bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Artificial Intelligence-Based Nonlinear Solver for Hydrocarbon Reservoir Simulations
Authors: Ebadi Mohammad , Bezyan Yashar , Zabihifar Seyed Hassan , Koroteev Dmitry 
Published: June 2020

Abstract
The reservoir simulation is based on the solving of second-order nonlinear Partial Differential Equations (PDEs). Following the high-level of nonlinearity or irregular boundaries, analytical solutions are not applicable to solve the supposed PDEs. To numerically solve the PDEs, applying nonlinear solvers are recommended. Dependencies on derivatives and proper initial guesses are the main disadvantages of classic solvers. To overcome the mentioned obstacles, solving supposed equations based on Adaptive Neural Network (ANN) has been introduced.
The algorithm starts by introducing an initial set into the Nonlinear Simultaneous Algebraic Equations (NSAE). The outputs are compared with the desired matrix of zeros to generate the required error. The calculated vectors of errors and its derivation are firstly employed to update the ANN weights through applying the adaption laws, and secondly, create the input vector to run the ANN. The outputs of the ANN are considered as corrections to be made to the initial set. Then, the corrected initial set is reintroduced into equations. The procedure continues iteratively until the outputs of equations meet the required level of accuracy.
By taking advantages of the adaptive laws, the outputs of the presented algorithm have successfully been matched with answers of the classic solvers, but with less computational costs. The convergence of the shown algorithm has practically been examined by assuming various mathematical types of initial sets. The implemented algorithm has been robust enough to converge for different forms of the initial sets, even for invalid values like minus numbers. However, records indicate that the convergence rates are strongly dependent on the values of initial sets. Following the sensitivity analysis over the primary model of ANN lead to the optimized network, which could solve the supposed NSAE three times faster. It has been interpreted that the number of neurons (NN), the diagonal coefficient matrix of error (λ), and the adaptive coefficient (Fw) have the most significant impacts on the performance of the algorithm.
In contrast to Newton's method as the most well-known nonlinear solver, the launched algorithm does not require any proper initial guesses. Moreover, the absolute independence of computing the partial derivatives of the Jacobian matrix and its inversion, which causes a notable reduction of computational costs, is the other remarkable advantage of the proposed approach. The represented algorithm can be taken as the platform to develop the next generation of simulators working based on machine learning.
Study of Plugging Capability and Seepage Characteristics of Air Foam Flooding in Low Permeability Reservoirs
Authors: He Minxia He , Sun Zheng , Chen Yu , Hu Yongle , Li Xiangfang , Zhou Yingfang 
Published: June 2020

Abstract
Foam flooding is an effective method to control water channeling in low permeability reservoirs. However, due to injection difficulty and poor foam plugging ability encountered in the application of foam flooding in field scale, oilfield production has not achieved expected effects. Therefore, it is of great significance to carry out research on foam plugging ability and multi-cycle gas-liquid alternating fluid seepage law to improve the development effect of foam flooding.
This paper establishes a resistance factor model to evaluate foam plugging ability and analyzes sensitivity parameters. By studying the characteristics of gas phase relative permeability under multiple cycles of gas-liquid alternate injection, the reason for the difficulty of gas injection during foam flooding process is clarified from a theoretical view, and it provides an alternate strategy for adjustment of development plan.
The results show that the resistance factor has an S-shaped relationship with the pore radius, a linear relationship with the liquid viscosity, an L-shaped relationship with the bubble radius, and an n-shaped relationship with the gas-liquid ratio, and the foam resistance factor is extremely sensitive to parameter changes. Investigation on gas relative permeability has shown that as the number of alternate cycle increases, the relative permeability of gas phase generally decreases. At the same injection volume, gas phase relative permeability in the long-period cycle is higher than that in the short-period cycle.
Application of Numerical Flow Simulation Methods to Risk-Based Well Decommissioning Design
Authors: Johnson Caroline , Haghighat Sefat Morteza , Davies David 
Published: June 2020

Abstract
The global oil and gas industry will experience intensive well decommissioning activities, with tens of thousands of well plug and abandonment (P&A) operations forecast to be executed worldwide, over the next few decades. In the North Sea alone, 2,624 wells are expected to be decommissioned over the next decade (OIL&GAS_UK 2019). With the increasing levels of activity in P&A operations, new, fit-for-purpose, P&A design tools and operational technologies have become essential elements in the petroleum engineer's toolbox to ensure safe and cost-effective solutions, especially in today's environment. This paper introduces a novel tool to support risk-based evaluation of well P&A designs using a numerical, grid-based, fluid-flow simulation methodology. The risk-based modelling framework covers the full range of North Sea P&A design scenarios and tracks the long-term evolution of hydrocarbon movement in the P&A well over several thousand years. A visualization engine identifies the dominant hydrocarbon flow-paths, while Monte Carlo methods are used to account for uncertainties in the model input parameters; allowing multiple design options to be ranked with respect to the risk of a hydrocarbon leak.
Transient flow modelling of the well P&A system allows new key performance indicators (KPIs) to be developed, e.g. evolution of hydrocarbon saturations within the P&A well over time and the time hydrocarbons reach the surface. These KPIs are not provided by steady-state P&A models. Results presented in this paper demonstrate the value derived from applications of the developed framework to: Optimally allocate resources by identifying fit-for-purpose P&A designs (e.g. the number, location and length of wellbore barriers, and the value of remedial operations on annular flow barriers).Support risk-based decision-making via investigation and comparison of how multiple P&A design options perform for given well/reservoir conditions.Identify critical modelling parameters and optimally allocate data gathering resources to reduce uncertainty. The "likelihood of occurrence" of each cement defect type is one such critical, but very uncertain, model input parameter.
Polymer Injection for EOR: Influence of Mobility Ratio and Slug Size on Final Oil Recovery
Authors: Juárez José Luis , Bertin Henri , Omari Abdelaziz , Romero Carolina , Bourdarot Gilles , Jouenne Stephane , Morel Danielle , Neillo Valerie 
Published: June 2020

Abstract
Among the many parameters needed to optimize a polymer flood is the choice of polymer viscosity, mobility ratio and polymer slug size that should be injected to maximize oil recovery. In this paper, a new polymer flooding experimental study is addressed to answer two questions. Firstly, considering a given crude oil, what optimal polymer solution viscosity should be injected? And secondly how much polymer solution should be injected during the polymer flood to maximise recovery?
Experiments were carried out using 1D homogeneous Bentheimer cores of similar properties. The cores were oil flooded using crude oil (µo = 120cP at T=60°C) and aged to obtain intermediate wet conditions. The polymer was a partially hydrolysed polyacrylamide (HPAM) dissolved in a moderate salinity brine. The polymer solutions were prepared at different concentrations (from 1500ppm to 3000ppm) to cover a large range of viscosity ratio (Rμ=μoμp from 2 to 18) which correspond to end-point mobility ratios of 0.5 and 5.4, respectively.
Corefloods results show as expected, that the polymer is more efficient in terms of oil recovery when viscosity ratio is low. In line with polymer flooding theory, we observed at intermediate wettability conditions, that a maximum oil recovery is reached at M =1 (Rµ = 5) and that oil recovery did not increase when reducing the ratio to M = 0.5 (Rµ= 2). However, when considering aspects such as polymer mass required, injectivity concerns and flow stability, we observe two favorable conditions, corresponding Rµ= 5 and Rµ = 10, for a mobility ratios of 1 and 7, respectively.
Different polymer slug sizes were injected in the cores at the above conditions (Rµ = 5 and Rµ = 10) followed by water flooding (chase water). Both injections (polymer and water injection) were carried out at same flow rate to minimize miscible viscous fingering at the rear of the polymer slug. Results show that an optimal polymer slug size exists for which one can obtain the same microscopic oil recovery than that of continuous polymer injections at Rµ=5 and Rµ=10, an important finding that can impact the economic viability of the process.
In conclusion, our experimental study shows that at 1D scale, optimal values of viscosity ratio, polymer slug size and polymer mass injected lead to the same maximum oil recovery obtained by continuous polymer injection. A necessary starting point before upscaling a polymer flood and studying the impact of heterogeneities.
The Role of Back Pressure and Perforation Hole Erosion on the Magnitude of the Coefficient of Discharge in Hydraulic Fracturing Stimulation
Authors: Yosefnejad Davood M. , Fricke Bernd , Loehken Joern , McNelis Liam 
Published: June 2020

Abstract
Hydraulic fracturing or fracking is a well stimulation technique for extracting hydrocarbons from naturally low (extra low) permeable oil and gas reservoirs. In this process water, proppant and chemicals are injected through the wellbore and from the perforation hole into the reservoir. The main goal of this treatment is to create artificial fluid path conduits in the formation and finally increase the permeability (and productivity) of the reservoirs.
One of the important factors which affects the near wellbore fluid pressure drop is the coefficient of discharge (Cd) which is a characteristic of the perforated hole in the wellbore tubular. The coefficient of discharge is defined as the ratio of the measured mass flow to the theoretical mass flow. The Cd depends on many factors and may change with time due to erosion caused by the injected sand, which was pumped into the formation. In this research, we investigated some of the factors that can affect the coefficient of discharge like the erosion of the perforated hole and the backpressure given by the fracture.
For this purpose, we have developed a new high-pressure high-flow rate setup for examining the effect of the following parameters which can alter the coefficient of discharge. More specifically we have investigated the effect of perforation hole size, perforation hole geometry and perforation shape on the Cd value at ambient conditions and with backpressure, before and after sand erosion. To do so, in a first step we have used machined holes with a clearly defined geometry and then compared the results with real perforated holes which were generated using various shaped charge designs. The coefficient of discharge was measured using water or gelled water with different pressure differentials and back-pressures. In our study, we have injected sand slurry for 30 minutes with a constant concentration. The flow rate and pressure drop were also recorded simultaneously during the injection of the sand.
Our results show how the erosion directly affects the Cd value and the subsequent pressure drop near the perforated hole. A clear increase of the Cd magnitude becomes visible only due to a change of the inlet geometry without changing the diameter. Also, the backpressure, which represents real fracking conditions, leads to a significant increase compared to the measurements at ambient outlet pressures.
The measured values before and after the erosion for real perforation holes differ from simple drilled holes. From the recorded results, it also seems that certain perforation shapes or geometries are more effected by erosion than others.
Machine Learning Clustering of Reservoir Heterogeneity with Petrophysical and Production Data
Authors: Konoshonkin Dmitry , Shishaev Gleb , Matveev Ivan , Volkova Aleksandra , Rukavishnikov Valeriy , Demyanov Vasily , Belozerov Boris 
Published: June 2020

Abstract
Reservoir development decisions strongly depend on our understanding on reservoir heterogeneity, which is often subject to sparse and conflicting data, interpretational bias and constraints imposed by the modelling assumptions. The work tackles a challenging task of accurately and quickly identifying and describing uncertainty in the spatial distribution of reservoir heterogeneity derived from geological well data and with respect to a geological concept. We propose a metric based machine-learning approach to identify and describe spatial trends in reservoir heterogeneity/facies property distribution using wireline and production data.
We demonstrate how the proposed method can help to partition reservoir heterogeneity and discover and verify spatial trends for a real mature producing field in the Western Siberia. The obtained clustering of reservoir facies based on the wireline logs (alpha-SP) demonstrated a good agreement with the reservoir zonation based on manual log interpretation and the geological concept. Clustering based on individual well production profiles has confirmed the reservoir partitioning and matched some of the reservoir features aligned with the prevailing geological concept. The outcome of the proposed method helps to improve the facies distribution model by integrating the discovered spatial trends into a geostatistical model and account for uncertainty in the depositional scenario that is difficult to quantify based on manual interpretation.
Parallel Framework for Complex Reservoir Simulation with Advanced Discretization and Linearization Schemes
Authors: Li Longlong , Khait Mark , Voskov Denis , Abushaikha Ahmad 
Published: June 2020

Abstract
The continuous progress of reservoir monitoring technology provides encouraging capacities to reduce uncertainties in the subsurface characterization and to mitigate risks in field development applying the reservoir simulation approach. However, it is always challenging to take full advantage of the observation data, since an accurate representation of strong heterogeneities requires a high-resolution grid. Most of the discretization methods cannot handle full tensor permeability, and high nonlinearity introduced by complex physical process drastically reduces simulation efficiency. In this work, we develop an advanced parallel framework for reservoir simulation with the implementation of state of the art discretization and linearization methods. We apply the multipoint flux approximation (MPFA) method to handle the full tensor permeability in unstructured grids. To keep the fidelity of the geological model and improve computational efficiency, we use massively parallel computations via Message Passing Interface (MPI). Complex subsurface physics is described by mass-based formulations making the framework flexible for general-purpose reservoir simulation. However, the representation of phase behavior introduces additional workload when compared with the phase-based formulations in the traditional approach. Here, we apply the Operator-Based Linearization (OBL) approach which not only overcomes this drawback but also turns it to an advantage. In this method, the conservation equations are described in an operator form. By constructing a library of tabulated operators, the repeated work spent on complex phase behavior and property evaluation can be significantly reduced. We benchmark the parallel framework with analytical solutions under single-phase flow and multiphase flow. The results demonstrate that the parallel framework provides accurate simulation results for structured and unstructured grids. We validate that MPFA implemented in our parallel framework converges to real solutions when the permeability is a full tensor. Besides, several realistic cases have been rigorously tested confirming high computational capacity, efficiency, and accuracy of the advanced massively parallel framework for general-purpose reservoir simulation. With the implementation of MPFA and OBL approaches, the parallel framework is fully equipped for the simulation of problems with full tensor permeability, high-heterogeneities, and complex physical processes.
A Practical Probabilistic Upscaling Workflow for Compositional Reservoir Simulations of Miscible Gas Injection
Authors: de Souza Rios Victor , de Souza Rios Vinicius , Schiozer Denis José , Schmall dos Santos Luiz Otávio 
Published: June 2020

Abstract
Numerical reservoir simulation often requires upscaling of fine-scale detailed models and coarse-scale models are necessary to reduce computational time for dynamic evaluations. However, these simplifications may degenerate results due to loss of resolution of the small-scale phenomena, averaging of sub-grid heterogeneity and numerical dispersion, especially in oil fields where miscible gas is injected.
Most of the existing upscaling techniques focus on reproducing the results of a specific geological realization, in a deterministic approach. Nowadays, however, reservoir simulation studies commonly include uncertainty quantifications, which is performed by simulating multiple geological realizations. For that, the use of fine-scale models can be computationally prohibitive and this requires a proper procedure to upscale the coarse-scale simulation models in multiple realizations environment.
In this work, we propose and test an ensemble-level upscaling technique for compositional systems with miscible gas injection. The new approach considers the classical Koval factor, calculated for the fine-scale models, as a guide for selecting representative fine-scale models to train pseudo-functions for the coarse-models. Only a few fine models are simulated (about 1%), and the uncertainty quantification process with coarse-scale models can be significantly improved.
The proposed workflow is guided by ranking the fine-scale models in increasing order of their Koval Factor. We selected representative models and applied a two-step methodology to improve upscaled coarse-scale results for these models. We then propose a consistent procedure to expand the fitted pseudo-functions to all the coarse models, providing an effective ensemble-level upscaling.
The correlation between Koval factor and oil recovery is a useful guide to extrapolate the pseudo-functions obtained for each selected representative model, enabling better coarse-scale simulation results when multiple realizations are considered. This procedure can be applied for continuous miscible gas injection and can be adapted for WAG scheme.
This work was motivated by the lack of practical procedures to improve coarse-scale results at the ensemble-level. With our approach, we can better represent uncertainty quantification using coarse-scale models with reduced computational cost and requiring only a few fine-scale simulation runs.
Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach
Authors: Cumming J A , Botsas T , Jermyn I H , Gringarten A C 
Published: June 2020

Abstract

Objectives/Scope
A stable, single-well deconvolution algorithm has been introduced for well test analysis in the early 2000’s, that allows to obtain information about the reservoir system not always available from individual flow periods, for example the presence of heterogeneities and boundaries. One issue, recognised but largely ignored, is that of uncertainty in well test analysis results and non-uniqueness of the interpretation model. In a previous paper (SPE 164870), we assessed these with a Monte Carlo approach, where multiple deconvolutions were performed over the ranges of expected uncertainties affecting the data (Monte Carlo deconvolution).

Methods, Procedures, Process
In this paper, we use a non-linear Bayesian regression model based on models of reservoir behaviour in order to make inferences about the interpretation model. This allows us to include uncertainty for the measurements which are usually contaminated with large observational errors. We combine the likelihood with flexible probability distributions for the inputs (priors), and we use Markov Chain Monte Carlo algorithms in order to approximate the probability distribution of the result (posterior).

Results, Observations, Conclusions
We validate and illustrate the use of the algorithm by applying it to the same synthetic and field data sets as in SPE 164870, using a variety of tools to summarise and visualise the posterior distribution, and to carry out model selection.

Novel/Additive Information
The approach used in this paper has several advantages over Monte Carlo deconvolution: (1) it gives access to meaningful system parameters associated with the flow behaviour in the reservoir; (2) it makes it possible to incorporate prior knowledge in order to exclude non-physical results; and (3) it allows to quantify parameter uncertainty in a principled way by exploiting the advantages of the Bayesian approach.