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Abstract This paper presents a new wettability alteration model based on surface complexation theory and an extensive experimental dataset. The objective is to provide a general correlation for contact angle calculation that (1) captures the main mechanisms that impact rock-brine-oil wettability and (2) minimizes the number of parameters used to tune with experimental data. We compile a set of 141 zeta-potential and contact-angle measurements from the literature. We study the oil/rock surface-complexation reactions and model the electrostatic behavior of each data point. We develop a new wettability model that estimates the contact angle and consists of five terms based on the Young-Laplace equation. We use the Nelder-Mead optimization algorithm to determine the model-parameter values that produce the best fit of experimental observations. The contact angle estimates produced by our model are also verified against those calculated by Extended-Derjaguin-Landau-Verwey-Overbeekand (EDLVO) theory and are validated using UTCOMP-IPhreeqc to simulate five limestone Amott tests from the literature. The Blind-testing test reveals that our model is predictive of the experimental data (R = 0.81, RMSE = 12.5). While reducing the tuning parameters by half, our model is comparable to andโin some casesโeven superior to the EDLVO modeling in predicting the contact angle measurements. We argue that EDLVO modeling has 10+ parameters, and the individual errors associated with each parameter could lead to wrong predictions. Amott-test simulations show excellent agreement between the proposed wettability-alteration model and experimental data. The rock's initial wettability was measured to be strongly oil-wet, with a negative Amott index and recovery factor around 5%, corroborating the calculated contact angle of 160 degrees. The recovery factor increases to about 20-35% as the rock becomes more water-wet after interaction with engineered water (contact angle changes to 90-64 degrees). Further analysis indicates the proposed model's capability to capture significant wettability-alteration trends. For example, we report increased water-wetting as brine ionic strength decreases, depicting the low-salinity effect. In addition, our model resulted in better convergence in some of the simulated core floods compared to EDLVO modeling. We conclude that our physics-based and data-driven model is a practical and efficient approach to predict rock-brine-oil wettability.
- Geology > Geological Subdiscipline (1.00)
- Geology > Mineral (0.69)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.48)
- North America > United States > Alaska > North Slope Basin > Duck Island Field > Endicott Field > Kekiktuk Formation (0.99)
- North America > United States > Texas (0.89)
- Europe > United Kingdom > North Sea (0.89)
- (3 more...)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Chemical flooding methods (0.67)
Abstract The main objective of this work is to investigate efficient estimation of the optimal design variables that maximize net present value (NPV) for the life-cycle production optimization during a single-well CO2 huff-n-puff (HnP) process in unconventional oil reservoirs. During optimization, the NPV is calculated by a machine learning (ML) proxy model trained to accurately approximate the NPV that would be calculated from a reservoir simulator run. The ML proxy model can be obtained with either least-squares support vector regression (LS-SVR) or Gaussian process regression (GPR). Given forward simulation results with a commercial compositional simulator that simulates miscible CO2 HnP process in a simple hydraulically fractured unconventional reservoir model with a set of design variables, a proxy is built based on the ML method chosen. Then, the optimal design variables are found by maximizing the NPV based on using the proxy as a forward model to calculate NPV in an iterative optimization and training process. The sequential quadratic programming (SQP) method is used to optimize design variables. Design variables considered in this process are CO2 injection rate, production BHP, duration of injection time period, and duration of production time period for each cycle. We apply proxy-based optimization methods to and compare their performance on several synthetic single-well hydraulically fractured horizontal well models based on Bakken oil-shale fluid composition. Our results show that the LS-SVR and GPR based proxy models prove to be accurate and useful in approximating NPV in optimization of the CO2 HnP process. The results also indicate that both the GPR and LS-SVR methods exhibit very similar convergence rates and require similar computational time for optimization. Both ML based methods prove to be quite efficient in production optimization, saving significant computational times (at least 5 times more efficient) than using a stochastic gradient computed from a high fidelity compositional simulator directly in a gradient ascent algorithm. The novelty in this work is the use of optimization techniques to find optimum design variables, and to apply optimization process fast and efficient for the complex CO2 HnP EOR process which requires compositional flow simulation in hydraulically fractured unconventional oil reservoirs.
- North America > United States > North Dakota (0.67)
- North America > United States > Montana (0.46)
- North America > United States > Texas (0.46)
- North America > Canada > Alberta (0.46)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (0.68)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play (0.46)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.36)
- North America > United States > South Dakota > Williston Basin > Bakken Shale Formation (0.99)
- North America > United States > North Dakota > Williston Basin > Bakken Shale Formation (0.99)
- North America > United States > Montana > Williston Basin > Bakken Shale Formation (0.99)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Thermal 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 (1.00)
Abstract CO2 storage through CO2 enhanced oil recovery (EOR) is considered as one of the technologies to help promote larger scale deployment of CO2 storage because of associated economic benefits through oil recovery, 45Q tax credits and the utilization of existing infrastructure. The objective of this study is to demonstrate how optimal reservoir management and operation strategies (including well completions and controls) can be used to optimize both CO2 storage and oil recovery. The optimization problem was focused on jointly estimating the well completions (i.e., fraction of injection/production well perforations in each reservoir layer) and CO2 injection/oil production controls that maximize the net present value (NPV) in a CO2 EOR and storage operation. We utilized the newly developed StoSAG algorithm, one of the most efficient optimization algorithms in the reservoir management community, to solve the optimization problem. The performance of joint optimization approach was compared with the performance of well control only optimization approach. In addition, the performance of co-optimization of CO2 storage and oil recovery approach was compared with the performances of maximization of only CO2 storage and maximization of only oil recovery approaches. The optimization results showed that a joint optimization of well completions and well controls can achieve an 8.84% higher final NPV than the one obtained from the optimization of only well controls. It was observed that the NPV incremental for joint optimization is mainly due to the fact that the optimal well completions and controls approach results in efficient CO2 storage and oil production from different reservoir layers depending on the differences in individual layer properties. Comparison of co-optimization (i.e., maximization of NPV) and maximization of only CO2 storage or only oil recovery showed that the co-optimization and maximization of only oil recovery result in significantly higher final NPV than that obtained through maximization of only CO2 storage approach while maximization of only CO2 storage can achieve significantly higher CO2 storage in the reservoir compared to the other two scenarios. The similar results for co-optimization and maximization of oil production are obtained because of the difference in oil revenue compared to CO2 storage tax credit. To the best of our knowledge, this is the first study in oil/gas industry and CO2 storage community to perform joint optimization of well completions and well controls in the fields. We expect that the proposed optimization framework will be a useful and efficient tool for field engineers to optimally manage CO2 EOR projects to maximize revenue through oil recovery as well as CO2 storage by taking advantage of the new 45Q tax law.
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > North America Government > United States Government (0.94)
- 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)
- (26 more...)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery (1.00)
- Health, Safety, Environment & Sustainability > Sustainability/Social Responsibility > Sustainable development (1.00)
- (2 more...)
Abstract The Gas and Downhole Water Sink-Assisted Gravity Drainage (GDWS-AGD) process has been developed to overcome of the limitations of Gas flooding processes in reservoir with strong aquifers. These limitations include high levels of water cut and high tendency of water coning. The GDWS-AGD process minimizes the water cut in oil production wells, improve gas injectivity, and further enhance the recovery of bypassed oil, especially in reservoirs with strong water coning tendencies. The GDWS-AGD process conceptually states installing two 7 inch production casings bi-laterally and completing by two 2-3/8 inch horizontal tubings: oil producer above the oil-water contact (OWC) and one underneath OWC for water sink drainage. The two completions are hydraulically isolated by a packer inside the casing. The water sink completion is produced with a submersible pump that prevents the water from breaking through the oil column and getting into the horizontal oil-producing perforations. The GDWS-AGD process was evaluated to enhance oil recovery in the heterogeneous upper sandstone pay in South Rumaila Oil field, which has an infinite active aquifer with a huge edge water drive. A compositional reservoir flow model was adopted for the CO2 flooding simulation and optimization of the GDWS-AGD process. Design of Experiments (DoE) and proxy metamodeling were integrated to determine the optimal operational decision parameters that affect the GDWS-AGD process performance: maximum injection rate and pressure in injection wells, maximum oil rate and minimum bottom hole pressure in production wells, and maximum water rates and minimum bottom hole pressure in the water sink wells. More specifically, Latin hypercube sampling and radial basis neural networks were used for the optimization of the GDWS-AGD process performance and to build the proxy model, respectively. In the GDWS-AGD process results, the water cut and coning tendency were significantly reduced along with the reservoir pressure. That resulted to improve gas injectivity and increase oil recovery. Further improvement in oil recovery was achieved by the DoE optimization after determining the optimal set of operational decision factors that constrains the oil and water production with gas injection. The advantage of GDWS-AGD process comes from its potential feasibility to enhance oil recovery while reducing water coning, water cut, and improving gas injectivity. That gives another privilege for the GDWSAGD process to reach significant improvement in oil recovery in comparison to other gas injection processes, such as the Gas-Assisted Gravity Drainage (GAGD) process, particularly in reservoirs with strong water aquifers.
- North America > United States (1.00)
- Europe (1.00)
- Asia > Middle East > Iraq > Basra Governorate (0.50)
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.49)
- Asia > Middle East > Saudi Arabia > Arabian Gulf > Arabian Basin > Arabian Gulf Basin > Zuluf Field > Wasia Formation (0.99)
- Asia > Middle East > Saudi Arabia > Arabian Gulf > Arabian Basin > Arabian Gulf Basin > Zuluf Field > Shuโฒaiba Formation (0.99)
- Asia > Middle East > Saudi Arabia > Arabian Gulf > Arabian Basin > Arabian Gulf Basin > Zuluf Field > Khafji Formation (0.99)
- (8 more...)
Abstract Recently there has been an increasing interest in Enhanced Oil Recovery (EOR) from shale oil reservoirs, including CO2 and field gas injection. For the performance assessment and optimization of CO2 and gas injection processes, compositional simulation is a powerful and versatile tool because of the capability to incorporate reservoir heterogeneity, complex fracture geometry, multi-phase and multi-component effects in nano-porous rocks. However, flow simulation accounting for such complex physics can be computationally expensive. In particular, field scale optimization studies requiring large number of high resolution compositional simulations can be challenging and sometimes computationally prohibitive. In this paper, we present a rapid and efficient approach for optimization of CO2 and gas injection EOR in unconventional reservoirs using the Fast Marching Method (FMM)-based flow simulation. The FMM-based simulation is analogous to streamline simulation and utilizes the concept of โDiffusive Time-of-Flight (DTOF)โ. The DTOF is a representation of the travel time of pressure โfrontโ propagation and accounts for geological heterogeneity, well architecture and complex fracture geometry. The DTOF can be efficiently obtained by solving the โEikonal equationโ using the FMM. The 3-D flow equation is then decoupled into equivalent 1-D equation using the DTOF as a spatial coordinate, leading to orders of magnitude faster computation for high-resolution and compositional models as compared to full 3-D simulations. The speed of computation enables the use of robust population-based optimization techniques such as genetic or evolutionary-based algorithm that typically require large number simulation runs to optimize the operational and process parameters. We demonstrated the efficiency and robustness of our proposed approach using synthetic and field scale examples. We first illustrate the validation of FMM-based simulation approach using an example of CO2 Huff-n-Puff for a synthetic dual-porosity and heterogeneous model with a multi-stage hydraulically fractured well. In the field-scale application, we present an optimization of operating strategies for gas injection EOR for a depleted shale oil reservoir in the Eagle Ford formation. The rapid computation of the FMM-based approach enabled intensive simulation study involving high-resolution geological models with million cells resulting in a comprehensive evaluation of the EOR project including sensitivity studies, parameter importance analysis and optimal operating strategies. This study shows the novelty and efficiency of the systematic optimization workflow incorporating the FMM-based compositional simulation for the field-scale modeling of CO2 and gas injection in shale oil reservoirs. Not only can it account for relevant physics such as reservoir heterogeneity, fracture geometry and fluid phase behavior but also lead to orders of magnitude saving in computational time over commercial finite difference simulators.
- North America > United States > Texas (1.00)
- Europe (0.93)
- Research Report > New Finding (0.86)
- Research Report > Experimental Study (0.54)
- Geology > Geological Subdiscipline (0.89)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.66)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Maverick Basin > Eagle Ford Shale Formation (0.99)
- (3 more...)
Abstract The polymer pilot project performed in the 8 TH reservoir of the Matzen field showed encouraging incremental oil production. To further improve the understanding of recovery effects resulting from polymer injection, an extension of the pilot is planned by adding a second polymer injector. Forecasting of the incremental oil production needs to take the uncertainty of the geological models and dynamic parameters into account. We propose a workflow which comprises a geological sensitivity and clustering step followed by a dynamic calibration step for decreasing the objective function to improve the reliability of a probabilistic forecast of the incremental oil recovery. For the geological sensitivity, hundreds of geological realizations were generated taking the uncertainty in the correlation of the sand and shale layers, logs, cores and geological facies into account. The simulated tracer response was used as dissimilarity distance to classify the geological realizations. Clustering was then applied to select 70 representative realizations (centroids) from a total of 800 to use in the full-physics dynamic simulation. In the dynamic simulation, an objective function comprising liquid rate and tracer concentration of the back-produced fluids was introduced. To further improve the calibration, the P50 value of incremental oil production as derived from simulation was compared with the incremental oil production determined from Decline Curve Analysis from the wells surrounding the polymer injection well. The mismatch between the P50 and the Decline Curve Analysis was improved by adjusting polymer viscosity. The calibrated models were then used to for a probabilistic forecast of incremental oil due to an additional polymer injector and to estimate the expected polymer concentration at the producing wells.
- North America > United States (1.00)
- Europe > Austria (1.00)
- Asia (1.00)
- Europe > Austria > Vienna > Vienna Basin (0.99)
- Europe > Austria > Vienna Basin > Matzen Field (0.99)
- Europe > Austria > Lower Austria > Vienna Basin (0.99)
- Asia > Middle East > Oman > Dhofar Governorate > South Oman Salt Basin > Marmul Field > Al-Qalata Formation (0.99)