bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dissipation-Based Nonlinear Solver for Efficient Implicit Simulation of Compositional and Discrete Fracture Models
Authors: Jiang Jiamin , Pan Huanquan 
Published: March 2023

Abstract
The solution of nonlinear equation-system resulting from the Fully Implicit Method (FIM) remains a challenge for numerically simulating multi-phase flow in subsurface fracture media. The Courant numbers can vary orders of magnitude across discrete fracture- matrix (DFM) models because of the high contrasts in the permeability and length-scale between matrix and fracture. The standard Newton solver is usually unable to converge for big timestep sizes or poor initial guesses.
Limited research has been conducted on nonlinear solver techniques for multi-phase compositional flow-transport in fractured media. We make an extension of a new dissipation-based continuation (DBC) algorithm to compositional and DFM models. Our goal is to prevent time-step cuttings and sustain efficient time-stepping for FIM. The DBC algorithm builds a homotopy of the discretized conservation equations through the addition of numerical dissipation terms. We introduce a continuation parameter for controlling the dissipation and ensuring that accuracy of the computed solution will not be reduced. Under the nonlinear framework of DBC, general dissipation operators and adaptive methods are developed to provide the optimal dissipation matrix for multiphase compositional hyperbolic systems.
We assess the new nonlinear solver through multiple numerical examples. Results reveal that the damped-Newton solver suffers from serious restrictions on timestep sizes and wasted iterations. In contrast, the DBC solver provides excellent computational performance. The dissipation operators are able to successfully resolve the main convergence difficulties. We also investigate the impact of star-delta transformation which removes the small cells at fracture intersections. Moreover, we demonstrate that an aggressive time-stepping does not affect the solution accuracy.
A Numerical Study of Selective Inorganic Precipitation in Fractures to Geoengineer Resilient and Secure Underground Storage Sites
Authors: Fan Shijun , Younis Rami M. , Lu Jun 
Published: March 2023

Abstract
A process to geoengineer hydrodynamic seals is investigates numerically. It is well-documented that upon mixing, Barium- and sulfate-rich brines lead to rapid deposition of impermeable and stable scale. When deposited within open fractures, the mineral scale also holds significant tensile strength. This work investigates the intentional injection of these incompatible ions into faulted formations and around caverns to create mechanically resilient hydrodynamic seals. A coupled hydrological-mechanical-chemical HMC model is calibrated to available bench-scale experimental observations. The model attains a close match with experimental core-flooding observations by fitting reaction rate, solubility, precipitation, and nonequilibrium formation blockage. It is then applied to conduct field-scale investigations of various synthetic operational injection scenarios. The calibrated simulations suggest that the saturation index of the injected brines, injection patterns, and rates have a first-order impact on seal creation and uniformity. With inorganic precipitation, the critical injection rates to triggering seismic nucleation may be reinforced by one to two orders in magnitude due to cohesive mechanical healing.
Integrating Geomechanics Studies to Shale and Tight Phased Development - Application to Delaware Basin
Authors: Khebzegga Ouassim , Chen Yuguang , Rey Alvaro, J , Wang Bin 
Published: March 2023

Abstract
Parent and child wells interference is a major concern in the development of shale and tight reservoirs. Oil and gas operators typically aim to prevent fractures interference during the child well’s stimulation, which connects the parent and child well’s fracture networks. Another aspect of the parent-child well interference lies in the impact of parent-well depletion on child-well stimulation, often leading to the underperformance of child-well production. For these circumstances, reservoir simulation that combines flow and geomechanics is required to predict child-well fractures growth. To achieve this goal, a new workflow combining flow (EDFM), geomechanics (single porosity) and fracturing was developed to predict the reservoir stress change, the growth of the child-well fractures, and the production of the parent and child wells. As one of the applications, this tool was applied to a Delaware basin reservoir and enabled the asset team to better design the pad for the child wells. Multiple scenarios were analyzed for eight child wells located between two parent wells. Using this tool, we were able to predict the asymmetric growth of the fractures in the direction of the parent wells for child wells that were close enough to their parent wells. The impact of this fractures’ asymmetric growth on the production of the child wells was also quantified, based on which a better configuration of child wells was recommended to mitigate the depletion effect of parent wells.
Integrating Pipe Fractional Flow Theory with Fully Compositional Wellbore Models
Authors: Zheng Shuang , Sharma Mukul 
Published: March 2023

Abstract
Multi-phase compositional wellbore flow is important in determining the flow and pressure drop in oil, gas, and geothermal wells. These effects become increasingly important in long laterals with multiple locations for fluid influx. Complex hydrocarbon phase behavior such as change in the number of phases, phase flipping, gas slippage can happen in the wellbore because of changes in pressure, temperature and inflow fluid rate and composition along the wellbore. This paper introduces a new wellbore model which integrates fully compositional fluid flow with an energy balance and pipe fractional-flow theory with multiple points of fluid entry along the wellbore.
Four sets of governing equations: component mass conservation, momentum conservation (pipe fractional flow theory), composition conservation and energy balance are solved fully implicitly along the wellbore. This is then fully implicitly coupled with the flow and energy balance equations in the reservoir and fracture domains. The primary unknowns along the wellbore (total flow rate, hydrocarbon component composition, water saturation, pressure, and temperature) can then be obtained. Flash calculations are used to calculate the hydrocarbon phase saturation, density, viscosity, etc. and the flow rate of each phase is obtained from the fractional flow theory given the local flow rate and saturations.
In the first case, we study the reservoir-wellbore flow in a gas condensate reservoir with 16 hydrocarbon components. As the pressure drops, an oil phase drops out of the single phase gas condensate, first in the wellbore and then in the reservoir. In a second case, we simulate CO2 flooding in a black-oil reservoir. Reservoir cooling is observed near the injection wellbore and an increased CO2 composition is observed in the produced oil from the production wellbore. In the third case, we study a low permeability volatile oil reservoir with 14 hydrocarbon components. Production from a hydraulically fractured horizontal wellbore is simulated considering the reservoir-fracture-wellbore flow. We observe that as the pressure drops in the wellbore, gas is liberated from the oil phase and this changes the wellbore pressure drop considerably. The lighter component compositions decrease with time while the heavier component compositions increase with time because of the liquid holdup effect. In the fourth case, we showcase a stand-alone wellbore model integrated with point sources.
This paper fully integrates a pipe-fractional flow formulation with compositional wellbore flow, and an energy balance for the first time. This allows the model to be used directly with compositional reservoir simulators. The wellbore mesh is automatically generated and coupled with the reservoir/fracture mesh to allow for an integrated and seamless simulation from the reservoir to the surface facility. This model allows engineers to accurately account for the pressure drop and phase behavior within the wellbore when simulating the production/injection of complex fluids.
Data-Space Inversion for Rapid Physics-Informed Direct Forecasting in Unconventional Reservoirs
Authors: Hui Mun-Hong , Wang Kainan , He Jincong , Tanaka Shusei 
Published: March 2023

Abstract
Traditionally, subsurface models are created based on reservoir characterization, then simulated and calibrated via history matching (HM) to honor data, generate forecasts, and quantify uncertainties. However, this approach is time consuming for unconventional projects with aggressive schedules. On the other hand, purely data-driven approaches such as decline curve analysis (DCA) are fast but not reliable for yet-to-be-observed flow regimes, e.g., boundaries or other effects causing late-time changes in productivity decline behaviors. We propose a physics-informed unconventional forecasting (PIUF) framework that combines simulations and data analytics for robust field applications. We apply Data-Space Inversion (DSI) to incorporate physics from a large ensemble of prior simulation models to generate posterior forecasts within a Bayesian paradigm. We also quantify the consistency of simulated physics and observed data by computing the Mahalanobis distance to ensure that the appropriate prior ensemble is employed. In lieu of history-matched models, a statistical relationship between data and forecast is learned; then posterior sampling is applied for data assimilation and direct forecasting in DSI. DSI reduces the dimensions of time-series (and other) data using parameterization like Principal Component Analysis. We implemented DSI within a tool that is connected to a vast database of observations for thousands of unconventional Permian Basin wells and a large ensemble of fracture simulations. We apply it to rapidly generate probabilistic forecasts (e.g., oil production rate, gas oil ratio) for unconventional wells and show that DSI can provide robust long-term forecasts based on early-time data when compared with DCA. We show that DSI yields robust uncertainty quantification with a manageable number of simulations compared with simple machine-learning methods like K-Nearest-Neighbors. We illustrate how data error and volume impact DSI forecasts in meaningful ways. We also introduce a DSI enhancement to generate posterior distributions for model parameters (e.g., hydraulic fracture height) to derive subsurface insights from data and understand key performance drivers. Our cloud-native implementation stores data (observed and simulated) in the cloud while the algorithm is implemented as a microservice that is efficient and elastic for the analysis of many wells. The overall framework is useful for rapid probabilistic forecasting to support development planning and de-risk new areas as an alternative to DCA or HM.
Multiscale Reservoir Simulation of High-Resolution Models
Authors: Natvig Jostein , Dias Daniel , Bratvedt Frode , Watanabe Shingo , Li Zhuoyi , Kozlova Antonina , Tomin Pavel , Jiang Jiamin , Shi Xundan 
Published: March 2023

Abstract
To quantify the uncertainty in reservoir performance, it is common to build ensembles of models that sample the space of possible reservoirs that are consistent with the available data. To evaluate the spread of possible outcomes, simulations experiments are run for each model in the ensemble to calculate for instance recovery factor. The geoscreening workflow is a common way to do this systematically and in a reasonable time. It can work as follows: First, run simulations with simplified physics to calculate recovery factor for every model in the ensemble. Then, use recovery factor (and other quantities) to rank and select representative models for high, medium, and low performance scenarios that can be used for full field simulations.
In this paper we present an application of the multiscale sequential fully implicit (MS SFI) framework to simulate extremely complex high-resolution models with simplified physics. This enables us to perform fast evaluations of geological uncertainty, such as in the geoscreening workflow. The multiscale SFI method computes each timestep in two steps: First, it solves a nonlinear equation for pressure (and flow). Then, it solves a nonlinear equation for saturations and mole fractions. The pressure equation is solved iteratively using a multiscale approach.
The MS SFI method has recently been made generally available in a commercial reservoir simulator and can easily be benchmarked with a state-of-the-art fully implicit (FI) method. The MS SFI method was used to successfully simulate a realistic high-resolution geological model in a practical time frame, achieving approximately 10 times speedup in CPU time compared to the FI method. This demonstrates the ability of the MS SFI method to effectively deal with extremely complex models, enabling fast quantification of geological uncertainty with a shorter turnaround time. In many instances the MS SFI method enables simulation of large models at the original geological resolutions without the need for upscaling.
Finally, we demonstrate how the MS SFI method benefits a geology screening workflow and discuss future use of the MS SFI framework to create fit-for-purpose simulation engines for other workflows.
New Fast Simulation of 4D (x, y, z, t) CO2 EOR by Fourier Neural Operator Based Deep Learning Method
Authors: Liu Jianqiao , Jing Hongbin , Pan Huanquan 
Published: March 2023

Abstract
The training speed is slow for the convolutional neural network (CNN)-based physics-informed neural network (PINN) in surrogate models and it is difficult to be applied to large-scale engineering problems. The Fourier Neural Operator (FNO) network can speed up 100 times faster than the PINN according to current literature. But the current FNO only handles the 3D (x, y, t) spatial-temporal domain. In this work, we developed a new framework to simulate the 4D (x, y, z, t) subsurface flow problems using the FNO network and the domain decomposition method. After numerical simulation runs, the obtained results of subsurface flow field distributions in 4D spatial-temporal domain (x, y, z, t) are decomposed into multiple 3D spatial-temporal domains (x, y, t) in the z dimension. Then, multiple FNO networks are used to train 3D spatial-temporal domain (x, y, t) in parallel to predict the distributions of the flow field in subsequent time steps. Finally, the predicted results of the 4D spatial-temporal solution in subsequent time steps are obtained by re-coupling the trained 3D (x, y, t) results in the z dimension. In this way, our new framework successfully extends FNO-network from 3D (x, y, t) to 4D (x, y, z, t) to predict field distributions in subsurface flow. The new framework was successfully applied to some very complex cases of CO2 injection for enhanced oil recovery (EOR) in compositional simulations. The predicted accuracy is enough for the method to be applied to simulate the complex CO2 EOR in fractured systems. The computational speed in 4D (x, y, z, t) can be as fast as it does in 3D (x, y, t) through parallel training. The tested results show that our new framework can efficiently simulate the EOR processes by injecting CO2 into complex fracture reservoirs. For the first time, we developed a new methodology that successfully extends the current FNO network from 3D (x, y, t) to 4D (x, y, z, t). Our framework paves way for the fast FNO network to solve the large-scale spatial-temporal domain of reservoir engineering systems.
Bridging Computational Stratigraphy and Reservoir Simulation for Geologically Realistic High-Resolution Reservoir Modeling
Authors: Li Boxiao , Li Lewis , Wen Xian-Huan , Sun Tao 
Published: March 2023

Abstract
Computational Stratigraphy (CompStrat) is a state-of-the-art earth-modeling method that captures the key heterogeneities in subsurface reservoirs through modeling of the detailed flow and sediment transportation processes in various depositional environments. The method is fully based on physics and generates high-resolution 3D earth models that are much more geologically realistic than those generated by traditional earth-modeling methods. It can accurately predict and preserve those spatially continuous but vertically thin and volumetrically insignificant layers, such as shale layers, thus enabling a much more accurate representation of natural reservoir connectivity.
In the past few years, CompStrat has been studied mainly within the earth science community and has yet been broadly applied in reservoir simulation research and practices. Our objective is to bridge this gap and allow this frontier technology to offer geologically realistic earth models for reservoir simulation to better understand how various geological features contribute and control subsurface flow patterns and performance, and subsequently leading to a better integration among earth modeling, flow simulation, and more reliable reservoir performance predictions.
CompStrat models often have large number of cells (hundreds of millions or more). A large proportion of them are related to thin shale layers. These thin cells can often cause convergence difficulties in reservoir simulations. We developed a grid coarsening method to drastically reduce the cell number and the simulation time with minimum altering of overall model connectivity characteristics. The method reduces the cell number by 85% to 93% and the simulation time by 94% to 99.4% with limited loss of accuracy for representative examples. Without this method, the simulation may take impractically long time to run for large models with complex multiphase flow dynamics.
The successful removal of the computational bottleneck enables the application of this frontier earth-modeling method in high-fidelity reservoir simulation. It also facilitates detailed understanding of the connection between geology and flow to offer valuable insight for reservoir modeling, production forecast uncertainty analysis, and history matching. We developed a method to label, evaluate, and rank geological features based on their influence on flow performance, with shale layers being the specific focus. The labeling is performed semi-automatically and the evaluation and ranking is done efficiently with a reduced-physics solver. The result is statistically consistent across multiple realizations.
A New Phase-Labeling Method Based on Machine Learning for CO2 Applications
Authors: Sheth Soham , Bennett James , Kachuma Dick , Heidari Mohammad Reza , Shaykhattarov Marat 
Published: March 2023

Abstract
Phase labeling can be very challenging for complicated compositional simulation cases. Inaccurate labeling can lead to issues ranging from incorrect resource accounting to non-convergent simulation runs. Accurate phase labeling algorithms are computationally demanding and are seldom used in commercial workflows. Instead, cheaper but inaccurate empirical methods are employed such as the Li-correlation (Reid et. el. 1966).
Phase labelling based on critical temperature alone mis-identifies fluids below the dew point pressure as liquids rather than vapour. This is a particular problem when performing surface flashes of CO2 or H2S rich fluids since both components have critical temperatures above standard temperature. This can lead to failures in the well model, for example when a well is controlled by gas rate but the produced phase is identified as a liquid. The second part of this paper therefore describes a new phase labeling method that uses both the critical temperature and saturation pressure predictions from the ML models to generate accurate labels. Results are presented for CO2 rich fluids. We show that this ML approach can result in accurate labeling and can outperform traditional methods in computational efficiency. We also show the application on simulation cases with complicated field management scenarios that require accurate phase labeling at the in-situ as well as separator conditions.
The ML workflow is based on a set of two interacting fully connected neural networks, one a classifier and the other a regressor, that are used to replace physical algorithms for single phase labelling and improve the convergence of the simulator. We generate real time compositional training data using different mixing strategies between the injected and the in-situ fluid compositions that can exhibit temporal evolution. In many complicated scenarios, a physical critical temperature as well as saturation pressure does not exist, and the iterative sequence fails to converge. We train the classifier to identify, a-priori, if a sequence of iterations will diverge. The regressor is then trained to predict an accurate value of critical temperature and saturation pressure. A framework is developed inside the simulator based on TensorFlow that aids real time machine learning applications. The training data is generated within the simulator at the beginning of the simulation run and the ML models are trained on this data while the simulator is running. All the run-times presented in this paper include the time taken to generate the training data and train the models.
Uncertainty Quantification for Transport in Porous Media Using Parameterized Physics Informed Neural Networks
Authors: Fraces Cedric G. , Tchelepi Hamdi 
Published: March 2023

Abstract
We present a Parametrization of the Physics Informed Neural Network (P-PINN) approach to tackle the problem of uncertainty quantification in reservoir engineering problems. We demonstrate the approach with the immiscible two phase flow displacement (Buckley-Leverett problem) in heterogeneous porous medium. The reservoir properties (porosity, permeability) are treated as random variables. The distribution of these properties can affect dynamic properties such as the fluids saturation, front propagation speed or breakthrough time. We explore and use to our advantage the ability of networks to interpolate complex high dimensional functions. We observe that the additional dimensions resulting from a stochastic treatment of the partial differential equations tend to produce smoother solutions on quantities of interest (distributions parameters) which is shown to improve the performance of PINNS. We show that provided a proper parameterization of the uncertainty space, PINN can produce solutions that match closely both the ensemble realizations and the stochastic moments. We demonstrate applications for both homogeneous and heterogeneous fields of properties. We are able to solve problems that can be challenging for classical methods. This approach gives rise to trained models that are both more robust to variations in the input space and can compete in performance with traditional stochastic sampling methods.