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This study fully integrates multidisciplinary, multi-scalar subsurface and surface data for successful exploration and development programs in a fractured rock reservoir. Unconventional Sycamore/Meramec and conventional Hunton Carbonate plays in Oklahoma are the focus of this study. The following questions are addressed in this thesis: 1) what fact...
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Characterizing the naturally fractured reservoir in a mature field is always a challenging task due to minimal subsurface data availability and the technology was not as advanced as nowadays. Therefore, this paper is proposed to provide an alternative solution to identify the presence of the fractures, classify them into the fractured quality relat...
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... The Arbuckle Group, especially in the upper part of the section, contains many karstic features and solution-collapse breccias developed during repeated subaerial exposure of north-south marine regression sequences [58]. These karst features are prominent factors controlling the matrix porosity, which usually provides a significant amount of porosity and permeability [31,32,33]. We suggest that such highporosity karsts within the Arbuckle Group can be targeted for CO 2 sequestration. ...
This paper examines a rigorous site characterization and analysis of geological storage capacity of CO 2 in Arbuckle Group in the north part of Oklahoma to accelerate Carbon Capture and Storage (CCS) and Utilization (CCUS) technology deployment. Data obtained from the core, logs, and historic wastewater injection and production data were used to build and validate a geological model. Subsurface structure, depth required to attain supercritical CO 2 , rock properties and required caprock criteria were applied to Arbuckle geological model to identify suitable region for geological storage of CO 2. The model estimates that the western Osage County has a storage capacity of > 50 million metric tons of CO 2. More specifically, two sweet spots with a higher potential for CO 2 storage were identified. The presence of several anthropogenic CO 2 sources in the vicinity of site, existing pipelines, and compression infrastructure are the significant elements of a techno-economic analysis of the prospect storage project(s). This study demonstrates that the carbonate Arbuckle Group could be a strategic geological unit for CO 2 sequestration, thus contributing toward emissions reduction from nearby industrial complex.
... The methodology used for the petrophysical calculations is summarized in the workflow ( Figure 5). The multiscale data integration yields high-level reservoir characterization and highly heterogeneous geological modeling Milad, 2019;. Results Figure 6 shows the Arbuckle thickness variations for the studied wells in Osage County with an average of 303 ft. Figure 7 shows the petrophysical log results and rock types at one of the Arbuckle wells (Total of 124 wells). ...
This study focuses on defining local-regional opportunities for carbon dioxide (CO2) storage in an underground saline formation in Oklahoma. CO2 sequestration is one of the major processes used to reduce carbon emissions intensity. This process exposes the rock to CO2 injection into the suitable geological subsurface formation of CO2 at which meets specific trapping mechanisms. Thus, this study characterizes the Arbuckle Group, to store CO2, with trapping mechanisms including storage potential (porous and permeable), impermeable caprock above CO2 reservoir, and a deeper depth. These mechanisms ensure safe and permanent storage and prevent CO2 from re-entering the atmosphere. This paper integrates multi-scalar scientific data from core and well logs for stratigraphic and petrophysical analyses to estimate the storage capacity of theCO2 Arbuckle reservoir in Osage County. First, Arbuckle stratigraphic thickness was determined from 124 wells. Then, lithology and electrofacies were determined from Arbuckle core and well logs. Afterward, total porosity, saturation, and permeability were determined at well locations. Finally, CO2 storage capacity was calculated volumetrically at a selected site in Osage County.
The presence of karst features in Arbuckle Group may provide a significant amount of porosity and permeability. Also, average porosity, and thickness for Arbuckle are 10% and 640 ft, respectively. The Woodford Shale is available in all studied wells, which may act as seal impermeable and caprock above the Arbuckle Group (CO2 reservoir). Therefore, the Arbuckle saline aquifer in Osage County Oklahoma, (Figure 1), could be an ideal candidate for CO2 sequestration with a storage capacity (~ 97M tonnes).
... Six common angle gathers ranging from 0 o to 30 o at 5 o increments were used for prestack inversion using © Hampson-Russell Software. Detailed descriptions about our inversion methodology and its results are available in Milad et al. [28] and Milad [29]. The main purpose of the inversion is to provide horizontal variograms for porosity distribution. ...
Optimal upscaling of a high-resolution static geologic model that reflects the flow performance of the reservoir is important for reasons such as time and calculation efficiency. In this study, we demonstrate that honoring reservoir heterogeneity is critical in predicting accurate production and reducing the time and cost of running reservoir flow simulations for the Hunton Group carbonate. We integrated three-dimensional (3D) seismic data, well logs, thin sections, multiscale fracture studies, discrete fracture networks, and geostatistical methods to create a 100 × 150 × 1 ft gridded representative geologic model. We calibrated our gridded porosity and permeability parameters, including the evaluation of fractures, by history matching the simulated production rate and cumulative production volumes from a baseline fine-scale model generated from petrophysical and production data obtained from five wells. We subsequently reperformed the simulations using a suite of coarser grids to validate our property upscaling workflow. Compared to our baseline history matching, increasing the horizontal grid cell sizes (i.e., horizontal upscaling) by factors of 2, 4, 8, and 16 results in cumulative production errors ranging from +0.5% for two time (2×) coarser to +74.22% for 16× coarser. The errors associated with vertical upscaling were significantly less, i.e., ranging from +0.5% for 2× coarser to +10.8% for 16× coarser. We observed higher production history matching errors associated with natural fracture size. Results indicate that greater connectivity provided by natural fracture length has a larger effect on production compared to the higher permeability provided by larger apertures. We also estimated the trade-off between accuracy and run times using two methods: (a) using progressively larger grid cell sizes; (b) applying 1, 5, 10, and 20 parallel processes. Computation time reduction in both scenarios may be described by simple power law equations. Observations made from our case study and upscaling workflow may be applicable to other carbonate reservoirs.
... In the Midcontinent region the Mississippian was characterized by four stages from oldest to youngest including, Kinderhookian, Osagean, Meramecian, and Chesterian (Watney et al., 2001). Deposition of siltstone and very fine sandstone separated by shale units comprised the predominant Mississippian Sycamore rocks in Southern Oklahoma (Milad, 2019). ...
This paper presents an interpreted depositional environment, developed 2nd order sequence stratigraphy framework, and detailed lithofacies identification from two Mississippian Sycamore outcrops (I-35 Sycamore and Speake Ranch) and subsurface wells in the SCOOP (South Central Oklahoma Oil Province). Then these rocks calibrate the rock properties with wireline log responses to identify the best landing zones.
Qualitative and quantitative techniques of field, laboratory, and machine learning studies were conducted. For the field studies, we measured the complete 450 ft of the outcrop stratigraphic section, another separate 50 ft outcrop, examined the underlying Woodford Shale and overlying Caney Shale boundary contacts, documented sedimentary structures, constructed an outcrop gamma-ray profile, and developed a sequence stratigraphic framework. Laboratory studies included petrographic analyses, detailed X-ray Fluorescence (XRF), Scanning Electron Microscopy (SEM), and X-ray Diffraction (XRD). For machine learning studies, a principal component analysis (PCA), elbow method, and self-organizing map (SOM) were used to analyze the electrofacies and chemofacies from the outcrop and a subsurface uncored well.
The outcrop hand-held gamma ray profile was obtained and correlated with subsurface wells. Five major outcrop lithofacies and chemofacies, within six stratigraphic units of alternating siltstone and shale strata, were identified from the wireline logs. A Maximum Flooding Surface (MFS), and two major 2nd order Sequence Boundaries (SB) were recognized at the outcrop and a nearby subsurface well. Bouma sequences and repetitive cycles of sedimentary structures indicated sediment gravity flow deposition on a marine slope setting.
This study provides geologic insights to better understanding the depositional environment and the lithology of the Sycamore rocks. The bioturbated siliceous shale and/or the sandy siltstone can be potential target zones due to their reservoir quality, lithology, bed continuity, and brittleness. This information can be of direct benefit to the exploration and development programs of many companies in the SCOOP area, particularly in the Anadarko and Ardmore basins in Oklahoma.
... The Mississippian was characterized by four stages from oldest to youngest including, Kinderhookian, Osagean, Meramecian, and Chesterian (Watney et al., 2001). Deposition of siltstone and very fine sandstone separated by shale units comprised the predominant Mississippian Sycamore rocks in Southern Oklahoma (Milad, 2019). ...
... More detailed Sycamore-Meramec studies are available in a doctorate studies by Milad (2019). ...
This paper presents detailed lithofacies identification from the I-35 Sycamore outcrop and predicts the rock properties from wireline logs to propose landing zones within the Mississippian Sycamore rocks in Southern Oklahoma. To achieve these objectives, three types of studies were conducted: (a) field studies (b) lab analysis, and (c) machine learning. In field studies, we measured the complete 450 ft Sycamore stratigraphic section on the south limb of the Arbuckle Mountains along I-35, measured the outcrop gamma-ray profile, calculated the fracture intensity per bed and restored the fracture orientations to the horizontal bedding plane. The contacts with the underlying Woodford Shale and overlying Caney Shale were additionally examined. Lab studies included petrographic analyses, Scanning Electron Microscopy (SEM), Rock Eval Pyrolysis analyses, and X-ray Diffraction (XRD). For machine learning studies, principal component analysis (PCA), elbow method, and self-organizing map (SOM) were used to analyze the electrofacies from the outcrop and an uncored well. As a result, it was found that the outcrop stratigraphy, lithofacies and electrofacies are tied with the hand-held gamma ray profile and correlated with a nearby subsurface well. Five major outcrop lithofacies are identified from wireline logs. Two fracture sets (N18E and N63W) were observed in the outcrop. Fracture intensity varied from 1.5 to 8 fractures per linear ft. Most fractures are filled with calcite, but some contain bitumen. The Rock Eval Pyrolysis analyses revealed that the I-35 Sycamore intervals are dominated by apparent type II and type III kerogen (oil prone and oil/gas prone) with an average Tmax of 440 ⁰C. Total organic matter ranged from 0.1 to 1.5 wt % in the outcrop. The reservoir quality was assessed by integrating lithofacies, fracture analyses, and geochemical analyses. The bioturbated shale and/or the sandy siltstone can be a potential target zone for the following reasons: the bioturbated shale is characterized by the highest fracture abundances (avg. 4.4 fractures per linear ft), and clay content is 35%, with 50% quartz, indicating a somewhat brittle rock, with a potential hydrocarbon migration, during production, from the underlying Woodford shale during hydraulic fracturing. The sandy siltstone is characterized by the absence of calcite cement, highest micro-porosity, highest quartz (58%), and potential hydrocarbon migrations from the underlying upper shale section of Mississippian rocks and/or charged from the overlying Caney shale or the underlying Woodford shale. These two lithofacies and other lithofacies can be predicted from well log signatures when uncored wells are unavailable. URTeC 991 2
Geoscientists have been implementing machine learning (ML) algorithms for several classifications and regression related problems in the last few decades. ML’s implementation in geosciences came in different phases, and often these broadly followed or lagged after certain advances in computer sciences. We can trace back some of the early applications of modern ML techniques to 1980–1990. Geoscientists were mostly dealing with deterministic analytical solutions at that time, and they were encouraged to do so at their organizations. This is also the time when geostatistics started flourishing in reservoir characterization and modeling efforts. Then, the early 2000’s saw a slight uptick in ML applications, mostly neural networks and decision trees. Since 2014–2015, a lot of ML-related work was published. In addition to open-source languages, this also has to do with access to the massive volume of data from unconventional reservoirs. And then, since 2017, there has been an explosion of deep learning related work. This again corresponds to the convolutional neural network architecture published by Goodfellow in 2014. Initially, the ML work in geosciences focused on petrophysics, seismic, and now core and thin section images. Another growing trend is the application of ML in passive geophysical data analysis (seismology, gravity, and magnetic, etc.). As of now, most of the published studies on ML are confined to outlier detection, facies, fracture, and fault classification, rock property (e.g., poro-perm-fluid saturation-total organic carbon-geomechanics) prediction, predicting missing logs/variables, and well log correlation. In this chapter, we will review some of these popular research problems tackled by ML.
