Summary Erosion and forces on rams may prevent a blowout preventer (BOP) from sealing a well. Analyzing the flow field throughout a BOP may provide insight into these flowing effects on the inability of a BOP to seal the well. 3D transient simulation of fluid flow throughout closing BOP fluid domains is demonstrated using computational fluid dynamics (CFD). Simulation may be used to analyze the transient stress, pressure, and velocity fields throughout a BOP domain as it is closing. Many challenges exist in simulating a closing BOP using CFD, including boundary conditions and treatment of dynamic meshing. Solutions to those challenges are presented in this work. CFD simulations are carried out using ANSYS Fluent v19.2 (ANSYS, Canonsburg, Pennsylvania, USA). For inlet boundary conditions to the CFD domain, the CFD simulations are explicitly coupled with a 1D wellbore simulator. The 1D wellbore simulator provides a connection between the BOP and constant pressure reservoir. Numerical instability is present during this coupling process. An implementation for dealing with this instability is presented. An example validation case is presented to demonstrate the accuracy of CFD for pressure fields throughout valves. A second 2D axisymmetric case is shown to demonstrate the meshing and coupling simulation process. A third case, simulation through a 3D shear geometry is then presented to show the applicability of the process to a more complex geometric design. Velocity and stress fields are plotted to show the practicality of CFD in analyzing the probable causes of failure in BOP closures.

Summary In the oil industry, chemicals can improve oil production by mobilizing trapped and bypassed oil. Such processes are known as chemical-enhanced oil recovery (CEOR). Surfactants and polymers are important chemicals used in CEOR with different mechanisms to improve oil recoveries, such as reduction in residual saturation, oil solubilization, and mobility control. However, both surfactant and polymer may increase the cost of oil production, making optimizing these processes essential. Reservoir simulators are tools commonly used when performing such field optimization. The simulation of surfactant flooding processes has been historically performed with the implicit pressure explicit composition (IMPEC) approach. The injection of surfactants requires modeling the brine/oil/microemulsion phase behavior along with other processes, such as capillary desaturation and retention. The microemulsion phase behavior and the complex relative permeability behavior can lead to convergence issues when using fully implicit (FI) schemes. Only recently, the FI approach has been efficiently applied to simulate this process using new modeling. The adaptive implicit method (AIM) can combine the benefits of the FI and IMPEC approaches by dynamically selecting the implicitness level of gridblocks in the domain. This work presents a new AIM in conjunction with recently developed models to mitigate discontinuities in the microemulsion relative permeabilities and phase behavior. The approach presented here considers the stability analysis method as a switching criterion between IMPEC and FI. To the best of our knowledge, the approach presented here is the first AIM to consider the brine/oil/microemulsion three-phase flow in its conception. The new approach uses the finite volume method in conjunction with Cartesian grids as spatial discretization and is applied here for field-scale problems. The new approach is tested for polymer flooding and surfactant-polymer (SP) flooding for problems with several active cells ranging from about a hundred thousand to almost a million. The AIM approach was compared with the FI and IMPEC approaches and displayed little variation in the computational performance despite changes in the timestep size. The AIM also obtained the fastest performance for all cases, especially for SP flooding cases. Furthermore, the results here suggest that the gap in performance between the AIM and FI seems to increase as the number of gridblocks increases.

Summary This study focuses on carbon capture, utilization, and sequestration (CCUS) via the means of nonlinearly constrained production optimization workflow for a CO2-enhanced oil recovery (EOR) process, in which both the net present value (NPV) and the net present carbon tax credits (NPCTC) are bi-objectively maximized, with the emphasis on the consideration of injection bottomhole pressure (IBHP) constraints on the injectors, in addition to field liquid production rate (FLPR) and field water production rate (FWPR), to ensure the integrity of the formation and to prevent any potential damage during the life cycle injection/production process. The main optimization framework used in this work is a lexicographic method based on the line-search sequential quadratic programming (LS-SQP) coupled with stochastic simplex approximate gradients (StoSAG). We demonstrate the performance of the optimization algorithm and results in a field-scale realistic problem, simulated using a commercial compositional reservoir simulator. Results show that the workflow can solve the single-objective and bi-objective optimization problems computationally efficiently and effectively, especially in handling and honoring nonlinear state constraints imposed onto the problem. Various numerical settings have been experimented with to estimate the Pareto front for the bi-objective optimization problem, showing the trade-off between the two objectives of NPV and NPCTC. We also perform a single-objective optimization on the total life cycle cash flow, which is the aggregated quantity of NPV and NPCTC, and quantify the results to further emphasize the necessity of performing bi-objective production optimization, especially when used in conjunction with commercial flow simulators that lack the capability of computing adjoint-based gradients.

Summary Surrogate models play a vital role in reducing computational complexity and time burden for reservoir simulations. However, traditional surrogate models suffer from limitations in autonomous temporal information learning and restrictions in generalization potential, which is due to a lack of integration with physical knowledge. In response to these challenges, a physics-informed spatial-temporal neural network (PI-STNN) is proposed in this work, which incorporates flow theory into the loss function and uniquely integrates a deep convolutional encoder-decoder (DCED) with a convolutional long short-term memory (ConvLSTM) network. To demonstrate the robustness and generalization capabilities of the PI-STNN model, its performance was compared against both a purely data-driven model with the same neural network architecture and the renowned Fourier neural operator (FNO) in a comprehensive analysis. Besides, by adopting a transfer learning strategy, the trained PI-STNN model was adapted to the fractured flow fields to investigate the impact of natural fractures on its prediction accuracy. The results indicate that the PI-STNN not only excels in comparison with the purely data-driven model but also demonstrates a competitive edge over the FNO in reservoir simulation. Especially in strongly heterogeneous flow fields with fractures, the PI-STNN can still maintain high prediction accuracy. Building on this prediction accuracy, the PI-STNN model further offers a distinct advantage in efficiently performing uncertainty quantification, enabling rapid and comprehensive analysis of investment decisions in oil and gas development.

CNOOC Research Institute Ltd Summary History matching is a crucial process that enables the calibration of uncertain parameters of the numerical model to obtain an acceptable match between simulated and observed historical data. However, the implementation of the history-matching algorithm is usually based on iteration, which is a computationally expensive process due to the numerous runs of the simulation. To address this challenge, we propose a surrogate model for simulation based on an autoregressive model combined with a convolutional gated recurrent unit (ConvGRU). The proposed ConvGRU-based autoregressive neural network (ConvGRU-AR- Net) can accurately predict state maps (such as saturation maps) based on spatial and vector data (such as permeability and relative permeability, respectively) in an end-to- end fashion. Furthermore, history matching must be performed multiple times throughout the production cycle of the reservoir to fit the most recent production observations, making continual learning crucial. To enable the surrogate model to quickly learn recent data by transferring experience from previous tasks, an ensemble-based continual learning strategy is used. Together with the proposed neural network-based surrogate model, the randomized maximum likelihood (RML) is used to calibrate uncertain parameters. The proposed method is evaluated using 2D and 3D reservoir models. For both cases, the surrogate inversion framework successfully achieves a reasonable posterior distribution of reservoir parameters and provides a reliable assessment of the reservoir's behaviors. Introduction The goal of history matching is to calibrate the uncertain parameters of the reservoir using observed reservoir behaviors, so that the simulation results can reproduce the production history and subsequently provide a reliable prediction for reservoir management and optimization. This is typically an ill-posed inverse problem, which implies that more than one combination of parameters can lead to an acceptable match for reproducing past reservoir behaviors. Well production data are commonly used to represent the observed reservoir behavior. However, the information content in most sets of production data is fairly low because of the limited number of observation locations and the diffusive nature of the flow (Oliver and Chen 2011). This results in poorly constrained reservoir parameters between wells.

ABSTRACT: Energy considerations are essential for the evaluation of violent failures which are commonly encountered as mining goes deeper. To address the relationships among different energy components, a series of numerical models were conducted by using 3DEC and a script was developed for energy visualization. The theoretical and numerical results of the ratio between the released kinetic energy and the excavated strain energy were compared under elastic and plastic models. The distribution of stored elastic strain energy and dissipated plastic strain energy in the vicinities of openings with different shapes were also investigated. Furthermore, the efficiency of a latest destressing method as a proactive measure for seismic management was evaluated based on the energy redistribution patterns. This research can improve the understanding of the energy evolution near excavations and contribute to the evaluation of burst-proneness of excavations as well as effectiveness of rockburst mitigation measures. INTRODUCTION When underground mining continues to reach deep deposits, significant energy changes take place in rock mass and cause excavation instabilities such as rock bursts (e.g., Cook et al. 1966 and Zhou et al. 2018). The involved brittle failures cannot be represented accurately by the traditional failure indictors such as deformation and stress. The acquisition of the energy variations is essential to describe these violent failure process (Wang et al. 2021). In view of the importance of energy considerations, more and more studies have been conducted through theoretical analysis, numerical simulation, and laboratory experiments in the past years. Salamon (1984) conducted theoretical analysis on the relationships among energy components during mining by using an elastic model. Different criteria for rockburst proneness of rock mass are proposed based on the strain-stress curves, especially the post-failure behavior obtained from laboratory experiments such as strain energy storage index (Kidybinski 1981 and Gong et al. 2019), potential energy of elastic strain (Wang & Park 2001 and Tajdus et al. 2014), brittleness index (Keneti & Sainsbury 2018) and so on. Meanwhile, several energy indices are introduced in the analysis of numerical simulation results including strain energy density (SED) (Xu et al. 2003 and Weng et al. 2017), energy release rate (ERR) (Cook 1966), local energy release rate (LERR) (Jiang et al. 2010) and excess energy (Khademian & Ozbay 2019).

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 213089, “Optimizing Artificial Lift Timing and Selection Using Reduced-Physics Models,” by Hardikkumar Zalavadia, SPE, Metin Gokdemir, and Utkarsh Sinha, SPE, Xecta Digital Labs, et al. The paper has not been peer reviewed. _ The complete paper presents an artificial lift timing and selection (ALTS) methodology based on a hybrid data-driven and physics-based work flow. The proposed method predicts future unconventional reservoir inflow performance relationship (IPR) consistently and allows for continuous evaluation of ALTS scenarios in unconventional reservoirs with multiple lift types and designs. Continuous use of this process has been shown to improve production, reduce deferred production, and extend the life of lift equipment. Introduction The intent of the work flow is to maximize the positive economic impact of a well. The authors write that, to their knowledge, incumbent methods do not include the effect of subsurface performance. In the proposed approach, feedback is injected to the reservoir and a closed-loop response is obtained implicitly because of the selection of artificial lift type and operational parameters of that type. The hybrid reservoir modeling methodology is based on identifying transient well performance (TWP). The method is based on a novel formulation that combines diffusive time of flight (DTOF), succession of pseudosteady-state material balance, and transient productivity-index (PI) concepts for estimating dynamic reservoir deliverability. (Equations associated with these processes are provided in the complete paper.) This is combined with well-deliverability estimation for different artificial lift methods and their operating parameters to perform continuous nodal analysis and forecast phase rates using novel PI-based forecasting techniques. TWP Analysis This work establishes a practical method to estimate transient-well PI for every well at field scale to understand unconventional well performance. This transient PI then can be used in a variety of applications, including well forecasting, artificial lift planning, production optimization, and field development planning. The complete paper is focused primarily on using the TWP work flow for optimizing ALTS in unconventional reservoirs that is practical in its application at a field scale. Fig. 1 depicts the suggested work flow, which combines reduced-physics and data-driven methodologies to describe well performance over a series of steps. For TWP calculations, material balance is used through a sequence of pseudosteady states on the drainage volume evolution of a closed system.

Summary Integrated reservoir studies for performance prediction and decision-making processes are computationally expensive. In this paper, we develop a novel linearization approach to reduce the computational burden of intensive reservoir simulation execution. We achieve this by introducing two novel components: (1) augmention of the state-space to yield a bilinear system and (2) an autoencoder based on a deep neural network to linearize physics reservoir equations in a reduced manifold using a Koopman operator. Recognizing that reservoir simulators execute expensive Newton-Raphson iterations after each timestep to solve the nonlinearities of the physical model, we propose “lifting” the physics to a more amenable manifold where the model behaves close to a linear system, similar to the Koopman theory, thus avoiding the iteration step. We use autoencoder deep neural networks with specific loss functions and structure to transform the nonlinear equation and frame it as a bilinear system with constant matrices over time. In such a way, it forces the states (pressures and saturations) to evolve in time by simple matrix multiplications in the lifted manifold. We also adopt a “guided” training approach, which is performed in three steps: (1) We initially train the autoencoder, (2) then we use a “conventional” model order reduction (MOR) as an initializer for the final (3) full training, when we use reservoir knowledge to improve and to lead the results to physically meaningful output. Many simulation studies exhibit extremely nonlinear and multiscale behavior, which can be difficult to model and control. Koopman operators can be shown to represent any dynamical system through linear dynamics. We applied this new framework to a 2D two-phase (oil and water) reservoir subject to a waterflooding plan with three wells (one injector and two producers) with speedups around 100 times faster and accuracy in the order of 1% to 3% on the pressure and saturation predictions. It is worthwhile noting that this method is a nonintrusive data-driven method because it does not need access to the reservoir simulation internal structure; thus, it is easily applied to commercial reservoir simulators and is also extendable to other studies. In addition, an extra benefit of this framework is to enable the plethora of well-developed tools for MOR of linear systems. To the authors’ knowledge, this is the first work that uses the Koopman operator for linearizing the system with controls. As with any MOR method, this can be directly applied to a well-control optimization problem and well-placement studies with low computational cost in the prediction step and good accuracy.

_ The interaction between the structural response and hydrodynamic loading (hydroelasticity) must be considered for design and operation purposes of high-speed planing craft made of composites that are prone to frequent water impact (slamming). A computational approach was proposed to study the hydroelastic slamming of a flexible wedge. The computational approach is a loose two-way coupling between a Wagner-based hydrodynamic solution and a linear finite element plate model. Verification and validation (V&V) was performed on this coupled model. It was found that the model overpredicts rigid-body/spray root kinematics by <15% and hydrodynamic loading/ structural response by <26%. Introduction One of the primary constraints on the operational envelope of high-speed craft is slamming (water impact). Slamming occurs between the hull body and the water surface when a portion/whole of the craft exits the water and then reenters at high-enough velocity (Lloyd 1989; Faltinsen 2005). The frequent water impacts, which work like “water hammers,” along with their induced acceleration pose great jeopardy on hull structures as well as crew and instrument on-board (Yamamoto et al. 1985; Ensign et al. 2000; Hirdaris et al. 2014). With the growing use of lightweight materials, the interaction between the structural deformation and the hydrodynamic loading (hydroelasticity) becomes more prevalent. The current design criteria of high-speed craft are based on empirical procedures with no regard to hydroelasticity due to the lack of understanding of this complex phenomenon (DNV 2013; ABS 2016). Therefore, a better comprehension of hydroelastic slamming is the first step to designing more high-performance craft (Fu et al. 2014; Judge et al. 2020).

China Oilfield Services Limited, Tianjin Summary The embed-to- control (E2C) framework provides a new deep-learning- based reduced-order modeling framework for much faster subsurface flow predictions than traditional simulation. However, the previous E2C model entails a large number of model parameters, which limits its applicability to large-scale cases. In addition, the previous E2C model has not been applied to a gas-driven subsurface system or well-control optimization. In this work, we make several improvements to the previous E2C framework for more complex and largerscale problems. First, we reduce the output dimension of the middle layers by increasing the number of downsampling layers and using the depth-wise separable (DWS) convolution techniques in the deconvolution operation. Second, we use the global average pooling (GAP) technique to reduce the model parameters. Third, we apply an "add" operation in the skip connection to fuse the features. The improved E2C surrogate model is applied to a high-dimensional gas system with flow driven by six wells operating under time-varying control specifications. In this case, we can reduce the graphics processing unit (GPU) memory usage from 19.22 GB to 2.57 GB. In the training process, a total of 160 high-fidelity simulations are performed offline, out of which 130 simulation results with partial time sequence are used for training the E2C surrogate model, which takes about 46 hours on an RTX 3090 GPU. The trained model is shown to provide accurate production forecasts under various well control scenarios during the prediction period. The online computations from our E2C model are about 6.5 seconds per case, which achieves a speedup of more than 500 factors to corresponding full-order simulations, which take about 1 hour per run. Finally, the improved E2C model, in conjunction with a particle swarm optimization (PSO) technique, is applied to optimize the injection well strategies of an oil-gas- water field case with 189 wells (i.e., 96 producers and 93 injectors). Due to the significant speedup and high accuracy of the improved surrogate model, it is shown that improved well-control strategies can be efficiently obtained.