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Results
Abstract Recently, the miscible CO2-EOR tertiary process used in the main pay zone (MP) of suitable reservoirs has broadened to include exploitation of the underlying residual oil zone (ROZ) where a significant amount of oil may remain. The objective of this study is to identify the ROZ and to assess the remaining oil in a brownfield ROZ by using core data and conventional well logs with probabilistic and predictive methods. Core and log data from three wells located in the East Seminole Field in Gaines County, Texas, were used to identify the MP and ROZ in the San Andres Limestone, and to predict oil saturations. The core measurements were used to calculate probabilistic in-situ oil saturations within the MP and the ROZ as a function of depth. Well logs, in combination with core data and calculated saturations, on the other hand, were used to develop two expert systems using artificial neural networks (ANN); one to identify the ROZ and MP, and the other to predict oil saturation. These systems were also supported by a classification and regression tree (CART) analysis to delineate the rules that lead to classifications of zones. Results showed that expert systems developed and calibrated by combining core and well log data can identify MP and ROZ with a success score of more than 90%. Saturations within these zones can be predicted with a correlation coefficient of around 0.6 for testing and 0.8 for training data. The analyses showed that neutron porosity and density well log readings are the most influential ones to identify zones in this field and to predict oil saturations in the MP and ROZ. To explain the relationships of input data with the results, a rule-based system was also applied, which revealed the underlying petrophysical differences between MP and ROZ. This new predictive approach using machine learning techniques, could potentially address the challenges that previous studies have come up against in defining the ROZ within the formation and quantifying remaining oil saturations. The method can potentially be applied to additional fields and help reliably identify the ROZ and estimate saturations for future resource evaluations.
- North America > United States > Texas > Andrews County (0.72)
- North America > United States > Oklahoma > Seminole County (0.62)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (30 more...)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Low Salinity Waterflooding: From Single Well Chemical Tracer Test Interpretation to Sector Model Forecast Scenarios
Spagnuolo, Marco (eni S.p.A.) | Callegaro, Chiara (eni S.p.A.) | Masserano, Franco (eni S.p.A.) | Nobili, Marianna (eni S.p.A.) | Sabatino, Riccardo (eni S.p.A.) | Blunt, Martin J. (Imperial College)
Abstract We study Enhanced Oil Recovery (EOR) through Low Salinity (LS) waterflooding in a brown oil field. LS waterflooding is an emerging EOR technique in which water with reduced salinity is injected into a reservoir to improve oil recovery, as compared with conventional waterflooding, in which High Salinity (HS) brine or seawater are commonly used. The efficiency of this technique can be quantified at the well-scale by a Single Well Chemical Tracer Test (SWCTT), which is an in-situ method for measuring the Remaining Oil Saturation (ROS) after flooding the near-wellbore region with a displacing agent. Two SWCTTs were executed on a sandstone North African field. The tests were realized in sequence with seawater and LS water to evaluate the EOR potential at the well-scale. Here, we propose the interpretation of these two SWCTTs. They were modeled through numerical simulations because of the presence of several non-idealities in the complex scenario considered. A recently-developed tracer simulator was employed to solve the reactive transport problem. This was used as a fast post-processing tool coupled with a conventional reservoir simulator. Model parameters were estimated within an inverse modeling framework, on the basis of an assisted history matching procedure that exploits the Metropolis Hastings Algorithm (MHA). Results were scaled up on a sector model of the field, and forecast scenarios that consider a field-scale implementation of this technique were defined. The well-scale displacement efficiency gain associated with LS water, as compared with seawater, was evaluated. It was quantified as a ROS reduction of 8 saturation unit (s.u.), with a P10–P90 range of 3–15 s.u. Reservoir-scale simulations suggest that the associated ultimate oil recovery of the EOR pilot may be increased by 2% with LS water, with a P10–P90 range of 0.7–4.3%. Overall, the LS EOR potential for a selected field was quantified through a robust and original workflow, based on SWCTT interpretation. This state-of-the-art procedure is now available for further applications. The simulated oil recovery improvement with LS water is promising, and leads the way to the implementation of an inter-well field trial.
- Asia (1.00)
- North America > United States > Texas > Terry County (0.34)
- North America > United States > Texas > Gaines County (0.34)
- Europe > United Kingdom > North Sea > Southern North Sea (0.34)
- Research Report > Experimental Study (0.68)
- Research Report > New Finding (0.46)
- North America > United States > Texas > East Texas Salt Basin > East Texas Field > Woodbine Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > Block 6507/8 > Heidrun Field > Åre Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > Block 6507/8 > Heidrun Field > Tilje Formation (0.99)
- (18 more...)