Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Results
Department of Petroleum Engineering, University of Houston, 2. Metarock Laboratories, 3. Department of Earth and Atmospheric Sciences, University of Houston) 16:00-16:30 Break and Walk to Bizzell Museum 16:30-17:30 Tour: History of Science Collections, Bizzell Memorial Library, The University of Oklahoma 17:30-19:00 Networking Reception: Thurman J. White Forum Building
- North America > United States > Texas (0.51)
- North America > United States > Oklahoma (0.44)
- North America > United States > Colorado (0.31)
- Geology > Geological Subdiscipline > Geomechanics (0.76)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.49)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (0.48)
Petroleum Engineering, University of Houston, 2. Metarock Laboratories, 3. Department of Earth and Atmospheric Sciences, University of Houston) 16:00-16:30 Break and Walk to Bizzell Museum 16:30-17:30 Tour: History of Science Collections, Bizzell Memorial Library, The University of Oklahoma 17:30-19:00 Networking Reception: Thurman J. White Forum Building
- Research Report > New Finding (0.93)
- Overview (0.68)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Mineral (0.72)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.68)
- (2 more...)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)
- North America > United States > Texas (1.00)
- Europe (0.93)
- Research Report > New Finding (0.93)
- Overview (0.88)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.68)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.47)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)
Department of Petroleum Engineering, University of Houston, 2. Metarock Laboratories, 3. Department of Earth and Atmospheric Sciences, University of Houston) 16:00-16:30 Break and Walk to Bizzell Museum 16:30-17:30 Tour: History of Science Collections, Bizzell Memorial Library, The University of Oklahoma 17:30-19:00 Networking Reception: Thurman J. White Forum Building
- North America > United States > Texas (0.51)
- North America > United States > Oklahoma (0.43)
- Geology > Geological Subdiscipline > Geomechanics (0.77)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.49)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (0.48)
- North America > United States > Texas (0.51)
- North America > United States > Oklahoma (0.44)
- Geology > Geological Subdiscipline > Geomechanics (0.77)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.49)
Estimating Recovery by Quantifying Mobile Oil and Geochemically Allocating Production in Source Rock Reservoirs
Adams, Jennifer (Stratum Reservoir, Houston) | Flannery, Matt (Stratum Reservoir, Houston) | Ruble, Tim (Stratum Reservoir, Houston) | McCaffrey, Mark A. (Stratum Reservoir, Houston) | Krukowski, Elizabeth (Stratum Reservoir, Houston) | Kolodziejczyk, Daniel (GeoLab Sur S.A., Buenos Aires, Argentina) | Villar, Héctor (GeoLab Sur S.A., Buenos Aires, Argentina)
Abstract Due to highly variable well performance, unconventional reservoir (UR) field development relies heavily on production monitoring to predict total recovery, assess well interference, delineate drained rock volume, and diagnose mechanical issues. Completion design and well spacing decisions depend on accurate recovery estimates from reservoir models, and these can be limited by non-uniqueness in the history matching. Geochemical production allocation can greatly improve operators’ understanding of well performance when integrated with reservoir characterization and in-reservoir P/T monitoring. There are several long-standing challenges in the characterization of UR fluid flow: (i) collecting reservoir samples representative of mobile oil, (ii) accounting for production fractionation over the life of a well, and (iii) determining recoverable original oil in place (OOIP) from contributing zones. Although many metrics and correlations are commonly used, ultimate recovery requires accurate quantification of the provenance of produced fluids and proportion of total OOIP. We have developed a rapid method for quantifying mobile and total oil saturations from water-based mud (WBM) collected, tight cuttings and sidewall core samples using low temperature hydrous pyrolysis (EZ-LTHP). These mobile oils commonly include even the gasoline range compounds, which are the dominant compounds of produced liquids in most mid-continent UR fields, making EZ-LTHP-derived oils representative end-members for geochemical production allocation studies. EUR estimates and production forecasts by zone, are more accurate when calibrated to the mobile oil fraction, rather than to total oil saturation. EZ-LTHP provides this step-change by quantifying the mobile oil fraction in WBM cuttings and, when paired with reservoir volumetrics, allows for better reservoir model calibration and field management. Other industry techniques, such as solvent extraction and vaporization, suffer from the same limitations as log-derived values which are known to overestimate mobile oil in kerogen-rich intervals by incorrectly including kerogen-bound immobile oil. In this paper, we present quantified mobile oil recovery estimates based on integrated geochemical allocation studies from the Vaca Muerta, Neuquén basin, and the Niobrara, Denver basin. In the Vaca Muerta play (Argentina), the organic-rich Cocina and Organico intervals in the Vaca Muerta expelled liquid into intervening good quality reservoir lithologies. However, liquids dominantly are produced from the most organic-rich zones, with evidence of a larger drained rock volume (DRV) during early production. Gas and oil allocations show different DRVs explained by fluid mobility. The Montney play (Canada) shows contribution of liquid from non-target zones. Interbedded zones of indigenous Montney oil mixed with migrated more mature fluid - and major discontinuities in mud gas isotopes - document minimal vertical mixing. Horizontal wells produce gas and oil dominantly from better-quality reservoirs regardless of landing zone, with natural gas bypassing low permeability zones. Accurate estimations of out-of-zone contributions therefore require cuttings/core-based geochemical allocation. A subset of these wells requires additional consideration of production fractionation.
- North America > United States > Texas (1.00)
- North America > United States > Colorado (1.00)
- North America > Canada > British Columbia (1.00)
- (3 more...)
- Geology > Geological Subdiscipline > Geochemistry (1.00)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.35)
- South America > Argentina > Patagonia > Neuquén > Neuquen Basin > Vaca Muerta Shale Formation (0.99)
- North America > United States > Wyoming > Powder River Basin (0.99)
- North America > United States > Wyoming > Laramie Basin > Niobrara Formation (0.99)
- (59 more...)
Abstract As the petroleum industry builds long-term production histories in major liquid-rich unconventional resource (UCR) plays, development geologists and engineers have realized that the production gas oil ratio, petroleum type, and ultimate recoveries do not always match the predictive petroleum system models. Early studies suggested that the UCR petroleum systems require neither traditional petroleum traps nor major migration systems but an organic-rich source within optimal maturity window. Possible explanations for these production discrepancies that were not fully characterized in the initial models include uncertainties in source rock characteristics, primary migration fractionation, fractionation related to storage, and production fractionation. Long-term empirical observations suggest that off-structure migration contribution, trapping mechanisms, and reservoir phase (single versus two) play an important role in the liquid-rich UCR production. If the liquid-rich UCR petroleum system is a well-behaved predominantly local charge system, then the generation product can be estimated with an understanding of the local organic matter type and in situ level of maturity. However, if the UCR play is hybrid with significant migrated down-dip charge contribution, then a more complicated work program will be required to estimate well rates and volumes. The liquid-rich UCR play evaluation should reflect these additional factors, which can greatly impact surface production rate and liquid recovery.
- North America > United States > North Dakota (1.00)
- North America > United States > Texas (0.94)
- Geology > Petroleum Play Type > Unconventional Play (1.00)
- Geology > Geological Subdiscipline > Geochemistry (1.00)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (1.00)
- (2 more...)
- North America > United States > Wyoming > Laramie Basin > Niobrara Formation (0.99)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Eagle Ford Shale Formation (0.99)
- (68 more...)
On the Path to Least Principal Stress Prediction: Quantifying the Impact of Borehole Logs on the Prediction Model
Dvory, N. Z. (Civil & Environmental Engineering & Energy and Geoscience Institute) | Smith, P. J. (Chemical Engineering) | McCormack, K. L. (Energy and Geoscience Institute) | Esser, R. (Energy and Geoscience Institute) | McPherson, B. J. (Civil & Environmental Engineering & Energy and Geoscience Institute)
ABSTRACT Knowledge of the minimum horizontal principal stress (Shmin) is essential for geo-energy utilization. Shmin direct measurements are costly, involve high-risk operations, and provide only discrete values of the required quantity. Other methods were developed to interpret a continuous stress sequence from sonic logs. These methods usually require some ‘horizontal tectonic stress’ correction for calibration and rarely match sections characterized by stress profiling due to viscoelastic stress relaxation. Recently, several studies have tried to predict the stress profile by an empirical correlation corresponding to an average strain rate through geologic time or by using machine learning technologies. Here, we used the Bayesian Physics-Based Machine Learning framework to identify the relationships among the viscoelastic parameter distributions and to quantify statistical uncertainty. More specifically, we used well logs data and ISIP measurements to quantify the uncertainty of the viscoelastic-dependent stress profile model. Our results show that the linear regression approach suffers from higher uncertainty, and the Gaussian process regression Shmin prediction shows a relatively smaller uncertainty distribution. Extracting the lithology logs from the prediction model improves each method's uncertainty distribution. We show that the density and the porosity logs have a superior correlation to the viscoplastic stress relaxation behavior. INTRODUCTION Comprehensive recognition of the least principal stress is essential for economic multistage hydraulic fracturing stimulation design. It is well established that hydraulic fractures propagate perpendicular to the least principal stress and that the stress profile prominent the hydraulic fractures generation in both the lateral and horizontal direction (Fisher et al., 2012; Hubbert and Willis, 1957; Kohli et al., 2022; Valkó and Economides, 1995; Zoback et al., 2022)c. In other words, the stress layering could act as a ‘frac barrier’ that limits fracture development in discrete directions and promotes progress in different directions (Singh et al., 2019). Detailed knowledge of the least principal stress profile is significant for hydraulic fracture growth assessment, proppants technology optimization, and efficient landing zone detection (Pudugramam et al., 2022). Traditionally, these considerations were aligned with the oil and gas industry. Still, today, they have substantial implications for enhanced geothermal system development, carbon storage integrity, and in a broader sense, a safe path for a carbon neutrality economy.
- Geology > Rock Type (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- North America > United States > Wyoming > DJ (Denver-Julesburg) Basin > Niobrara Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- North America > United States > Texas > Permian Basin > Midland Basin (0.99)
- (13 more...)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Abstract Traditional assessment of "free" oil-in-place via programmed pyrolysis can be challenged due to false positives (OBM invasion/interference), non-unique signatures (i.e. low temperature shoulders) or biased from sample handling procedures (unpreserved or ‘vintage’ core/cuttings). Additionally, estimated oil saturations and volumetrics of producible hydrocarbons from core material may be underrepresented if certain extraction practices are used. Here, we utilize an advanced thermal extraction technique that is tailored to optimize mobile bulk volumes of oil within a target horizon. Further geochemical assessment of the collected thermal extract aids in additional understanding of the hydrocarbons in place (source/maturity/migration). Retort oil evaluation via fine-tuned thermal extraction techniques can significantly increase estimated oil saturations and oil in place calculations. It’s important to note that the selected retort temperature regime for Formation X in Basin A may very well be different than Formation Y in Basin B due to variations in source rock (kerogen) type, thermal maturity and/or a number of other factors. Therefore, a tailored experimental set up for a specific formation of interest would provide the dataset with the highest confidence for saturation and producibility evaluations. Introduction When evaluating the geochemical makeup of hydrocarbons within a rock (core/SWC/cuttings/etc.), it is important to understand the effects that the chosen extraction technique has on the fluid that is being extracted. For instance, when using excess solvent in a Soxhlet or Dean Stark apparatus, the solvent must be evaporated off to concentrate the extract before analysis. During that evaporation phase, light- to mid-chain hydrocarbons (typically up to ∼nC15), which were potentially present in the parent rock sample, would also be lost before analysis even begins. If extraction of the heavier hydrocarbons was the goal, a solvent-based approach is appropriate but if light- to mid-chain hydrocarbons are dominant or if accurate original oil in place (OOIP) estimations are needed, then the loss of such hydrocarbons should be avoided.
- North America > United States > Texas (1.00)
- North America > United States > Colorado (0.94)
- Geology > Geological Subdiscipline > Geochemistry (1.00)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (0.34)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.75)
- North America > United States > Wyoming > Uinta Basin (0.99)
- North America > United States > Wyoming > Laramie Basin > Niobrara Formation (0.99)
- North America > United States > Utah > Uinta Basin (0.99)
- (10 more...)
High-Resolution Core Study Relating Chemofacies to Reservoir Quality: Examples from the Permian Wolfcamp XY Formation, Delaware Basin, Texas
Putri, Shaskia Herida (Colorado School of Mines) | Jobe, Zane (Colorado School of Mines) | Wood, Lesli J. (Colorado School of Mines) | Melick, Jesse (Colorado School of Mines) | French, Marsha (Colorado School of Mines) | Pfaff, Katharina (Colorado School of Mines)
Abstract The Wolfcamp and Bone Spring Formations are comprised of siliciclastic and carbonate sediment gravity flow deposits, including turbidites and debrites that were sourced from multiple uplifted areas and deposited in the Delaware Basin, Texas during the early-middle Permian (Early Leonardian, ∼285 Ma). Deep-water lobe deposits in these formations are primary unconventional reservoir targets in the North-central Delaware Basin of Texas. Despite numerous recent reservoir characterization studies in this area, integrated multi-scale core-based studies relating to reservoir quality are sparsely published. This research aims to provide a workflow to better predict source rock and reservoir distribution by integrating geochemistry and petrophysical data from this deep-water depositional system. Using high-resolution (1 cm), continuous X-ray fluorescence (XRF) data from 218 feet of core from the Wolfcamp XY interval, this study focuses on the controls that depositional processes and diagenesis impart on chemofacies. Unsupervised k-means clustering and principal component analysis on 17 XRF-derived elemental concentrations derive four chemofacies that characterize geochemical heterogeneity: (1) calcareous, (2) oxic-suboxic argillaceous, (3) anoxic argillaceous, and (4) detrital mudrock. Results indicate that vertical, event-bed-scale variations in XRF-based chemofacies accurately represent depositional facies changes, often matching cm-by-cm the human-described lithofacies. This research demonstrates the relationship of chemofacies to petrophysical properties (e.g., total organic carbon, porosity, and water saturation), which can be used for log-based reservoir prediction of the Wolfcamp and Bone Spring Formations in the Permian Basin, as well as for other mixed clastic-carbonate deep-water reservoirs around the world. Introduction Mixed siliciclastic-carbonate mudstone unconventional reservoirs contain complex sub-well-log-scale heterogeneity in mineralogical composition due to depositional process variability (Lazar et al., 2015; Comerio et al., 2020, Kvale et al., 2020; Ochoa et al., 2022). Moreover, these lithofacies are organized as repetitive meter-scale sedimentation units that are linked to depositional-element architectural and sequence stratigraphic evolution (Thompson et al., 2018; Zhang et al., 2021). High-resolution core studies can help to capture fine-scale depositional units and diagenetic process (Baumgardner et al., 2014; Ochoa et al., 2022). Because it is difficult to visually observe the heterogeneity in mixed siliciclastic-carbonate mudstone cores, it is crucial to integrate quantitative petrophysical analyses with mineralogical and geochemical data to improve the accuracy of predictive models (Lazar et al., 2015; Ochoa et al., 2022).
- North America > United States > Texas (1.00)
- North America > United States > New Mexico (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geophysics > Seismic Surveying (0.93)
- Geophysics > Borehole Geophysics (0.88)
- South America > Argentina > Patagonia > Neuquén > Neuquen Basin (0.99)
- North America > United States > Wyoming > Powder River Basin (0.99)
- North America > United States > Wyoming > Laramie Basin > Niobrara Formation (0.99)
- (55 more...)