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DOE OSTI2021

Evaluation of “Tight Oil” Well Performance and Completion Practices in the Powder River Basin - “Time Slice” Analysis

The objective of this paper is to assess how well completion practices and well performance have evolved over time (“time slices”) in the tight sands and shales of the Powder River Basin (PRB). This information can provide a foundation for helping operators define more effective well completion practices and thus optimize well performance in an emerging “tight oil” basin. To start, the authors assembled a “database” containing well completion practices, production data, and geologic information for more than 800 horizontal (Hz) wells targeting three “tight oil” formations -- Turner, Frontier, and Mowry--placed on production in the past dozen years. To control for the impact of geologic and reservoir properties on well performance, the authors defined 12 geologically distinct areas (“partitions”) for the Turner, Frontier, and Mowry Shale in the Powder River Basin (4 partitions in each formation). This paper discusses the methodology for establishing “time slices” for each partition within each of the three “tight oil” formations, including (1) taking out, to the extent practical, the effects of geology by partitioning the three “tight oil” formations based on their geologic parameters; (2) using type curves and estimated ultimate recoveries (EURs) to establish a reliable measure of well performance; and (3) rigorously inspecting the production and well completion data to assure a quality dataset. Partition #2, a high thermal maturity area of the Frontier Sandstone in the western Powder River Basin, helps illustrate the results of the study. Using a dataset of 55 Hz wells, the study found that well performance has steadily improved, with oil EURs increasing from 180 MBbl in 2012-13 to 290 MBbl in 2018-19. Much of this improvement was due to the use of longer Hz laterals, increasing notably from 3,765 ft in 2012-13 to 10,260 ft in 2018-19. More intensive completion practices contributed, as well. For example, the number of frac stages more than doubled, from 17 to 35, and proppant concentrations increased from 1,060 lbs/ft to 1,320 lbs/ft. Finally, the data show diminishing returns to longer laterals and more intensive completion practices. For example, the key performance measure of oil EUR per 1,000 ft of lateral is decreasing. For 2012-13, Hz wells recovered 50 MBbl of oil per 1,000 ft of lateral while the more recent 2018-19 Hz wells only provide 30 MBbl of oil per 1,000 ft of lateral. Considerable insight can be gained by using “time slices” and geologic partitioning to better understand the relationship between changes in well drilling and completion practices and changes in well performance in emerging “tight oil” plays. The results from this study can also serve as a foundation for subsequent, more intense efforts involving data analytics for defining more optimum well completion practices targeting specified geologic settings and formations.

02 PETROLEUM↗

DOE OSTI2023

AmeriFlux US-CLF Cole Farm

This is the AmeriFlux version of the carbon flux data for the site US-CLF Cole Farm. Site Description - The Cole Farm catchment (0.65km2) is located ~ 4 km southwest of the Shale Hills site, draining orthogonally to a syncline axis of the Wills Creek Formation, a calcareous shale containing interbedded siltstone, sandstone, shaly limestone, and dolomite. Even though the farm adopted no-till practices in the 1970s, the axial channel of Cole Farm flows over a thick (>2.5 m) package of sediment in the valley floor. Soils range in texture from silty clay at the ridge top to sandy loam in the valley floor. Data was collected and funded by the Critical Zone Observatory Network.

Davis, Kenneth J.↗

DOE OSTI2026

AmeriFlux FLUXNET-1F US-CLF Cole Farm

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-CLF Cole Farm. This is the FLUXNET version of the carbon flux data for the site US-CLF Cole Farm produced by applying the standard ONEFlux (1F) software. Site Description - The Cole Farm catchment (0.65km2) is located ~ 4 km southwest of the Shale Hills site, draining orthogonally to a syncline axis of the Wills Creek Formation, a calcareous shale containing interbedded siltstone, sandstone, shaly limestone, and dolomite. Even though the farm adopted no-till practices in the 1970s, the axial channel of Cole Farm flows over a thick (>2.5 m) package of sediment in the valley floor. Soils range in texture from silty clay at the ridge top to sandy loam in the valley floor. Data was collected and funded by the Critical Zone Observatory Network.

Davis, Kenneth J. [Department of Meteorology, Eart↗

DOE OSTI2018

SECARB Petrographic Data and Photos

Thin sections, sedimentary graphic logs, and XRD results from CFU31-F2 and CFU31-F3 wells. Data collected as part of geologic characterization phase of SECARB project at the Cranfield oilfield in southwest Mississippi. Thin sections for CFU29-12 well also included. Associated Publications: Kordi, M., 2013, Characterization and prediction of reservoir quality in chlorite-coated sandstones: evidence from the Late Cretaceous Lower Tuscaloosa Formation at Cranfield Field, Mississippi, U.S.A., The University of Texas at Austin, Ph.D. dissertation, 193 p.

carbon↗

DOE OSTI2018

SECARB Core Analysis Data and Photos

Core photos and analysis collected from Cranfield oilfield in southwest Mississippi as part of SECARB project. Cores are from CFU31F-1, CFU31F-2, and CFU31F-3 wells in the Detailed Area of Study. Data includes permeability and porosity measurements and gamma ray scans. Associated Publications: Kordi, M., 2013, Characterization and prediction of reservoir quality in chlorite-coated sandstones: evidence from the Late Cretaceous Lower Tuscaloosa Formation at Cranfield Field, Mississippi, U.S.A., The University of Texas at Austin, Ph.D. dissertation, 193 p.

carbon↗

DOE OSTI2018

SECARB Pressure Monitoring Data

Bottom-hole, above zone monitoring interval, and injection zone pressure data collected during the SECARB project in Cranfield, Mississippi to assess the relationship between pressure field and multiphase field. Submission includes 10-second interval data from Detailed Area of Study wells: CFU31-F1 (injector), CFU31-F2 (observation), CFU31-F3 (observation) and Ella G Lees no. 7 (observation) well located west of the DAS. Associated Publications: Joy, C. A., 2011, The effects of pressure variation and chemical reactions on the elasticity of the lower Tuscaloosa sandstone of the Cranfield Field Mississippi, The University of Texas at Austin, Master’s thesis, 97 p. Kim, S., and Hosseini, S. A., 2013, Above-zone pressure monitoring and geomechanical analysis of a field scale CO2 injection, Cranfield Mississippi, Greenhouse Gases: Science and Technology, doi:10.1002/ghg.1388. Kim, S., and Hosseini, S. A., 2017, Study on the ratio of pore-pressure/stress changes during fluid injection and its implications for CO2 geologic storage: Journal of Petroleum Science and Engineering, v. 149, p. 138-150, doi:10.1016/j.petrol.2016.10.037. Mathias, S. A., Gluyas, J. G., Gonzalez Martinez de Miguel, G. J., and Hosseini, S. A., 2011, Role of partial miscibility on pressure buildup due to constant rate injection of CO2 into closed and open brine aquifers: Water Resources Research, v. 47, W12525, 11 p., doi:10.1029/2011WR011051. Meckel, T. A., Zeidouni, M., Hovorka, S. D., and Hosseini, S.A., 2013, Assessing sensitivity to well leakage from three years of continuous reservoir pressure monitoring during CO2 injection at Cranfield, MS, USA: International Journal of Greenhouse Gas Control, [insert volume no., page numbers], doi:10.1016/j.ijggc.2013.01.019. Nicot, J.-P., Oldenburg, C. M., Bryant, S. L., and Hovorka, S. D., 2009, Pressure perturbations from geologic carbon sequestration: area-of-review boundaries and borehole leakage driving forces, in Energy Procedia (v. 1, no.1), Proceedings of 9th International Conference on Greenhouse Gas Control Technologies, GHGT9, 16–20 November, Washington DC, p. 47–54. Tao, Q., Bryant, S. L., and Meckel, T. A., 2013, Modeling above-zone measurements of pressure and temperature for monitoring CCS sites: International Journal of Greenhouse Gas Control, v. 18, p. 523–530, doi:10.1016/j.ijggc.2012.08.011.

above zone monitoring interval↗

DOE OSTI2022

Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets

This synthetic multi-scale and multi-physics data set was produced in collaboration with teams at the Lawrence Berkeley National Laboratory, National Energy Technology Laboratory, Los Alamos National Laboratory, and Colorado School of Mines through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. Data are associated with the following publication: Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., “The Kimberlina Synthetic Geophysical Model and Data Set for CO2 Monitoring Investigations”, The Geoscience Data Journal, 2023, DOI: 10.1002/gdj3.191. The dataset uses the Kimberlina 1.2 CO2 reservoir flow model simulations based on a hypothetical CO2 storage site in California (Birkholzer et al., 2011; Wainwright et al., 2013). Geophysical properties models (P- and S-wave seismic velocities, saturated density, and electrical resistivity) were produced with an approach similar to that of Yang et al. (2019) and Gasperikova et al. (2022) for 100 Kimberlina 1.2 reservoir models. Links to individual resources are provided below: [CO2 Saturation Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models); Resistivity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-3); [Vp Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models); [Vs Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models); [Density Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models). The 3D distributions of geophysical properties for the 33 time stamps of the SIM001 model were used to generate synthetic seismic, gravity, and electromagnetic (EM) responses for 33 times between zero and 200 years. Synthetic surface seismic data were generated using 2D and 3D finite-difference codes that simulate the acoustic wave equation (Moczo et al., 2007). 2D data were simulated for six point-pressure sources along a 2D line with 10 m receiver spacing and a time spacing of 0.0005 s. 3D simulations were completed for 25 surface pressure sources using a source separation of 1 km in both the x and y directions and a time spacing of 0.001 s. Links to individual resources are provided below: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models) and [2D surface seismic data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-surface-seismic-data). [3D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-velocity-models), and 3D seismic data [year0](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year0), [year1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year1), [year2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year2), [year5](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year5), [year10](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year10), [year15](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year15), [year20](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year20), [year25](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year25), [year30](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year30), [year35](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year35), [year40](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year40), [year45](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year45), [year49](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year49), [year50](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year50), [year51](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year51), [year52](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year52), [year55](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year55), [year60](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year60), [year65](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year65), [year70](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year70), [year75](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year75), [year80](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year80), [year85](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year85), [year90](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year90), [year95](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year95), [year100](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year100), [year110](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year110), [year120](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year120), [year130](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year130), [year140](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year140), [year150](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year150), [year175](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year175), [year200](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year200). The Python scripts to read these models and data are provided [here](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-python-scripts). EM simulations used a borehole-to-surface survey configuration, with the source located near the reservoir level and receivers on the surface using the code developed by Commer and Newman (2008). Pseudo-2D data for the source at [2500 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz2500m) and [3025 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz3025m), used a 2D inline receiver configuration to simulate a response over 3D resistivity models. The [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-csem-data) contain electric fields generated by borehole sources at monitoring well locations and measured over a surface receiver grid. Vector gravity data, both on the surface and in boreholes, were simulated using a modeling code developed by Rim and Li (2015). The simulation scenarios were parallel to those used for the EM: [pseudo-2D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were calculated along the same lines and within the same boreholes, and [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were simulated over 3D models on the surface and in three monitoring wells. A series of [synthetic well logs](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-well-logs) of CO2 saturation, acoustic velocity, density, and induction resistivity in the injection well and three monitoring wells are also provided at 0, 1, 2, 5, 10, 15, and 20 years after the initiation of injection. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected in the Kimberlina 1 well that was drilled at the proposed site. Measurements of permeability and pore connectivity were made on cores of Vedder Sandstone, which forms the primary reservoir unit: [CT micro scans](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-ct-micro-scans-of-vedder-formation) and [Industrial CT Images](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-industrial-ct-images-vedder-formation). These measurements provide the range of scales in the otherwise synthetic data set to be as close to a real-world situation as possible. References: Birkholzer, J.T., Zhou, Q., Cortis, A. and Finsterle, S., 2011. A sensitivity study on regional pressure buildup from large-scale CO2 storage projects. Energy Procedia, 4, 4371-4378. Commer, M., and Newman, G.A., 2008. New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513-535. Gasperikova, E., Appriou, D., Bonneville, A., Feng, Z., Huang, L., Gao, K., Yang, X., Daley, T., 2022, Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes, Int. J. Greenh. Gas Control, Volume 114, 103585, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103585. Moczo, P., J.O. Robertsson and L. Eisner, 2007, The finite-difference time-domain method for modeling of seismic wave propagation: Advances in geophysics, 48, 421-516. Rim, H., and Y. Li, 2015, Advantages of borehole vector gravity in density imaging, Geophysics, 80, G1-G13. Wainwright, H. M.; Finsterle, S.; Zhou, Q.; Birkholzer, J. T., 2013. Modeling the Performance of Large-Scale CO2 Storage Systems: A Comparison of Different Sensitivity Analysis Methods. International Journal of Greenhouse Gas Control, 17, 189205. https://doi.org/10.1016/j.ijggc.2013.05.007, DOI: 10.18141/1603331. Yang, X., Buscheck, T.A., Mansoor, K., Wang, Z., Gao, K., Huang, L., Appriou, D., and Carroll, S.A., 2019. Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, 90, 102803, https://doi.org/10.1016/j.ijggc.2019.102803.

CCUS↗

DOE OSTI2023

SMART CarbonSAFE Model

The modeling work for this project built upon previous work done for the CarbonSAFE Rocky Mountain Phase I. A geologic and dynamic model was built in Petrel version 2019.3 for the area of the Colorado Plateau near Price, Utah. It is located at the north end of the San Rafael Swell in a natural gas field called Drunkards Wash. The model is 61.5 km east-west and 53 km north to south with 123x106x21 cells, giving a cell dimension of 50x50 m2. The 21 layers in the Z direction are of varying thickness depending on the formation. The Carmel, the seal layer, has 3 layers and is about 200 meters thick around the injection well. The Navajo Sandstone, the principal reservoir, has 5 layers and is about 100 meters thick. The Kayenta has 5 layers and is roughly 50 meters thick and the Wingate, a secondary reservoir, has 5 layers and is 120 meters thick. The base of the model is the Chinle Formation with 3 layers and about 120 meters of thickness..

Carbon Dioxide↗

DOE OSTI2024

Geologic Characterization of the South Georgia Rift Basin for Source Proximal CO2 Storage

The project Geologic Characterization of the South Georgia Rift Basin for Source Proximal CO2 Storage is one of 9 site characterization projects that were implemented as part of ARRA (American Recovery and Reinvestment Act). Data from this project was used to improve resolution of data in NATCARB in the area of study. Data related to this study has already been incorporated in NATCARB Atlas. The South Carolina Research Foundation and partners evaluated the feasibility of CCS in the Jurassic/ Triassic (J / TR) saline formations of the buried Mesozoic South Georgia Rift (SGR) Basin that extends from South Carolina into Georgia. The J / TR sequence, based on preliminary assessment of limited geologic and geophysical data, appears to have both the appropriate areal extent and multiple horizons to permanently and safely store CO2 The presence of several igneous rock layers within the sequence may potentially provide adequate seals to prevent upward CO2 migration into the Coastal Plain aquifer systems. Approximately 81 kilometers of 2-D seismic reflection data were collected by Bay Geophysical, Inc. to explore a portion of the SGR located in southern Georgia. The 81 kilometers were divided into two lines approximately 40.5 kilometers each, with Line 1 intersecting Georgia well GGS 3457. Line 2 intersects Line 1 at the southern portion of Line 1 to maximize the extent of coverage away from GGS-3457 (a deep well drilled in the 1980s for oil and gas exploration). This well had a set of usable logs, including gamma and neutron logs that provided promising results related to CO2 storage. Results showed sandstone with porosity values greater than 10 percent and a thickness of 120 meters. The design of the seismic shot was to extrapolate information away from the well and to better define the extent of the SGR and the necessary reservoir and caprock for a successful CO2 injection. A numerical simulation model of CO2 Injection and migration was developed based on the geology log for the GGS-3457 well. The simulation model was used to investigate the feasibility of injecting 30 million metric tons of CO2 into SGR J / TA sediments and integrity of the diabase layers as seals to prevent CO2 migration.

2-D seismic↗

DOE OSTI2018

Oxygen Isotope Microanalysis By Secondary Ion Mass Spectrometry Suggests Continuous 300-million-year History of Calcite Cementation and Dolomitization in the Devonian Bakken Formation

Calcite cementation and dolomitization are key diagenetic processes in many sedimentary systems. Unravelling detailed histories and timescales of cementation and replacement is, however, often compromised by the limited spatial resolution of many analytical techniques; in some cases multiple grains are co-analyzed so that diagenetic histories are blurred and reaction periods are difficult to establish. In this study we have used 10-micrometer-resolution, in situ secondary ion mass spectrometry to determine the oxygen isotope composition of 197 individual, 10–50-micrometer-size crystals of dolomite and calcite from six samples in a single core of Upper Devonian middle Bakken Member siltstones and sandstones, the major tight oil formation of the Williston basin, USA. This amount of data places important constraints on the range of temperatures and times that carbonate cementation and replacement occurred. Petrographic data show that microcrystalline calcite cement is an early phase, and combined with mineralogical data suggest that much of the dolomite replaces calcite. Over spatial scales of less than a centimeter, analyses of individual calcite crystals have a range of 5‰ for δ 18 O in the group of crystals, and for the group of individual dolomite crystals, 10‰. These sub-centimeter ranges are as great as those observed in previous studies of carbonate cements sampled over many meters and remind us that previous low-resolution studies may have inadvertently analyzed mixed phases. There is no relationship between dolomite texture and isotopic composition at this spatial scale; microscale backscattered electron imagery and scanning electron microscopy cathodoluminescence zoning is seen, but cannot be resolved with a 10-micrometer spot size. Assuming, since it is an early cement, that calcite precipitated from seawater (δ 18 O = –1.5‰), it formed at ca. 15–40°C, mainly at the lower temperatures. Present-day formation waters in Devonian rocks in this region have oxygen isotope compositions of 7–8‰ VSMOW. Using these values as a likely dolomitizing fluid, we suggest that dolomitization occurred continuously between 40 and 140°C over 150–200 million years, most likely in a fluid with a high Mg/Ca ratio resulting from gypsum formation in local evaporites. Finally, we suggest that this exceptionally low rate of dolomitization was controlled by the low rate of supply of Mg in a very sluggish flow regime; dolomitization is incomplete because of a limited supply of Mg.

58 GEOSCIENCES↗

DOE OSTI2019

Experimental Preservation of Muscle Tissue in Quartz Sand and Kaolinite

Siliciclastic sediments of the Ediacaran Period contain exceptionally preserved fossils of macroscopic organisms, including three-dimensional casts and molds commonly found in sandstones and siltstones and some two-dimensional compressions reported in shales. The sporadic and variable associations of these exceptionally preserved macroscopic fossils with pyrite, clay minerals, and microbial fossils and textures complicate our understanding of fossilization processes. This hinders inferences about the evolutionary histories, tissue types, original morphologies, and lifestyles of the enigmatic Ediacara biota. In this work, we investigate the delayed decay of scallop muscles buried in quartz sand or kaolinite for 45 days. This process occurs in the presence of microbial activity in mixed redox environments, but in the absence of thick, sealing microbial mats. Furthermore, microbial processes that mediate organic decay and release the highest concentrations of silica and Fe(II) into the pore fluids are associated with the most extensive tissue decay. Delayed decay and the preservation of thick muscles in sand are associated with less intense microbial iron reduction and the precipitation of iron oxides and iron sulfides that contain Fe(II) or Fe(III). In contrast, muscles buried in kaolinite are coated only by <10 μm-thick clay veneers composed of kaolinite grains and newly formed K- and Fe(II)-rich aluminosilicate phases. Muscles that undergo delayed decay in kaolinite lose more mass relative to the muscles buried in sand and undergo vertical collapse. These findings show that the composition of minerals that coat or precipitate within the tissues and the vertical dimension of the preserved features can depend on the type of sediment that buries the muscles. Similar processes in the zone of oscillating redox likely facilitated the formation of exceptionally preserved macrofossils in Ediacaran siliciclastic sediments.

58 GEOSCIENCES↗

DOE OSTI2014

Discrete Element Modeling Results of Proppant Rearrangement in the Cooke Conductivity Cell

The study of propped fracture conductivity began in earnest with the development of the Cooke cell which later became part of the initial API standard. Subsequent developments included a patented multicell design to conduct 4 tests in a press at the same time. Other modifications have been used by various investigators. Recent studies by the Stim-Lab proppant consortium have indicated that the flow field across a Cooke proppant conductivity testing cell may not be uniform as initially believed which resulted is significantly different conductivity results. Post test analysis of low temperature metal alloy injections at the termination of proppant testing prior to the release of the applied stress suggest that higher flow is to be expected along the sides and top of the proppant pack than compared to the middle of the pack. To evaluate these experimental findings, a physics-based two-dimensional (2-D) discrete element model (DEM) was developed and applied to simulate proppant rearrangement during stress loading in the Cooke conductivity cell and the resulting porosity field. Analysis of these simulations are critical to understanding the impact of modification to the testing cell as well as understanding key proppant conductivity issues such as how these effects are manifested in proppant concentration testing results. The 2-D DEM model was constructed to represent a realistic cross section of the Cooke cell with a distribution of four material properties, three that represented the Cooke cell (steel, sandstone,square rings), and one representing the proppant. In principle, Cooke cell materials can be approximated as assemblies of independent discrete elements (particles) of various sizes and material properties that interact via cohesive interactions, repulsive forces, and frictional forces. The macroscopic behavior can then be modeled as the collective behavior of many interacting discrete elements. This DEM model is particularly suitable for modeling proppant mechanical interactions subjected to an applied stress, where the experimental cell is represented as a cohesive body composed of a large number of discrete elements, and proppants can be modeled as the individual discrete particles with various sizes (following the proppant size distribution-density function used in the test) that exhibit no cohesive strength between the particles. Initial 2-D DEM modeling results suggest that proppant rearrangement and non-uniform stress distribution across the proppant pack results in significant non-uniform porosity distribution across the Cooke cell. Larger porosities develop along the edge of the proppant pack beneath the square ring seal and would result in a disproportionate higher flow field along these edges as compared to the middle of the proppant pack. These results suggest that reported conductivity values determined by the Cooke cell may be biased to overestimate the actual conductivity of the proppant at high stresses and that modifications to the standard Cooke cell will affect the magnitude of this bias.

03 NATURAL GAS↗

DOE OSTI2018

Cryogenic Fracturing of Wellbores Under True Triaxial-Confining Stresses: Experimental Investigation

A laboratory study of cryogenic fracturing was performed to test its ability to improve oil/gas recovery from low-permeability reservoirs. Our aim is to develop well-stimulation technologies using cryogenic fluids [e.g., liquid nitrogen (LN)] to increase permeability in a large reservoir volume surrounding wells. The new technology has the potential to reduce formation damage caused by current stimulation methods and minimize or eliminate water usage. The concept of cryogenic fracturing is that a sharp thermal gradient (thermal shock) created at the surfaces of formation rocks by applying cryogenic fluid can cause strong local tensile stress and start fractures. We created a laboratory system for cryogenic fracturing under true-triaxial loading, with LN-delivery/control and -measurement systems. The loading system simulates confining stresses by independently loading each axis up to approximately 5,000 psi on 888-in. cubes. Temperature in boreholes and at block surfaces and fluid pressure in boreholes were continuously monitored. Acoustic and pressure-decay measurements were obtained before and at various stages of stimulations. Cubic blocks (8 88-in.) of Niobrara shale, concrete, and sandstones were tested, and stress levels and anisotropies varied. Three schemes were considered: Gas fracturing without cryo-stimulation, gas fracturing after low-pressure cryogen flow-through, and gas fracturing after high-pressure cryogen flow-through. Findings from pressure-decay tests show that LN stimulation clearly increases permeability, and repeated stimulations further increase the permeability. Acoustic velocities and amplitudes decreased significantly after cryo-stimulation, indicating fracture creation. In the gas fracturing without the stimulation, breakdown (complete fracturing) occurs suddenly without any initial leaking, and major fracture planes form along the plane containing principal-stress and intermediate-stress directions, as expected theoretically. However, in the gas fracturing after cryogenic stimulations, breakdown occurred gradually and with massive leaking because of thermal fractures created during stimulation. In addition, the major fracture direction does not necessarily follow the plane containing the principal-stress direction, especially at low confining-stress levels. In tests, we observed that cryogenic stimulation seems to disrupt the internal stress field. The increase in borehole temperature after stimulation affects the permeability of the specimen. When a stimulated specimen is still cold, it maintains high permeability because fractures remain open and local thermal tension is maintained near the borehole. When the rock warms back, fractures close and permeability decreases. In these tests, we have not used proppants. Overall, fractures are clearly generated by low- and high-pressure thermal shocks. The added pressure of the high-pressure thermal shocks helps to further propagate cryogenic fractures generated by thermal shock. Breakdown pressure is significantly lowered by LN stimulation, with observed breakdown-pressure reductions up to approximately 40%.

42 ENGINEERING↗

DOE OSTI2019

An Investigation of CO2-Responsive Preformed Particle Gel for Conformance Control of CO2 Flooding in Reservoirs With Fractures or Fracture-Like Channels

Summary Gel treatment is an important technique to solve early CO2 breakthrough and excess–CO2–production problems, caused by the low viscosity and low density of CO2, as well as the heterogeneity of reservoirs with fractures or fracture–like channels. However, there is no reported work on gel that increases its volume after reacting with CO2 (termed CO2–responsive gel) for the conformance control of CO2 flooding. In this paper, the intrinsic properties of a CO2–responsive preformed particle gel (CR–PPG) were evaluated in a water/supercritical–CO2 (scCO2) environment in high–pressure vessels. Continuous scCO2 injection and CR–PPG treatment were conducted in fractured sandstone cores, to probe their plugging performance to scCO2 flow in a high–permeability–contrast system. The volumetric swelling ratio (VSR) of the CR–PPG increased by approximately two times in the presence of scCO2, compared with a sample under similar conditions in the absence of scCO2. The CR–PPG swelling ratio decreased with increasing NaCl concentration. Under the same conditions, the temperature did not have an apparent effect on the swelling ratio after 31 days of swelling. In coreflooding experiments, the placed CR–PPG resisted a considerable pressure up to 617.0 psi before breakthrough. After a shut–in process, CO2–breakthrough pressure was detected at 437.2 psi. It is observed that the shut–in process improved the plugging performance of CR–PPG to CO2 as revealed by the increase in the residual resistance factor. Controlling the shut–in time was found to be effective in augmenting the increase in the residual resistance factor, by increasing the VSR of placed CR–PPG. Resistance of CR–PPG to some real field challenges, including a high pressure gradient and long–term exposure to CO2, was also reported for field–applicability concerns.

Engineering↗

DOE OSTI2020

Fast and Accurate Core Analysis by the Full-Immersion Pressure-Pulse Decay: Part 1—Theory

Summary Core-scale measurements are considered the ground truth that oil and gas or electric utility companies use to predict the migration of fluids such as oil, natural gas, carbon dioxide, or brine deep underground during their extraction or injection operations. To provide a greater understanding of petrophysical properties of low-permeability geologic formations such as shales and tight gas sandstones, this study introduces a novel core-analysis procedure. The technique follows conventional pressure-pulse-decay permeametry, where the pressure in an inlet chamber adjacent to a cylindrical core plug undergoes a rapid pressurization, the system is shut in, and the pressure reaches a new equilibrium. However, unlike a standard unidirectional pressure-pulse decay, the full-immersion pressure-pulse decay (Hannon 2019) applies a pressure disturbance to the entire outer surface area of the sample. This article covers the numerical simulator designed to model flow through an anisotropic porous sample in this scenario. The model assumes distinct but uniform permeabilities along the radial and axial directions of the cylindrical plug sample. When extracting a plug vertically (or perpendicular to bedding), the permeability along the radial direction associates with the horizontal permeability (i.e., parallel to bedding), whereas in the axial direction, flow occurs perpendicular to bedding (a vertical permeability). Investigations of these model outputs demonstrate an approximately 20-fold decrease in time to complete a full-immersion experiment compared with conventional pressure-pulse decay. Furthermore, the pressure-decay curves resulting from the full-immersion method have slightly different shapes than those resulting from other unidimensional transient methods. These differences begin to demonstrate that, under achievable experimental conditions, the analysis of pressure data from one full-immersion test could enable the simultaneous estimation of the apparent permeabilities parallel and perpendicular to bedding of a cylindrical sample in addition to its porosity. A follow-up article finalizes the proof of this capability with a parameter-estimation procedure and presents experimental verification through a proof-of-concept study. Described in greater detail in that article, the parameter estimator requires multiple forward simulations to analyze the data, which behooves minimizing the compute time for each simulation. By using an alternating direction implicit time-marching scheme and a structured but variably spaced grid, the numerical simulator built for this purpose provides a forward model output with suitable accuracy in approximately 0.5 seconds.

Energy & Fuels↗

DOE OSTI2020

Fast and Accurate Core Analysis by the Full-Immersion Pressure-Pulse Decay: Part 2—Practice and Demonstration

Summary This study continues the work of presenting a novel approach for making petrophysical assessments of tight core samples. This method, the full-immersion pressure-pulse decay (Hannon 2019), involves applying a rapid increase in pressure in a chamber surrounding the entire outer surface area of a cylindrical sample, shutting the system in, and monitoring the pressure decay in the chamber as it reaches a new equilibrium. A precursor article covered the numerical simulator designed to model flow through the sample, demonstrating its performance and accuracy in addition to providing a first-order comparison between the speed and shape of the pressure-decay responses of the full-immersion method with those of other similar transient methods. This study covers the parameter-estimation procedure and experimental verification through a proof-of-concept laboratory investigation. The investigations provided here demonstrate that under appropriate, achievable experimental conditions, the pressure data can be analyzed in such a way that returns an estimate of the porosity and apparent permeabilities both parallel and perpendicular to bedding from a single test performed on a single cylindrical sample. After determining these experimental conditions (the uniqueness window), this report outlines a data-inversion strategy to estimate the petrophysical properties (porosity, horizontal permeability, and vertical permeability) from each test. This strategy is put to the test through comparisons with measurements performed by a commercial core laboratory. A common set of samples recovered from an outcrop of a tight-gas sandstone formation were investigated using the full-immersion method, and their results are compared with those from conventional steady-state measurement procedures performed by the commercial laboratory. Comparisons between petrophysical characterizations of these samples, which had permeabilities between 25 nd and 2.3 μd, demonstrated close agreement in most cases. However, whereas steady-state measurements performed at the professional laboratory required 4 to 5 hours of testing time per measurement of a single permeability, similar assessments using the full-immersion technique, requiring approximately 5 to 10 minutes to complete, returned estimates of the horizontal and vertical permeability simultaneously. Additional analyses are provided to determine principal reasons of discrepancies in instances where agreement was not as strong. Based on lessons learned from these experiences, the report closes with suggestions on areas of improvement in the experimental approach. Once complete, these developments should propel this technology to fill a critical need to determine petrophysical properties (porosity and permeability) of tight rocks in a time-efficient manner and in a way that does not compromise their accuracy.

Energy & Fuels↗

DOE OSTI2020

Direct Injection of Biomineralizing Agents to Restore Injectivity and Wellbore Integrity

Summary In this manuscript, we describe the second of two field demonstrations of microbially induced calcium carbonate precipitation (MICP) performed in a failed waterflood injection well in Indiana. In 2012, fracture-related flow pathways developed in the wellbore cement, causing injection water to bypass the oil-bearing formation and enter a high-permeability sandstone thief zone, thereby substantially decreasing injection pressure. In the first field demonstration, our study team characterized the well's mode of failure and successfully applied MICP to decrease flow through the defective cement. However, because the MICP treatment was conducted using a bailer delivery system, the degree of permeability reduction achievable was not adequate to fully restore the historic injection pressure of 1,400 psi at 1 gal/min. For the second field demonstration (reported herein), a direct injection system was developed that substantially increased the injection volume of MICP-promoting fluids. Two strategies were implemented to produce more ureolytic microbes: resuspending concentrated frozen cells immediately before injection and scaling up the bioreactor growth capacity. Multiple pulses of microbes and urea-calcium media were pumped into a string of 1-in.-diameter tubing separated by brine spacers and injected continuously at a flow rate of 3.4 to 1.4 gal/min. During the third day of injection, an injection pressure of 1,384 psi at a flow rate of 1.4 gal/min was achieved, and the experiment was terminated. This study demonstrates that MICP can be successfully used in large-volume applications where the time frame for the delivery of reactants is limited. This finding has significant relevance for commercialization of the MICP biotechnology in the oil and gas industry.

Engineering↗

DOE OSTI2022

Fluid – Fluid Interfacial Area and Its Impact on Relative Permeability - A Pore Network Modeling Study

Abstract Relative permeability (kr) is commonly modeled as an empirical function of phase saturation. Although current empirical models can provide a good match of one or two measured relative permeabilities using saturation alone, they are unable to predict relative permeabilities well when there is hysteresis or when physical properties such as wettability change. Further, current models often result in relative permeability discontinuities that can cause convergence and accuracy problems in simulation. To overcome these problems, recent research has modeled relative permeability as a state function of both saturation (S) and phase connectivity (X). Pore network modeling (PNM) data, however, shows small differences in relative permeability for the same S-X value when approached from a different flow direction. This paper examines the impact of one additional Minkowski parameter (Mecke and Arns, 2005), the fluid-fluid interfacial area, on relative permeability to identify if that satisfactorily explains this discrepancy. We calculate the total fluid-fluid interfacial areas (IA) during two-phase (oil/water) flow in porous media using pore network modeling. The area is calculated from PNM simulations using the areas associated with corners and throats in pore elements of different shapes. The pore network is modeled after a Bentheimer sandstone, using square, triangular, and circular pore shapes. Simulations were conducted for numerous primary drainage and imbibition cycles at a constant contact angle of 0° for the wetting phase. Simultaneous measurements of capillary pressure, relative permeability, saturation, and phase connectivity are made for each displacement. Fluid-fluid interfacial area is calculated from the PNM capillary pressure, the fluid location in the pore elements, and the pore element dimensional data. The results show that differences in the relative permeability at the same (S,X) point is explained well by differences in the fluid-fluid interfacial area (IA). That is, for a larger change in IA at these intersection points, the permeability difference is greater. That difference in relative permeability approaches zero as the difference in IA approaches zero. This confirms that relative permeability can be modeled better as a unique function of S, X, and IA. The results also show that an increase in IA restricts flow decreasing the nonwetting (oil) phase permeability. This decrease is caused by an increase in the throat area fraction compared to the corner area as the total area IA increases. The wetting phase relative permeability, however, shows the inverse trend, in that its relative permeability is greater when IA becomes larger owing to a greater fraction of the total area associated with the corners. The area IA, however, impacts the nonwetting phase relative permeability more than the wetting phase relative permeability. Corner flow improves the wetting phase relative permeability because the wetting phase is continuous there. Finally, a sensitivity analysis shows that relative permeability a is more sensitive to change in S than they are for IA for the case studied implying that if only two parameters are used to model relative permeability it is better to choose S and X.

Engineering↗