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Parsons, Mark A. (Virginia Tech, Blacksburg) | Kara, Mustafa Y. (Virginia Tech, Blacksburg) | Robinson, Kevin M. (United States Coast Guard Academy, New London) | Stinson, Nicholas T. (Virginia Tech, Blacksburg) | Brown, Alan J. (Virginia Tech, Blacksburg)
This article describes an architecture flow optimization (AFO) method for naval ship system design. AFO is a network-based method. It is used to design and analyze naval ship Mission, Power, and Energy Systems (MPES) in a naval ship Concept and Requirements Exploration (C&RE) process at a sufficient level of detail to better understand system energy flow, define MPES architecture and sizing, reduce system vulnerability, and improve system reliability. This method decomposes MPES into three architectures: logical, physical, and operational which describe the system’s spatial, functional, and temporal characteristics, respectively. Using this framework, the AFO incorporates system topologies, input/output energy coefficient component models, preliminary arrangements, and (nominal and damaged) steady-state operational scenarios into a linear optimization method to minimize the energy flow cost required to satisfy all operational scenario demands and constraints. AFO results are used to inform system topology design and assess the feasibly and survivability of representative designs in the C&RE process. AFO results may also be used in physics-based vital component sizing, calculation of vulnerability/effectiveness metrics in the C&RE process, and subsequent linear optimization formulations to assess recoverability and operational effectiveness in the time domain.
Suarez-Rivera, Roberto (W. D. Von Gonten Laboratories) | Panse, Rohit (W. D. Von Gonten Laboratories) | Sovizi, Javad (Baker Hughes) | Dontsov, Egor (ResFrac Corporation) | LaReau, Heather (BP America Production Company, BPx Energy Inc.) | Suter, Kirke (BP America Production Company, BPx Energy Inc.) | Blose, Matthew (BP America Production Company, BPx Energy Inc.) | Hailu, Thomas (BP America Production Company, BPx Energy Inc.) | Koontz, Kyle (BP America Production Company, BPx Energy Inc.)
Abstract Predicting fracture behavior is important for well placement design and for optimizing multi-well development production. This requires the use of fracturing models that are calibrated to represent field measurements. However, because hydraulic fracture models include complex physics and uncertainties and have many variables defining these, the problem of calibrating modeling results with field responses is ill-posed. There are more model variables than can be changed than field observations to constrain these. It is always possible to find a calibrated model that reproduces the field data. However, the model is not unique and multiple matching solutions exist. The objective and scope of this work is to define a workflow for constraining these solutions and obtaining a more representative model for forecasting and optimization. We used field data from a multi-pad project in the Delaware play, with actual pump schedules, frac sequence, and time delays as used in the field, for all stages and all wells. We constructed a hydraulic fracturing model using high-confidence rock properties data and calibrated the model to field stimulation treatment data varying the two model variables with highest uncertainty: tectonic strain and average leak-off coefficient, while keeping all other model variables fixed. By reducing the number of adjusting model variables for calibration, we significantly lower the potential for over-fitting. Using an ultra-fast hydraulic fracturing simulator, we solved a global optimization problem to minimize the mismatch between the ISIPs and treatment pressures measured in the field and simulated by the model, for all the stages and all wells. This workflow helps us match the dominant ISIP trends in the field data and delivers higher confidence predictions in the regional stress. However, the uncertainty in the fracture geometry is still large. We also compared these results with traditional workflows that rely on selecting representative stages for calibration to field data. Results show that our workflow defines a better global optimum that best represents the behavior of all stages on all wells, and allows us to provide higher-confidence predictions of fracturing results for subsequent pads. We then used this higher confidence model to conduct sensitivity analysis for improving the well placement in subsequent pads and compared the results of the model predictions with the actual pad results.
Summary A physics-based data-driven model is proposed for forecasting of subsurface energy production. The model fully relies on production data and does not require any in-depth knowledge of reservoir geology or governing physics. In the proposed approach, we use the Delft Advanced Reservoir Terra Simulator (DARTS) as a workhorse for data-driven simulation. DARTS uses an operator-based linearization technique that exploits an abstract interpretation of physics benefiting computational performance. The physics-based datadriven model is trained to fit data increasing the fidelity of the model forecast and reflecting significant changes in reservoir dynamics or physics over its history. The model is examined and validated for both synthetic and real field production data. We demonstrate that the developed approach is capable of providing accurate and reliable production forecast on a daily basis, even if the exact geological information is not available. Introduction Computer technologies are progressing rapidly. Computational capacities that are currently available provide an opportunity for many subsurface applications to perform complex numerical simulations of high-resolution 3D geocellular computer models. Predictions obtained from such models are an important factor governing efficient reservoir management and decision making. The models describe complex geological features through a set of gridblocks and associated rock and fluid properties. However, in many cases, the reliability of geological information is questionable or even not available. Although it is possible to develop a high-fidelity model on a reliable basis of reservoir geology, a high-resolution computer model can exceed a few million blocks and can take hours or even days to simulate. It is still not computationally feasible to perform history matching or reservoir-development optimization at such resolution because it involves a large number of simulation runs. Different methods have been developed to overcome the issue.
Summary In this work, we investigate the efficient estimation of the optimal design variables that maximize net present value (NPV) for the life-cycle production optimization during a single-well carbon dioxide (CO2) huff-n-puff (HnP) process in unconventional oil reservoirs. A synthetic unconventional reservoir model based on Bakken Formation oil composition is used. The model accounts for the natural fracture and geomechanical effects. Both the deterministic (based on a single reservoir model) and robust (based on an ensemble of reservoir models) production optimization strategies are considered. The injection rate of CO2, the production bottomhole pressure (BHP), the duration of injection and the production periods in each cycle of the HnP process, and the cycle lengths for a predetermined life-cycle time can be included in the set of optimum design (or well control) variables. 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. Similar to the ML algorithms, we use both least-squares (LS) support vector regression (SVR) and Gaussian process regression (GPR). Given a set of forward simulation runs with a commercial compositional simulator that simulates the miscible CO2 HnP process, a proxy is built based on the ML method chosen. Having the proxy model, we use it in an iterative-sampling-refinement optimization algorithm directly to optimize the design variables. As an optimization tool, the sequential quadratic programming (SQP) method is used inside this iterative-sampling-refinement optimization algorithm. Computational efficiencies of the ML proxy-based optimization methods are compared with those of the conventional stochastic simplex approximate gradient (StoSAG)-based methods. Our results show that the LS-SVR- and GPR-based proxy models are accurate and useful in approximating NPV in the optimization of the CO2 HnP process. The results also indicate that both the GPR and LS-SVR methods exhibit very similar convergence rates, but GPR requires 10 times more computational time than LS-SVR. However, GPR provides flexibility over LS-SVR to access uncertainty in our NPV predictions because it considers the covariance information of the GPR model. Both ML-based methods prove to be quite efficient in production optimization, saving significant computational times (at least 4 times more efficient) over a stochastic gradient computed from a high-fidelity compositional simulator directly in a gradient ascent algorithm. To our knowledge, this is the first study presenting a comprehensive review and comparison of two different ML-proxy-based optimization methods with traditional StoSAG-based optimization methods for the production optimization problem of a miscible CO2HnP.
Hampton, Thomas J. (Consultant) | El-Mandouh, Mohamed (Consultant) | Weber, Stevan (Consultant) | Thaker, Tirth (Computer Modelling Group) | Patel, K.. (Computer Modelling Group) | Macaul, Barclay (Computer Modelling Group) | Erdle, Jim (Computer Modelling Group)
Abstract Mathematical Models are needed to aid in defining, analyzing, and quantifying solutions to design and manage steam floods. This paper discusses two main modeling methods – analytical and numerical simulation. Decisions as to which method to use and when to use them, requires an understanding of assumptions used, strengths, and limitations of each method. This paper presents advantages and disadvantages through comparison of analytical vs simulation when reservoir characterization becomes progressively more complex (dip, layering, heterogeneity between injector/producer, and reservoir thickness).While there are many analytical models, three analytical models are used for this paper:Marx & Langenheim, Modified Neuman, and Jeff Jones.The simulator used was CMG Stars on single pattern on both 5 Spot and 9 Spot patterns and Case 6 of 9 patterns, 5-Spot. Results were obtained using 6 different cases of varying reservoir properties based on Marx & Langenheim, Modified Neuman, and Jeff Jones models.Simulation was also done on each of the 6 cases, using Modified Neuman steam rates and then on Jeff Jones Steam rates using 9-Spot and 5-Spot patterns.This was done on predictive basis on inputs provided, without adjusting or history matching on analog or historical performance.Optimization runs using Particle Swarm Optimization was applied on one case in minimizing SOR and maximize NPV. Conclusion from comparing cases is that simulation is needed for complex geology, heterogeneity, and changes in layering. Also, simulation can be used for maximizing economics using AI based optimization tool. While understanding limitations, the analytical models are good for quick looks such as screening, scoping design, some surveillance, and for conceptual understanding of basic steam flood on uniform geologic properties. This paper is innovative in comparison of analytical models and simulation modeling.Results that quantify differences of oil rate, SOR, and injection rates (Neuman and Jeff Jones) impact on recovery factors is presented.
Abstract The rate of penetration (ROP) was optimized using a particle swarm optimization algorithm for real-time field data to reduce drilling time and increase efficiency. ROP is directly related to drilling costs and is a major factor in determining mechanical specific energy, which is often used to quantify drilling efficiency. Optimization of ROP can therefore help cut down costs associated with drilling. ROP values were chosen from real-time field data, accounting for weight on bit, bit rotation, flow rate variation along with bit wear. A random forest regressor was used to find correlations between the dependent parameters. The parameters were then optimized for the given constraints to find the optimal solution space. The boundary constraints for the ROP function were determined from the real-time data. The function parameters were optimized using a particle swarm optimization algorithm. This is a meta-heuristic model used to optimize an objective function for its maximum or minimum within given constraints. The optimization method makes use of a population of solution particles which act as the particle swarm. These particles move collectively in the given solution space controlled by a mathematical model based on their position and velocity. This model makes use of the best-known solution for each particle and the global best position of the system to guide the swarm towards the optimal solution. The function was optimized for each well, providing optimal ROP values during real-time drilling. A fast drilling optimizer is crucial to automate and streamline the drilling process. This simultaneous optimization of ROP based on real-time data can be implemented during the process thereby increasing the efficiency of drilling as well as reducing the required drilling time.
Abstract The description of chemical kinetics is very import to the simulation of reactive transport for enhanced oil recovery (EOR). Characterizing petroleum ignition is especially important for simulation and prediction of In-Situ Combustion (ISC). In order to model crude oil oxidation reactions accurately, an experimental workflow is introduced to obtain kinetic parameters for ISC chemical reaction models. An optimization algorithm assists to match the reaction model parameters to the experimental results, and this validated model is used to predict ignition of crude oil in porous media. Apparent activation energy is estimated from ramped temperature oxidation experiments under several heating rates, including 1.5, 2.0, 2.5, 3.0, 5, 10, 15, and 20 °C/min. These experiments are separated into a small heating rates group (1.5, 2.0, 2.5, 3.0 °/min) and large heating rates (5, 10, 15, 20 °/min). The results show that experiments with small heating rates capture the details of reaction kinetics such that the estimated activation energy is more accurate, with the validated simulation model able to make accurate predictions for this particular crude oil. After matching the kinetics parameters, we predict the ignition conditions as a function of the air flow rates and the heat loss rates. The ignition envelope indicates that the window for air flow rates to ignite the oil decreases if the heat loss rate is high. Greater heat losses require more thermal energy to be released from the reaction to overcome losses and for ignition to occur. This leads to a narrower range of ignition air flow rates due to convective heat transfer. The uncertainty quantification results provide a confidence region for the ignition envelope impacted by the threshold temperature of the ignition criterion. The novelty of this work is the description of optimized combustion reaction models with rigorous experimental verification and uncertainty quantification for reactive transport simulations.
Wang, Shihao (Colorado School of Mines) | Di, Yuan (Peking University) | Winterfeld, Philip H. (Colorado School of Mines) | Li, Jun (King Fahd University of Petroleum and Minerals) | Zhou, Xianmin (King Fahd University of Petroleum and Minerals) | Wu, Yu-Shu (Colorado School of Mines) | Yao, Bowen (Colorado School of Mines)
Summary In this paper, we aim to enhance our understanding of the multiphysical processes in carbon dioxide (CO2)-enhanced-oil-recovery (EOR) (CO2-EOR) operations using a modeling approach. We present the development of a comprehensive mathematical model for thermal/hydraulic/mechanical (THM) simulation of CO2-EOR processes. We adopt the integrated-finite-difference method to simulate coupled THM processes during CO2-EOR in conventional and unconventional reservoirs. In our method, the governing equations of the multiphysical THM processes are solved fully coupled on the same unstructured grid. To rigorously simulate the phase behavior of a three-phase, nonisothermal system, a three-phase flash-calculation module, dependent on the minimization of Gibbs energy, is implemented in the simulator. The simulator is thus applicable to both miscible and immiscible flooding simulations under isothermal and nonisothermal conditions. We have investigated the effect of cold-CO2 injection on injectivity as well as on phase behavior. We conclude that cold-CO2 injection is an effective way to increase injectivity in tight oil reservoirs and reduces overriding effect in high-water-bearing reservoirs. Using the developed general simulation framework, we have discovered and studied several intriguing multiphysical phenomena that cannot be captured by commonly used reservoir simulators, including the temperature-decreasing phenomenon near the production well and the permeability-enhancement effect induced by the thermal unloading process. These phenomena can be captured only by the fully coupled multiphysical model. The novelty of this paper lies in its integration of multiple physical simulation modules to form a general simulation framework to capture realistic flow and transport processes during CO2 flooding, and in revealing the behavior of cold-CO2 injection under THM effects.
Summary Numerical simulation of coupled multiphase multicomponent flow and transport in porous media is a crucial tool for understanding and forecasting of complex industrial applications related to the subsurface. The discretized governing equations are highly nonlinear and usually need to be solved with Newton's method, which corresponds with high computational cost and complexity. With the presence of capillary and gravity forces, the nonlinearity of the problem is amplified even further, which usually leads to a higher numerical cost. A recently proposed operator-based linearization (OBL) approach effectively improves the performance of complex physical modeling by transforming the discretized nonlinear conservation equations into a quasilinear form according to state-dependent operators. These operators are approximated by means of a discrete representation on a uniform mesh in physical parameter space. Continuous representation is achieved through the multilinear interpolation. This approach provides a unique framework for the multifidelity representation of physics in general-purpose simulation. The applicability of the OBL approach was demonstrated for various energy subsurface applications with multiphase flow of mass and heat in the presence of buoyancy and diffusive forces. In this work, the OBL approach is extended for multiphase multicomponent systems with capillarity. Through the comparisons with a legacy commercial simulator using a set of benchmark tests, we demonstrate that the extended OBL scheme significantly improves the computational efficiency with the controlled accuracy of approximation and converges to the results of the conventional continuous approach with an increased resolution of parametrization. Introduction Numerical simulation, a tool developed by combining physics, mathematics, and computer programming, is an efficient way to understand the complex fluid flow in subsurface reservoirs with applications to the evaluation of hydrocarbon recovery, performance analysis, and various optimization problems (Todd et al. 1972; Spillette et al. 1973; Thomas and Thurnau 1983). It involves solving the partialdifferential equations governing coupled multiphase flow and transport in porous media with highly nonlinear physics (Aziz and Settari 1979; Coats et al. 1995).