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27 records · Page 2Linked 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↗
USGS1987

Pressure sensitivity of low permeability sandstones

Detailed core analysis has been carried out on 32 tight sandstones with permeabilities ranging over four orders of magnitude (0.0002 to 4.8 mD at 5000 psi confining pressure). Relationships between gas permeability and net confining pressure were measured for cycles of loading and unloading. For some samples, permeabilities were measured both along and across bedding planes. Large variations in stress sensitivity of permeability were observed from one sample to another. The ratio of permeability at a nominal confining pressure of 500 psi to that at 5000 psi was used to define a stress sensitivity ratio. For a given sample, confining pressure vs permeability followed a linear log-log relationship, the slope of which provided an index of pressure sensitivity. This index, as obtained for first unloading data, was used in testing relationships between stress sensitivity and other measured rock properties. Pressure sensitivity tended to increase with increase in carbonate content and depth, and with decrease in porosity, permeability and sodium feldspar. However, scatter in these relationships increased as permeability decreased. Tests for correlations between pressure sensitivity and various linear combinations of variables are reported. Details of pore structure related to diagenetic changes appears to be of much greater significance to pressure sensitivity than mineral composition.

Journal of Petroleum Science and Engineering↗
USGS2006

Coalbed Methane Extraction and Soil Suitability Concerns in the Powder River Basin, Montana and Wyoming

The Powder River Basin is located in northeastern Wyoming and southeastern Montana. It is an area of approximately 55,000 square kilometers. Extraction of methane gas from the coal seams that underlie the Powder River Basin began in Wyoming in the late 1980s and in Montana in the late 1990s. About 100-200 barrels of co-produced water per day are being extracted from each active well in the Powder River Basin, which comes to over 1.5 million barrels of water per day for all the active coalbed methane wells in the Basin. Lab testing indicates that Powder River Basin co-produced water is potable but is high in sodium and other salts, especially in the western and northern parts of the Powder River Basin. Common water management strategies include discharge of co-produced water into drainages, stock ponds, evaporation ponds, or infiltration ponds; treatment to remove sodium; or application of the water directly on the land surface via irrigation equipment or atomizers. Problems may arise because much of the Powder River Basin contains soils with high amounts of swelling clays. As part of the USGS Rocky Mountain Geographic Science Center's hyperspectral research program, researchers are investigating whether hyperspectral remote sensing data can be beneficial in locating areas of swelling clays. Using detailed hyperspectral data collected over parts of the Powder River Basin and applying our knowledge of how the clays of interest reflect energy, we will attempt to identify and map areas of swelling clays. If successful, such information will be useful to resource and land managers.

Fact Sheet↗
USGS2007

Characterization of methane hydrate host sediments using synchrotron-computed microtomography (CMT)

The hydrate-sediment interaction is an important aspect of gas hydrate studies that needs further examination. We describe here the applicability of the computed microtomography (CMT) technique that utilizes an intense X-ray synchrotron source to characterize sediment samples, two at various depths from the Blake Ridge area (a well-known hydrate-prone region) and one from Georges Bank, that once contained methane trapped as hydrates. Detailed results of the tomographic analysis performed on the deepest sample (667??m) from Blake Ridge are presented as 2-D and 3-D images which show several mineral constituents, the internal grain/pore microstructure, and, following segmentation into pore and grain space, a visualization of the connecting pathways through the pore-space of the sediment. Various parameters obtained from the analysis of the CMT data are presented for all three sediment samples. The micro-scale porosity values showed decreasing trend with increasing depth for all three samples that is consistent with the previously reported bulk porosity data. The 3-D morphology, pore-space pathways, porosity, and permeability values are also reported for all three samples. The application of CMT is now being expanded to the laboratory-formed samples of hydrate in sediments as well as field samples of methane hydrate bearing sediments.

Blake Ridge, Georges Bank↗
USGS2019

A fuzzy logic approach for estimating recovery factors of miscible CO2-EOR projects in the United States

"Recovery factor (RF) is one of the most fundamental parameters that define engineering and economical success of any operational phase in oil and gas production. The effectiveness of the operation, e.g. CO2-EOR (enhanced oil recovery with carbon dioxide injection), is usually defined by multiplying the resultant recovery factor by the original oil in place. Moreover, investment decisions for such engineering projects are also performed based on predicted recovery factors. Despite its importance, though, it is not easy to predict recovery factors as they are affected by many factors including the type of the recovery process, reservoir type, fluid properties, reservoir heterogeneity, depth, thickness, to name a few. The usual method of estimating recovery factors is laboratory experiments or numerical modeling, each of which has their own limitations due to data requirements, boundary conditions and scale effects. In this work, a fuzzy inference system approach has been adopted to predict miscible CO2-EOR recovery factors of the major field applications in the United States with the premise that it can be used as a guidance tool for making decisions based on different inputs. The fuzzy system was build using a Mamdani-type fuzzy logic inference engine, and by using reservoir data compiled from different sources as inputs and recovery factors gathered from a literature survey. Due to the limited number of field cases that could be used for this purpose, 24 sets of applications were included in the study. Selected input variables were water saturation after waterflood (Sorw), well spacing, porosity, permeability, depth, net pay thickness, initial pressure, API gravity of oil, hydrocarbon pore volume CO2 injected, and reservoir lithology. The type of membership functions were decided based on the system’s predictive performance. The model showed reasonable predictive capability for the field observations of recovery factor despite the complexity of this parameter. In addition, since the fuzzy solution was multi-dimensional due to multiple inputs, system behavior was used to demonstrate response of miscible CO2-EOR recovery factor to different inputs. "

Journal of Petroleum Science and Engineering↗
USGS2009

Geomechanical response of permafrost-associated hydrate deposits to depressurization-induced gas production

In this simulation study, we analyzed the geomechanical response during depressurization production from two known hydrate-bearing permafrost deposits: the Mallik (Northwest Territories, Canada) deposit and Mount Elbert (Alaska, USA) deposit. Gas was produced from these deposits at constant pressure using horizontal wells placed at the top of a hydrate layer (HL), located at a depth of about 900??m at the Mallik site and 600??m at the Mount Elbert site. The simulation results show that general thermodynamic and geomechanical responses are similar for the two sites, but with substantially higher production and more intensive geomechanical responses at the deeper Mallik deposit. The depressurization-induced dissociation begins at the well bore and then spreads laterally, mainly along the top of the HL. The depressurization results in an increased shear stress within the body of the receding hydrate and causes a vertical compaction of the reservoir. However, its effects are partially mitigated by the relatively stiff permafrost overburden, and compaction of the HL is limited to less than 0.4%. The increased shear stress may lead to shear failure in the hydrate-free zone bounded by the HL overburden and the downward-receding upper dissociation interface. This zone undergoes complete hydrate dissociation, and the cohesive strength of the sediment is low. We determined that the likelihood of shear failure depends on the initial stress state as well as on the geomechanical properties of the reservoir. The Poisson's ratio of the hydrate-bearing formation is a particularly important parameter that determines whether the evolution of the reservoir stresses will increase or decrease the likelihood of shear failure.

Journal of Petroleum Science and Engineering↗
USGS2008

Towards monitoring land-cover and land-use changes at a global scale: the global land survey 2005

Land cover is a critical component of the Earth system, infl uencing land-atmosphere interactions, greenhouse gas fl uxes, ecosystem health, and availability of food, fi ber, and energy for human populations. The recent Integrated Global Observations of Land (IGOL) report calls for the generation of maps documenting global land cover at resolutions between 10m and 30m at least every fi ve years (Townshend et al., in press). Moreover, despite 35 years of Landsat observations, there has not been a unifi ed global analysis of land-cover trends nor has there been a global assessment of land-cover change at Landsat-like resolution. Since the 1990s, the National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS) have supported development of data sets based on global Landsat observations (Tucker et al., 2004). These land survey data sets, usually referred to as GeoCover ™, provide global, orthorectifi ed, typically cloud-free Landsat imagery centered on the years 1975, 1990, and 2000, with a preference for leaf-on conditions. Collectively, these data sets provided a consistent set of observations to assess land-cover changes at a decadal scale. These data are freely available via the Internet from the USGS Center for Earth Resources Observation and Science (EROS) (see http://earthexplorer.usgs.gov or http://glovis.usgs.gov). This has resulted in unprecedented downloads of data, which are widely used in scientifi c studies of land-cover change (e.g., Boone et al., 2007; Harris et al., 2005; Hilbert, 2006; Huang et al. 2007; Jantz et al., 2005, Kim et al., 2007; Leimgruber, 2005; Masek et al., 2006). NASA and USGS are continuing to support land-cover change research through the development of GLS2005 - an additional global Landsat assessment circa 20051 . Going beyond the earlier initiatives, this data set will establish a baseline for monitoring changes on a 5-year interval and will pave the way toward continuous global land-cover monitoring at Landsat-like resolution in the next decade.

Photogrammetric Engineering and Remote Sensing↗
USGS2007

Surficial sediment character of the New York-New Jersey offshore continental shelf region: A GIS compilation

Broad continental shelf regions such as the New York Bight are the product of a complex geologic history and dynamic oceanographic processes, dominated by the Holocene marine transgression (>100 m sea-level rise) following the end of the last Pleistocene ice advance ~ 20,000 years ago. The area of the U.S. Exclusive Economic Zone (U.S. EEZ) territory, extending 200 nautical miles seaward from the coast, is larger than the continental U.S. and contains submerged landforms that provide a variety of natural functions and societal benefits, such as: critical habitats for fisheries, ship navigation and homeland security, and engineering activities (i.e. oil and gas platforms, pipeline and cable routes, potential wind-energy-generation sites). Some parts of the continental margins, particularly inner-continental shelf regions, also contain unconsolidated hard-mineral deposits such as sand and gravel that are regarded as potential aggregate resources to meet or augment needs not met by onshore deposits (Williams, 1992). The present distribution of surficial sediment off the northeastern United States is shaped from the deposits left by the last glaciation and reflects the cumulative effects of sediment erosion, transport, sorting, and deposition by storm and tidal processes during the Holocene rise in sea level. As a result, the sediments on the sea floor represent both an historical record of former conditions and a guide to possible future sedimentary environments. The U.S. Geological Survey (USGS) through the Coastal and Marine Geology Program, in cooperation with the University of Colorado and other partners, has compiled extant sediment character and textural data as well as other geologic information on the sea floor from all regions around the U.S. into the usSEABED data system (Reid and others, 2005; Buczkowski and others, 2006; Reid and others, 2006). The usSEABED system, which contains information on sediment grain size and lithology for more than 340,500 stations within the U.S. EEZ. has been developed and populated with data as part of the USGS Marine Aggregate Resources and Processes and the National Benthic Habitats projects in order to provide the base-line data needed to update the current maps of offshore surficial geologic character and sediment distribution. The maps are also used to characterize benthic sea floor environments important for marine ecosystems. U.S. Geological Survey, Data Series 118 (Reid and others, 2005), of the usSEABED data release series, represents the combined efforts of the USGS and several other government agencies to provide a unified resource for accessing and preserving records of U.S. east coast sea floor geologic information and sediment texture data. For this present report, we have chosen to focus on the New York-New Jersey region, an area that has been intensely studied by the USGS for many years to address many complex issues. This report illustrates the uses of the usSEABED database for GIS applications, while offering additional insight into the resources and data available from the USGS and its collaborative institutions. This report is based on data contained in U.S. Geological Survey Data Series 118 (Reid and others, 2005) and shows an assortment of example GIS products that are possible using usSEABED. All data are intended to be GIS-ready and should not require any additional cleanup, formatting, or renaming of fields in order to use the data in a Geographic Information System. This project employs the Environmental Systems Research Institute's (ESRI) ArcView™ software. Many of these maps were made as part of the ongoing USGS study to assess marine aggregate resources offshore New York and New Jersey, but these maps can serve many other purposes. The marine science community, educators, students and others are encouraged to use these data to generate GIS products for their own purposes. The objectives of the Marine Aggregate Resources and Processes project are to produce a series of new geologic maps and reports of the sea floor that will provide scientific insights into the character and geologic development of U.S. continental margins and to use these maps and information to assess the potential availability of offshore sand and gravel resources. The mapping and aggregate resource assessments are being conducted on a national scale using the usSEABED data base as described in Williams and others (2003). Potential uses for these data include: (1) defining the geological variability of the sea floor in relation to benthic habitat diversity; (2) improving our understanding of the processes that control the distribution and transport of bottom sediments and benthic habitats; (3) locating aggregate resources for beach nourishment and industrial applications; and (4) providing a detailed geospatial framework for future marine science research, monitoring, and management activities. The initial assessments are in progress for the New York Bight and Louisiana offshore areas.

New Jersey, New York↗
USGS2019

An ANCOVA model for porosity and its uncertainty for oil reservoirs based on TORIS dataset

Porosity is one of the most important parameters to assess in-place oil or gas in reservoirs, and to evaluate recovery from enhanced production operations. Since it is relatively well-established to determine porosity using different laboratory and field methods, its value is usually determined at many locations across a reservoir as part of the common practice to capture reservoir heterogeneity and the variability in values. This suite of measurements and the distribution of values are most valuable for probabilistic reservoir assessments, and for spatial modeling if the exact data locations are known. Despite the importance of individual measurements to set the range of values for probabilistic studies, it is not always possible to access these data due to confidentiality. In most cases, commercial or publicly available databases that assessments may rely on usually report only mean values of porosity, like any other reservoir data, or they may not report a value at all. This makes both quantifying the mean value and the uncertainty around it difficult for probabilistic assessments. In this study, the TORIS (Tertiary Oil Recovery Information System) dataset of the National Petroleum Council and the U.S. Department of Energy was used to model porosity and the uncertainty around predicted values. TORIS is an integrated dataset of production data, reservoir properties, and project databases of crude oil reservoirs in the United States. The model presented in the paper was based on ANCOVA (Analysis of Co-Variance) of data from 1038 reservoirs from the TORIS dataset for porosity prediction, validation and testing for quantitative and qualitative parameters that may be readily available in most cases, and to estimate uncertainty around the mean values. This model also explored association of porosity values to different parameters, and to different depositional systems and diagenetic overprint conditions. Furthermore, an ANN (Artificial Neural Network) model was created to compare the predicted values of both models. Results showed that the ANN model was able to represent more of the variability, however it lacked the insights that might be gained from the ANCOVA model.

Journal of Petroleum Science and Engineering↗