scholarly journals Deposition, Diagenesis, and Sequence Stratigraphy of the Pennsylvanian Morrowan and Atokan Intervals at Farnsworth Unit

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1057
Author(s):  
Martha Cather ◽  
Dylan Rose-Coss ◽  
Sara Gallagher ◽  
Natasha Trujillo ◽  
Steven Cather ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Co2 Storage ◽  
Mercury Porosimetry ◽  
Large Set ◽  
Incised Valley ◽  
Petrographic Analyses ◽  
Regional Partnership ◽  
Field Unit ◽  
Core Descriptions ◽  
Depositional Setting

Farnsworth Field Unit (FWU), a mature oilfield currently undergoing CO2-enhanced oil recovery (EOR) in the northeastern Texas panhandle, is the study area for an extensive project undertaken by the Southwest Regional Partnership on Carbon Sequestration (SWP). SWP is characterizing the field and monitoring and modeling injection and fluid flow processes with the intent of verifying storage of CO2 in a timeframe of 100–1000 years. Collection of a large set of data including logs, core, and 3D geophysical data has allowed us to build a detailed reservoir model that is well-grounded in observations from the field. This paper presents a geological description of the rocks comprising the reservoir that is a target for both oil production and CO2 storage, as well as the overlying units that make up the primary and secondary seals. Core descriptions and petrographic analyses were used to determine depositional setting, general lithofacies, and a diagenetic sequence for reservoir and caprock at FWU. The reservoir is in the Pennsylvanian-aged Morrow B sandstone, an incised valley fluvial deposit that is encased within marine shales. The Morrow B exhibits several lithofacies with distinct appearance as well as petrophysical characteristics. The lithofacies are typical of incised valley fluvial sequences and vary from a relatively coarse conglomerate base to an upper fine sandstone that grades into the overlying marine-dominated shales and mudstone/limestone cyclical sequences of the Thirteen Finger limestone. Observations ranging from field scale (seismic surveys, well logs) to microscopic (mercury porosimetry, petrographic microscopy, microprobe and isotope data) provide a rich set of data on which we have built our geological and reservoir models.

scholarly journals Multiscale Assessment of Caprock Integrity for Geologic Carbon Storage in the Pennsylvanian Farnsworth Unit, Texas, USA

2021 ◽  
Author(s):  
Natasha Trujillo ◽  
Dylan Rose-Coss ◽  
Jason E. Heath ◽  
Thomas A Dewers ◽  
William Ampomah ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Noble Gas ◽  
Co2 Storage ◽  
Mercury Porosimetry ◽  
Coupled Processes ◽  
Data Set ◽  
Sedimentary Architecture ◽  
Caprock Integrity ◽  
Regional Partnership ◽  
Combined Data

The assessment of caprock integrity for underground storage of CO2 and/or enhanced oil recovery (EOR) systems is a multiscale endeavor. Caprock sealing behavior depends on coupled processes that operate over a broad range of length and time scales including nanoscale heterogeneity in capillary and wettability properties to depositional heterogeneity that is basin wide. Larger-scale sedimentary architecture, fractures, and faults can govern properties of potential “seal-bypass” systems that may be difficult to assess. We present a multiscale investigation of geologic sealing integrity of the caprock system that overlies the Morrow B sandstone reservoir, Farnsworth Unit, Texas, USA. The Morrow B sandstone is the target geologic unit for an on-going combined CO2 storage–EOR project by the Southwest Regional Partnership on Carbon Sequestration (SWP). Methods and/or data encompass small-to-large scales, including: petrography using electron and optical microscopy; mercury porosimetry; core examinations of sedimentary architecture and fractures; well logs; a suite of geomechanical testing; and a noble gas profile through sealing lithologies into the reservoir, as preserved from fresh core. The combined data set allows a comprehensive examination of sealing quality by scale, by primary features that control sealing behavior, and an assessment of sealing behavior over geologic time.

Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 Years of Production History

2021 ◽  
pp. 1-18
Author(s):  
Gisela Vanegas ◽  
John Nejedlik ◽  
Pascale Neff ◽  
Torsten Clemens
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
Numerical Models ◽  
Operating Conditions ◽  
Model Parameters ◽  
Large Set ◽  
Model Parameter ◽  
Production History ◽  
Hydrocarbon Fields ◽  
Parameter Distributions

Summary Forecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data. Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells. The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.

scholarly journals Impact of Low Salinity Water Injection on CO2 Storage and Oil Recovery for Improved CO2 Utilization

2021 ◽  
Vol 229 ◽  
pp. 116127
Author(s):  
Krishna Raghav Chaturvedi ◽  
Durgesh Ravilla ◽  
Waquar Kaleem ◽  
Prashant Jadhawar ◽  
Tushar Sharma
Keyword(s):  
Oil Recovery ◽  
Co2 Storage ◽  
Water Injection ◽  
Co2 Utilization ◽  
Low Salinity ◽  
Low Salinity Water ◽  
Salinity Water ◽  
Low Salinity Water Injection

scholarly journals Chemistry of Reservoir Fluids in the Aspect of CO2 Injection for Selected Oil Reservoirs in Poland

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6456
Author(s):  
Ewa Knapik ◽  
Katarzyna Chruszcz-Lipska
Keyword(s):  
Oil Recovery ◽  
Carbon Capture ◽  
Co2 Storage ◽  
Carbon Capture And Storage ◽  
Oil Reservoirs ◽  
Co2 Injection ◽  
Oil Composition ◽  
Co2 Solubility ◽  
Geological Co2 Storage ◽  
Reservoir Conditions

Worldwide experiences related to geological CO2 storage show that the process of the injection of carbon dioxide into depleted oil reservoirs (CCS-EOR, Carbon Capture and Storage—Enhanced Oil Recovery) is highly profitable. The injection of CO2 will allow an increasing recovery factor (thus increasing CCS process profitability) and revitalize mature reservoirs, which may lead to oil spills due to pressure buildups. In Poland, such a solution has not yet been implemented in the industry. This work provides additional data for analysis of the possibility of the CCS-EOR method’s implementation for three potential clusters of Polish oil reservoirs located at a short distance one from another. The aim of the work was to examine the properties of reservoir fluids for these selected oil reservoirs in order to assure a better understanding of the physicochemical phenomena that accompany the gas injection process. The chemical composition of oils was determined by gas chromatography. All tested oils represent a medium black oil type with the density ranging from 795 to 843 g/L and the viscosity at 313 K, varying from 1.95 to 5.04 mm/s. The content of heavier components C25+ is up to 17 wt. %. CO2–oil MMP (Minimum Miscibility Pressure) was calculated in a CHEMCAD simulator using the Soave–Redlich–Kwong equation of state (SRK EoS). The oil composition was defined as a mixture of n-alkanes. Relatively low MMP values (ca. 8.3 MPa for all tested oils at 313 K) indicate a high potential of the EOR method, and make this geological CO2 storage form more attractive to the industry. For reservoir brines, the content of the main ions was experimentally measured and CO2 solubility under reservoir conditions was calculated. The reservoir brines showed a significant variation in properties with total dissolved solids contents varying from 17.5 to 378 g/L. CO2 solubility in brines depends on reservoir conditions and brine chemistry. The highest calculated CO2 solubility is 1.79 mol/kg, which suggest possible CO2 storage in aquifers.

Enhanced Oil Recovery and CO2 Storage by Carbonated Water Injection

2009 ◽  
Author(s):  
Mehran Sohrabi ◽  
Masoud Riazi ◽  
Mahmoud Jamiolahmady ◽  
Shaun Ireland ◽  
Christopher Brown
Keyword(s):  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Co2 Storage ◽  
Water Injection ◽  
Carbonated Water Injection ◽  
Carbonated Water

Improving CO2 storage and oil recovery by injecting alcohol-treated CO2

2020 ◽  
Vol 60 (2) ◽  
pp. 662
Author(s):  
Saira ◽  
Furqan Le-Hussain
Keyword(s):  
Oil Recovery ◽  
Co2 Sequestration ◽  
Computer Modelling ◽  
Co2 Storage ◽  
Co2 Injection ◽  
Displacement Efficiency ◽  
Sweep Efficiency ◽  
Numerical Studies ◽  
Reservoir Simulator ◽  
Enhance Oil Recovery

Oil recovery and CO2 storage related to CO2 enhance oil recovery are dependent on CO2 miscibility. In case of a depleted oil reservoir, reservoir pressure is not sufficient to achieve miscible or near-miscible condition. This extended abstract presents numerical studies to delineate the effect of alcohol-treated CO2 injection on enhancing miscibility, CO2 storage and oil recovery at immiscible and near-miscible conditions. A compositional reservoir simulator from Computer Modelling Group Ltd. was used to examine the effect of alcohol-treated CO2 on the recovery mechanism. A SPE-5 3D model was used to simulate oil recovery and CO2 storage at field scale for two sets of fluid pairs: (1) pure CO2 and decane and (2) alcohol-treated CO2 and decane. Alcohol-treated CO2 consisted of a mixture of 4 wt% of ethanol and 96 wt% of CO2. All simulations were run at constant temperature (70°C), whereas pressures were determined using a pressure-volume-temperature simulator for immiscible (1400 psi) and near-miscible (1780 psi) conditions. Simulation results reveal that alcohol-treated CO2 injection is found superior to pure CO2 injection in oil recovery (5–9%) and CO2 storage efficiency (4–6%). It shows that alcohol-treated CO2 improves CO2 sweep efficiency. However, improvement in sweep efficiency with alcohol-treated CO2 is more pronounced at higher pressures, whereas improvement in displacement efficiency is more pronounced at lower pressures. The proposed methodology has potential to enhance the feasibility of CO2 sequestration in depleted oil reservoirs and improve both displacement and sweep efficiency of CO2.

scholarly journals Application of a physics-based lumped parameter model to evaluate reservoir parameters during CO2 storage

2020 ◽  
Vol 10 (8) ◽  
pp. 3925-3935
Author(s):  
Samin Raziperchikolaee ◽  
Srikanta Mishra
Keyword(s):  
Oil Recovery ◽  
Co2 Storage ◽  
Field Application ◽  
Oil Field ◽  
Lumped Parameter Model ◽  
Linear Regression Method ◽  
Oil Reservoir ◽  
Michigan Basin ◽  
Reservoir Properties ◽  
Lumped Parameter

Abstract Evaluating reservoir performance could be challenging, especially when available data are only limited to pressures and rates from oil field production and/or injection wells. Numerical simulation is a typical approach to estimate reservoir properties using the history match process by reconciling field observations and model predictions. Performing numerical simulations can be computationally expensive by considering a large number of grids required to capture the spatial variation in geological properties, detailed structural complexity of the reservoir, and numerical time steps to cover different periods of oil recovery. In this work, a simplified physics-based model is used to estimate specific reservoir parameters during CO2 storage into a depleted oil reservoir. The governing equation is based on the integrated capacitance resistance model algorithm. A multivariate linear regression method is used for estimating reservoir parameters (injectivity index and compressibility). Synthetic scenarios were generated using a multiphase flow numerical simulator. Then, the results of the simplified physics-based model in terms of the estimated fluid compressibility were compared against the simulation results. CO2 injection data including bottom hole pressure and injection rate were also gathered from a depleted oil reef in Michigan Basin. A field application of the simplified physics-based model was presented to estimate above-mentioned parameters for the case of CO2 storage in a depleted oil reservoir in Michigan Basin. The results of this work show that this simple lumped parameter model can be used for a quick estimation of the specific reservoir parameters and its changes over the CO2 injection period.

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