields of the Permian Basin, Texas and New Mexico

The U.S. Geological Survey (USGS) recently completed an estimate of technically recoverable, conventional oil in selected oil fields in the Permian Basin in west Texas and southeastern New Mexico. The mean total volume of potential additional oil resources that might be added using improved oil-recovery technologies was estimated to be about 2.7 billion barrels of oil.

New Mexico;Texas↗

USGS2012

Assessment of potential additions to conventional oil and gas resources of the world (outside the United States) from reserve growth, 2012

The U.S. Geological Survey estimated volumes of technically recoverable, conventional petroleum resources resulting from reserve growth for discovered fields outside the United States that have reported in-place oil and gas volumes of 500 million barrels of oil equivalent or greater. The mean volumes were estimated at 665 billion barrels of crude oil, 1,429 trillion cubic feet of natural gas, and 16 billion barrels of natural gas liquids. These volumes constitute a significant portion of the world's oil and gas resources.

Fact Sheet↗

USGS2008

Database for Assessment Unit-Scale Analogs (Exclusive of the United States)

This publication presents a database of geologic analogs useful for the assessment of undiscovered oil and gas resources. Particularly in frontier areas, where few oil and gas fields have been discovered, assessment methods such as discovery process models may not be usable. In such cases, comparison of the assessment area to geologically similar but more maturely explored areas may be more appropriate. This analog database consists of 246 assessment units, based on the U.S. Geological Survey 2000 World Petroleum Assessment. Besides geologic data to facilitate comparisons, the database includes data pertaining to numbers and sizes of oil and gas fields and the properties of their produced fluids.

Open-File Report↗

USGS2007

Regional impact of water resource investments in an developing area

Regional development and industrialization patterns are investigated and related via regression analysis to water resource investments for the island of Puerto Rico. Although results of this study indicate such investments have little immediate or short‐term impact, significant relationships and variations in regional responses appear over longer time periods. This is shown by applying a variation of Zellner's method of performing seemingly unrelated regressions jointly. By this method, subsets of parameter coefficients of specific economic variables were restricted across regional equations while unrestricted coefficients were interpreted as explaining systematic regional variations in response to public investment. Regional differences, obtained by using this method, are frequently neglected when simply examining the overall development process. Among the more interesting results in terms of policy implications is the apparent significant relationship, over the period considered, between changes in the distribution of income and the pattern of water resource development.

Journal of the American Water Resources Associatio↗