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Collaborating Authors
Results
Improving Recovery in the Yates Field Using Dynamic Feedback Loop based on Physics-Informed Artificial Intelligence
De Bruyker, Dirk (NeoTek Energy) | Kosut, Robert (SC Solutions) | Valdez, Raul | Haymes, Seth (Kinder Morgan) | Schoeling, Lanny | Petro, Miroslav | Weiner, Douglas | Joseph, Alfred (NeoTek Energy) | Lee, Jun Kyu | Emami-Naeini, Abbas | Ebert, Jon | Ghosal, Sarbajit (SC Solutions)
Abstract We present a robust control system and methodology for physics-informed artificial intelligence (PAI) used to optimize and improve oil recovery, demonstrated in the Yates Field operated by the Kinder Morgan CO2 company. The system consists of a robust control system (referred to as Dynamic Feedback Loop, or DFL) equipped with novel hydrocarbon sensors that measure oil concentration and other parameters continuously and simultaneously on a set of producing wells. The goal of this system is to optimize operational parameters (e.g. choke valve settings, injection rates) to reach specific target metrics of production (e.g. maximizing produced oil while minimizing produced gas). The key element of our approach is the use of a multi-layer artificial neural network (deep neural network, or DNN, to be specific) that extracts physics-based parameters from the real-time measurements and predicts relevant parameters of the DFL control system. DNNs are prone to overfitting in training, making them ineffective in unfamiliar or challenging situations outside the training dataset. To overcome this problem, we have developed a physics-informed robust neural network technique, where the reservoir physics and sensor data are used to train DNN representations of the key physical parameters. Typically, only simplified physical models are developed using available geostatic or historical production data. Also, due to the dynamic nature of these systems, the accuracy of the models often changes over time. To improve predictive capability of the model, we combine the DNN with the system-theoretic robust control concepts based on physics with a model uncertainty formulation. The concept was first validated using a combination of simulations, isolated sensor data and analyses based on sets of historic production data. A study using historic production data on Kinder Morgan's Yates Field Unit (YFU) 4045 Pilot (3 producing wells) indicates application of the DFL system results in an increase in cumulative production of up to 35% per year, compared to what is obtained through a traditional (fixed-point) control system. Currently, the DFL is being field-tested on a different set of wells in the Yates Field, instrumented with the novel hydrocarbon sensors that generate continuous and simultaneous production data.
- 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)
- (23 more...)
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...)
Abstract Given limited CO2 supply, operational constraints, and pattern specific reservoir performance, WAG schedule can be customized such that NPV or other metrics are optimized. Depending on the WAG schedule, recovery can fluctuate between 5–15% at the pattern scale due to reservoir heterogeneity causing variations in sweep efficiency. An analytical method was developed to optimize WAG schedules that couples traditional reservoir modeling and simulation with machine learning, enabling the discovery of optimal WAG schedules that increase recovery at the pattern level. A history-matched reservoir model of Chaparral Energy's Farnsworth Field, Ochiltree County, TX was sampled intelligently to perform predictive reservoir flow simulations and artificially build an intelligent reservoir model that samples a broad range of possible WAG scenarios for optimization. The intelligent model generates the next "best" sample to investigate in the numerical simulator and converges on the optima, quickly reducing the number of runs investigated. Results in this paper demonstrate that there can be significant improvements in net present value as well as net utilization rates of CO2 using this analytical technique. The WAG design generated by the intelligent reservoir model should be deployed in the field in early 2016 for validation. It is intended that the intelligent reservoir model will be updated on a regular basis as injection and production data is obtained. This effort represents the beginning of a paradigm shift in the application of modeling and simulation tools for significant improvements in field production operations.
- North America > United States > Texas > Ochiltree County (0.24)
- North America > United States > Texas > Jones County (0.24)
- North America > United States > Texas > Fort Worth Basin > Farnsworth Field (0.99)
- North America > United States > Texas > Anadarko Basin (0.89)
- North America > United States > Oklahoma > Anadarko Basin (0.89)
- (2 more...)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Miscible methods (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Chemical flooding methods (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (0.86)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (0.68)