Summary A composite fault detection attribute is produced by Adaptive Principal Component Analysis of attributes derived from spectral decomposition. The composite fault detection attribute looks similar in time slice view to conventional attributes such as coherency and curvature but is far more readily interpretable in vertical cross-section view. Detailed interpretation of time slices reveals that window based attributes such as coherence can exhibit discontinuities in the incorrect spatial position if the time slice does not correspond to the strongest event in the window. This problem is less severe on time slices of the composite fault detection attribute and faults are thus, more correctly located. These ideas are demonstrated in a case study in the Hitts Lake Field, where faults are verified by missing section in well logs.
Hackley, Paul C. (U.S. Geological Survey) | Valentine, Brett J. (U.S. Geological Survey) | Enomoto, Catherine B. (U.S. Geological Survey) | Lohr, Celeste D. (U.S. Geological Survey) | Scott, Krystina R. (U.S. Geological Survey) | Dulong, Frank T. (U.S. Geological Survey) | Bove, Alana M. (U.S. Geological Survey)
This study evaluates regional ‘shale gas’ prospectivity of the Aptian section (primarily Pine Island Shale) in the downdip Mississippi Salt Basin (MSB). Previous work by the U.S. Geological Survey estimated a mean undiscovered gas resource of 8.8 trillion cubic feet (TCF) in the chronostratigraphic-equivalent Pearsall Formation in the Maverick Basin of south Texas, where industry has established a moderately successful horizontal gas and liquids play. Wells penetrating the downdip MSB Aptian section at depths of 12,000-15,000 ft were used to correlate formation tops in a 15-well cross-section extending about 200 miles (mi) east-southeastward from Adams Co. to Jackson Co. Legacy cuttings from these wells were analyzed for thermal maturity and source rock quality. Bitumen reflectance (n=53) increases with increasing present-day burial depth in the east-central study area from 1.0% to 1.7%. As the Aptian section shallows in Adams Co. to the west, bitumen Ro values are higher (1.7-2.0%), either from relatively greater heat flux or greater mid-Cenomanian uplift and erosion in this area. Total organic carbon (TOC) content ranges 0.01-1.21 and averages 0.5 wt.% (n=51); pyrolysis output (S2; n=51) averages 0.40 mg HC/g rock, indicating little present-day hydrocarbon-generative potential. Bitumen reflectance is preferred as a thermal maturity parameter as Tmax values are unreliable. Normalized X-ray diffraction (XRD) mineral analyses (n=26) indicate high average clay abundance (53 wt.%) relative to quartz (29%) and carbonate (18%). Mineral content shows a spatial relationship to an Appalachian orogen clastic sediment source, with proximal high clay and quartz and distal high carbonate content. Clastic influx from the Appalachian orogen is confirmed by detrital zircon U-Pb ages with dominant Grenville and Paleozoic components [105 ages from a Rodessa sandstone and 112 ages from a Paluxy (Albian) sandstone]. Preliminary information from fluid inclusion microthermometry (41 aqueous measurements from calcite cements in one argillaceous James Limestone sample) indicates homogenization temperatures (Th) of 120-135°C, consistent with present-day bottom-hole conditions and measured bitumen Ro values towards the western end of the MSB. Downdip in the central MSB, microthermometry (26 aqueous measurements from quartz dust rims in one Paluxy sandstone sample) and measured bitumen Ro values indicate maximum temperatures may have been significantly higher (~25°C) than present-day conditions. High inclusion salinities (15-25 wt.% salt) at both locations suggest interaction of pore fluids with evaporites. Mercury injection capillary pressure (MICP) analyses (n=3) indicate porosity ranges 1.3-2.1% and permeability 0.006-0.02 μD for Pine Island and Rodessa shales. Overall, results from this work indicate generally poor ‘shale gas’ prospectivity compared to other shale reservoirs based primarily on depth, low organic content, low porosity, and high clay content. However, thickness and thermal maturity are appropriate, moderate reservoir pressures are present, and petroleum systems modelling by others has indicated high undiscovered gas potential for the basin as a whole.
In assessing the efficiency of any CO2 enhanced oil recovery projects, it is essential to track the flow path of injected CO2 and determine the bypassed oil pockets.
Time lapse seismic is an effective tool for monitoring CO2 movement and deciphering flow channels in the reservoir. Reservoir simulation is used by reservoir engineers to determine displacement efficiency and dynamic changes of pressure and saturations in the reservoir, using the production-injection data. Reservoir simulation also serves as a prediction tool to evaluate the economics of the CO2 projects. Both time lapse seismic analysis and reservoir simulation can complement each other to increase the resolution of CO2 flow path and CO2 effectiveness.
In this paper, integration of geology, geophysics and engineering was applied to a continuous CO2 injection EOR project in Delhi field, Louisiana. The CO2-targeted formations are Tuscaloosa and Paluxy sandstones. The production and injection activities in Delhi Field result in changes in fluid saturations and pore pressure. These changes affect properties of the reservoir rock and fluids, which can be detected by time-lapse seismic analysis as P-wave anomalies. A geologic model was built using well logs, petrophysical measurements and seismic inversion for the Reservoir Characterization Project (RCP) area. Simulation models, including a black oil model and an eleven-component compositional model, were built based on the geologic model as the initial step in the integrated research effort. Production history matches were performed for the primary, secondary and tertiary production phases of the field.
The simulated flow paths of the injected CO2 agree well with the time-lapse seismic interpretations. This agreement validates the effectiveness of integrating geologic modeling, reservoir simulation, and time-lapse seismic analysis in assisting field operations and maximizing oil recovery efficiency.
Haghighat, S. Alireza (West Virginia University) | Mohaghegh, Shahab D. (West Virginia University) | Gholami, Vida (West Virginia University) | Shahkarami, Alireza (West Virginia University) | Moreno, Daniel Alejandro (West Virginia University)
Smart Fields are distinguished with two characteristics: Big Data and Real-Time access. A small smart field with only ten wells can generate more than a billion data points every year. This data is streamed in real-time while being stored in data historians. The challenge for operating a smart field is to be able to process this massive amount of information in ways that can be useful in reservoir management and relevant operations. In this paper we introduce a technology for processing and utilization of data generated in a smart field. The project is a CO2 storage demonstration at Citronelle Dome, Alabama and the objective is to use smart field technology to build a real-time, long-term, CO2 Intelligent Leakage Detection System (ILDS).
The main concern for geologic CO2 sequestration is the capability of the underground carbon dioxide storage to confine and sustain the injected CO2 for very long time. If a leakage from a geological sink occurs, it is crucial to find the approximate location and amount of the leak in order to take on proper remediation activity.
To help accommodate CO2 leak detection, two PDGs (Permanent Down-hole Gauges) have been installed in the observation well. A reservoir simulation model for CO2 sequestration at Citronelle Dome was developed. Multiple scenarios of CO2 leakage are modeled and high frequency pressure data from the PDGs in the observation well are collected. The complexity of the pressure signal behavior and the reservoir model makes the use of inverse solution of analytical models impractical. Therefore an alternate solution is developed for the ILDS, based on Machine Learning.
High Frequency Data Streams are processed in real-time, summarized (by Descriptive Statistics) and transformed into a format appropriate for pattern recognition technology. Successful detection of location and amount of CO2 leaking from the reservoir using the real-time data streams demonstrates the power of pattern recognition and machine learning as a reservoir and operational management tool for smart fields.
Pore pressure and CO2 saturation changes are important to detect and quantify for maximizing oil recovery in Delhi Field. Delhi Field is a enhanced oil recovery (EOR) project with active monitoring by 4D multicomponent seismic technologies.
Dynamic rock physics modeling integrates the rich dataset of core, well logs, thin sections and facies providing a link between reservoir and elastic properties. We use Vp/Vs ratio and acoustic impedance to predict pore pressure and CO2 saturation changes in the reservoir. PP and PS seismic data are used to jointly invert for Vp/Vs ratio and acoustic impedance. Combination of the inversion results from the monitor surveys of June 2010 and August 2011 provides impedance and Vp/Vs percentage differences. The time-lapse inverted response fits the predicted dynamic models (calibrated at the wells).
Dynamic reservoir characterization adds value in this stratigraphic complex reservoir composed by the Paluxy and Tuscaloosa Formations. The results indicate that reservoir heterogeneities and pore pressure gradients control the CO2 flow. Paluxy injectors 148-2 and 140-1 shows CO2 is moving downdip following a distributary channel and induced by differential pressure from an updip injector or a barrier caused by a heterogeneity in the reservoir. This fact makes reservoir monitoring important for hydrocarbon recovery and reservoir management at Delhi Field.
Koperna, George Jonathan (Advanced Resources International Inc.) | Riestenberg, David Edward (Advanced Resources International Inc.) | Kuuskraa, Vello Alex (Advanced Resources International Inc.) | Rhudy, Richard (Electric Power Research Inst.) | Trautz, Robert Christian (Electric Power Research Inst.) | Hill, Gerald (Southern States Energy Board) | Esposito, Richard A. (Southern Company)