bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accurate Production Forecast and Productivity Decline Analysis Using Coupled Full-Field and Near-Wellbore Poromechanics Modeling
Authors: Li Yan , Wang Bin , Wang Jiehao , Zaki Karim , Wu Ruiting , Barnum Bradley , Rijken Peggy , Guyaguler Baris 
Published: March 2023

Abstract
Productivity Index (PI) decline is caused by different mechanisms in both the wellbore region and the far field. The damages in the wellbore region can be simulated by detailed wellbore modeling. To incorporate field pressure evolution and impact on well productivity, a newly developed full-field and near-wellbore poromechanics coupling scheme is used to model PI degradation against time. Near wellbore damages, field and well interactions are identified when applying the coupling scheme for a deep water well. History matching, production forecast and safe drawdown limits are derived for operation decisions.
The coupling scheme is applied in a deep water well for history matching and drawdown strategy evaluation. The field model containing reservoir properties and operation conditions is coupled to multiple near-wellbore models which have completion and detailed wellbore geometry. During history matching, the field model is explicitly coupled with near-wellbore models. Field pressure is dynamically mapped into near-wellbore models and the PI multiplier in the field model for the given well(s) is updated based on near wellbore flow paths simulated in the near-wellbore models. Well productivity changes are accurately represented in the reservoir model for history matching and the production forecast.
Using the coupling scheme, we successfully history matched well production and the PI trend. PI decline damage mechanisms, such as perforation efficiency, fines migration, fracture connectivity, fracture conductivity, compaction, are modeled in the coupling scheme. At each coupling step, well productivity calculated in near-wellbore model(s) is updated as a PI multiplier for the well(s) in the field model. History matching results showed the dominant PI decline factors are perforation efficiency, fines migration, and fracture conductivity. Near wellbore perforation collapse reduces flowing area and changes flow path. Fines migration is observed at the high velocity region. It damages the permeability around high velocity flow path and the damage accumulates with time. Maintaining perforation efficiency and conducting a stimulation job to remove fines damage could mitigate PI decline.
Production forecast is performed with different drawdown rates. Results showed that there is no further PI decline with low drawdown. For high drawdown rate, PI could continue to decline and PI decline slope is related with drawdown pressure.
Guided Deep Learning Manifold Linearization of Porous Media Flow Equations
Authors: Dall'Aqua Marcelo J. , Coutinho Emilio J. R. , Gildin Eduardo , Guo Zhenyu , Zalavadia Hardik , Sankaran Sathish 
Published: March 2023

Abstract
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) augment the state-space to yield a bi-linear system, and (2) an autoencoder based on a deep neural network to linearize physics reservoir equations in a reduced manifold employing 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: our training process is performed in three steps: we initially train the autoencoder, then we use a "conventional" MOR (Dynamic Mode Decomposition) as an initializer for the final 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 multi-scale 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 two-dimensional two-phase (oil and water) reservoir subject to a waterflooding plan with three wells (one injector and two producers) with speed ups around 100 times faster and accuracy in the order of 1-3 percent on the pressure and saturations predictions. It is worthwhile noting that this method is a non-intrusive data-driven method since 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. This is the first work that utilizes the Koopman operator for linearizing the system with controls to the author's knowledge. As with any ROM 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.
Implementing a Hardware Agnostic Commercial Black-Oil Reservoir Simulator
Authors: Szyndel Matthew , Lemon Christopher , de Brito Dias Daniel , Dodds Eamon , Khramchenkov Eduard , Rinco Simone , Sheth Soham , Tene Matei , Han Choongyong , Shi Xundan , Wolfsteiner Christian , Cao Hui , Liao Terrence , Sekachev Michael , Zaydullin Rustem 
Published: March 2023

Abstract
Commercial reservoir simulators have traditionally been optimized for parallel computations on central processing units (CPUs). The recent advances in general-purpose graphics processing units (GPUs) have provided a powerful alternative to CPU, presenting an opportunity to significantly reduce run times for simulations. Realizing peak performance on GPU requires that GPU-specific code be written, and also requires that data are laid out sympathetically to the hardware. The cost of copying data between the CPU memory and GPU memory at the time of this writing is egregious. Peak performance will only be realized if this is minimized.
In paper Cao et al., 2021, the authors establish approaches to enable a simulator to give excellent performance on a CPU or GPU, with the same simulation result using either hardware. We discuss how their prototype was generalized into high-quality, maintainable code with applicability across a wide range of models.
Different parts of a reservoir simulator benefit from different approaches. A modern, object-oriented simulator requires components to handle initialization, property calculation, linearization, linear solver, well and aquifer calculations, field management, and reporting. Each of these areas will present architectural challenges when broadening the scope of the simulator from CPU only to supporting CPU or GPU. We outline these challenges and present the approaches taken to address them. In particular, we discuss the importance of abstracting compute scheduling, testing methods, data storage classes, and associated memory management to a generic framework layer.
We have created a high-quality reservoir simulator with the capacity to run on a CPU or GPU with results that match to within a very small tolerance. We present software engineering approaches that enable the team to achieve and maintain this in the future. In addition, we present test outcomes and discuss how to achieve excellent performance.
To our knowledge, no simulator capable of both CPU simulation and full GPU simulation (meaning simulation with no copies of full grid-size data for purposes other than reporting) has been presented. We will present novel software approaches used to implement the first such commercial simulator.
A Quasi-Newton Method for Well Location Optimization Under Uncertainty
Authors: Eltahan Esmail , Alpak Faruk Omer , Sepehrnoori Kamy 
Published: March 2023

Abstract
Subsurface development involves well-placement decisions considering the highly uncertain understanding of the reservoir in the subsurface. The simultaneous optimization of a large number of well locations is a challenging problem. Conventional gradient-based methods are known to perform efficiently for well-placement optimization problems when such problems are translated into real-valued representations, and special noisy objective function handling protocols are implemented. However, applying such methods to large-scale problems may still be impractical because the gradients of the objective function may be too expensive to compute for realistic applications in the absence of the implementation of the adjoint method. In this paper, we develop a quasi-Newton method based on the stochastic simplex approximate gradient (StoSAG), which requires only objective-function values.
We have implemented the BFGS quasi-Newton updating algorithm together with line-search and trust-region optimization strategies. We have developed a novel approach to enhance the accuracy of StoSAG gradients by modifying their formulations to enable exploiting the objective-function structure. The objective function is treated as a summation of element functions, each representing the contribution from an individual well at distinct time steps. Instead of working with a single value for the gradient, we treat it as a sum of sub-gradients. We then utilize problem-specific prior knowledge to form a matrix W that acts on the sub-gradients. The entries of W vary from 0 to 1 and are proportional to the interference effects the neighbouring wells have on each other. We define those entries (or weights) based on the radii of investigation around the wells. The BFGS-StoSAG variants are demonstrated on a realistic synthetic case with 26 wells while varying the average reservoir permeability.
We first show that the BFGS algorithm delivers promising performance as in many cases it results in the most rapid improvement for the objective-function values (especially in early iterations). Further testing results confirm that the trust-region protocol is more effective than the line-search protocol for accelerating convergence with BFGS. Although the objective function is not always continuously differentiable with respect to well locations, the StoSAG variants overcome this challenge owing to their smoothing properties of approximate gradients. Moreover, we show that using our gradient correction procedures on the well-location optimization problem results in drastic acceleration in convergence indicating enhancement in the StoSAG gradient approximation quality.
Coupled CO2 Injection Well Flow Model to Assess Thermal Stresses under Geomechanical Uncertainty
Authors: Andrianov Nikolai , Amour Frédéric , Hajiabadi Mohammad Reza , Nick Hamidreza M. , Haspang Martin Patrong 
Published: March 2023

Abstract
We develop a two-phase transient non-isothermal wellbore flow model, augmented with a radial heat conduction in the annulus, casing, and the reservoir. Using the available data for a saline aquifer in Denmark, we build a one-dimensional geomechanical well model and assess the stresses at the wellbore wall using the analytical Kirsch formula. Using the temperature at the wellbore wall, we calculate the corresponding thermal stresses. Furthermore, we assess the impact of the uncertainty in thermal expansion coefficients on the magnitudes of thermal stresses. For the cases considered, the magnitude of the changes in the critical pressure and in the fracture pressure with and without thermal stresses does not exceed 3%.
High Performance Integrated Asset Modeling: A Giant Gas Field Case Study
Authors: Rosa Andrea , Wiegand Klaus , Mukundakrishnan Karthik , Pizzolato Alberto , Panfili Paola , Cominelli Alberto , Picone Silvia , Ruffino Rosario , Patacchini Leonardo 
Published: March 2023

Abstract
We present the development of a field-scale simulation tool coupling the model of Zohr, a super giant deepwater gas reservoir requiring a multi-million active cells grid with dual porosity/dual permeability formulation, to its gathering network. Deepwater field development often relies on a complex subsea gathering infrastructure, possibly evolving over time and leading to a complex topology. Here, facilities route the multiphase production stream to an onshore compression plant throughout a network with bifurcations.
To perform integrated modeling of such assets, we have developed a Facility Network Solver (FNS) with flexible topology, whose formulation relies on a graph representation with continuity equations at nodes and tabulated constitutive equations at edges. FNS was designed to be integrated with the industrial grade GPU-based reservoir simulator used in the company, both jointly developed by Eni and Stone Ridge Technology, with emphasis on preserving usability and simulation performance.
The correctness of FNS results in the presence of bifurcations was thoroughly assessed in standalone mode, through benchmarking against what is today considered a reference commercial network solver. FNS integration with the reservoir simulator enables forecasts where the back-pressure is taken into account. Engineers can better assess the viability of different development scenarios, including dynamic upgrades to the network topology, using reservoir simulation workflows they are well accustomed to. In particular, a single tool replaces often heterogeneous associations of third-party software, without impacting simulation time.
Sensitivity analyses were performed on both coupling frequency and location. It was concluded that periodic coupling at the well-head was a satisfactory setting, yielding negligible performance overhead with respect to standalone reservoir simulations, thus enabling the integrated model to be used routinely as the sole simulation model.
An Adaptive Coloring Scheme for Graphics Processing Unit Preconditioners
Authors: Lemon Christopher , Cao Hui , Szyndel Matthew , Khramchenkov Eduard 
Published: March 2023

Abstract
A single modern graphics processing unit (GPU) typically has the memory bandwidth equivalent to many central processing unit (CPU) nodes. This makes GPU hardware appealing for linear solvers that tend to require high memory bandwidth and fast inter-core communication. Reservoir simulators are designed to handle a wide range of simulation models, and to obtain peak performance the linear solver must be well suited to the resulting linear systems. This fact can lead to disappointing performance when shifting the linear solver from CPU to GPU. To fully utilize the capabilities of the latest GPU devices, we must transition from coarse-grained to fine-grained parallel preconditioners. To enable such high levels of parallelism in the linear solver a common approach is to employ a multicolor reordering of the linear system. Depending on the specific properties of the simulation model, this process can cause a significant weakening of the parallel preconditioner, resulting in much slower convergence. In some situations, this slow convergence can cause an order of magnitude increase in the linear iteration count, and result in the GPU linear solver performing worse than the CPU version. In this paper we analyze the impact on performance of employing different coloring schemes for different simulation models and we identify how the coloring can be automatically adapted for the properties of each simulation model. In this way, the performance improvements expected on the GPU can be realized for a wider range of simulations.
Phase-Field Simulation of Near-Wellbore Nucleation and Propagation of Hydraulic Fractures in Enhanced Geothermal Systems (EGS)
Authors: Fei Fan , Costa Andre , Dolbow John E. , Settgast Randolph R. , Cusini Matteo 
Published: March 2023

Abstract
Enhanced geothermal systems (EGS) rely on the artificial creation of fractures (i.e., hydraulic fractures) to enhance the permeability of the formation which would, otherwise, be too low to allow for fluid circulation. Hydraulic fracturing involves complex nucleation and propagation processes, which are key to the analysis and prediction of well productivity. Numerical simulations are commonly employed to understand the specific mechanisms behind nucleation and propagation of hydraulic fractures. However, most numerical approaches face tremendous challenges in tracking and accommodating the evolving fracture geometry, especially when curved and branched fractures occur. The phase-field method can overcome this obstacle, as it can model fracture propagation without the need for tracking the fracture tip nor for remeshing. However, the most common phase-field formulation is unable to accurately capture fracture nucleation. In this work, we develop a new phase-field approach for hydraulic fracturing that accounts for fracture nucleation due to the strengths of geologic material and the existence of small defects. Verification examples show that the proposed formulation can accurately predict near-wellbore nucleation and propagation of hydraulic fractures and the wellbore breakdown pressure. Simulation of a three-dimensional wellbore problem further demonstrates the efficiency of the proposed phase-field method in handling fracture nucleation and propagation.
Water Injection Optimization to Prevent Reservoir Souring
Authors: Izgec Bulent 
Published: March 2023

Abstract
This paper presents a three-stage integrated workflow using thermal/ geomechanical and thermal/ chemical simulation models to prevent reservoir souring during high concentration sulphate water injection. Reservoir souring takes place when sulphate reducing bacteria (SRB) is activated by cold injection water and gains access to high concentrations of sulphate along with volatile fatty acids (VFA) and/ or hydrocarbons (BTEX) as nutrients.
In the first stage a thermal/ chemical reservoir simulation model is history matched to quantify the changes in nutrient distribution around the injector. Earlier in the life of the well high volumes of low sulphate content water injection washes away the VFA/ BTEX in the near wellbore area creating a nutrient depleted zone for SRB.
In the second stage, a detailed wellbore model is coupled with a thermal/ geomechanical reservoir simulator and history matched to predict the downhole injection water temperature. Because the size of the cooled region changes with injection induced fractures extending from the wellbore into the formation, a thermal/ geomechanical simulation model is preferred.
The final stage of the workflow takes place later in the life of the injector when injection water has high sulphate concentrations. During this stage, the size of the formation cooling around the wellbore is constrained within the nutrient depleted zone with dynamic injection rate optimization to stop bacterial activity and accordingly reservoir souring.
Case studies from deepwater Gulf of Mexico reservoirs demonstrate how the workflow has been successfully operationalized to eliminate reservoir souring during high concentration sulphate water injection. Four years after the initiation of high sulphate concentration water injection, lack of sour gas production from the fields points to the robustness of the method. As suggested by the simulation models SRB activity is not promoted if cooling is only limited to the nutrient depleted zone.
This method helps quantify an upper limit for the sulphate concentration in injection water that will not cause reservoir souring. Chemical injections and/ or adjustments to the existing facility design is not required.
Smooth Formulation for Three-Phase Black-Oil Simulation with Superior Nonlinear Convergence
Authors: Jiang Jiamin , Wen Xian-Huan 
Published: March 2023

Abstract
Black-oil simulations with phase changes are challenging, because of the complex interactions between the different components and the equilibrium behavior of the phases. The common method for solving this type of nonlinear problem is to use a fully-implicit approach. However, the conventional black-oil model can lead to difficulties with converging using Newton's method. Discontinuities in discrete system can occur when a phase transition happens, which can lead to oscillations or even failure of the Newton iterations. The goal is to design a smoothing formulation that eliminates any sudden changes in properties or discontinuities that occur during phase transitions.
We first employ a compositional formulation based on K-values to describe the standard black-oil model. Next, the coupled system is reformulated such that the discontinuities are carried over to the phase equilibrium model. In this manner, a single, succinct non-smooth equation is obtained, which allows for deriving a smoothing approximation. A mixed complementarity problem (MCP) for phase-equilibrium in the area of chemical process modeling served as the foundation for the reformulation. The new formulation is non-intrusive and simple to implement, requiring minor changes to current black-oil simulator frameworks.
We analyze and demonstrate that phase changes lead to the changes of fluid-properties and discrete system, under the conventional black-oil formulation. By comparison, the newly proposed formulation uses a smoothing parameter to ensure smooth transitions of variables between the phase regimes. It also generates unique solutions that are valid for all three phases. Several complex heterogeneous problems are tested. The conventional black-oil model experiences many time-step cuttings and wasting nonlinear iterates. On the contrary, the smoothing model exhibits excellent convergence behaviors. Overall, the new formulation addresses the issues with convergence caused by phase-changes, while barely affecting solution results.