bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comparative Study for Deep-Learning-Based Methods for Automated Reservoir Simulation
Authors: Maarouf Alaa , Tahir Sofiane , Su Shi , Kada Kloucha Chakib , Mustapha Hussein 
Published: January 2023

Abstract
Reservoir simulation is essential for various reservoir engineering processes such as history matching and field development plan optimization but is typically an intensive and time-consuming process. The aim of this study is to compare various deep-learning algorithms for constructing a machine-learning (ML) proxy model, which reproduces the behavior of a reservoir simulator and results in significant speedup compared to running the numerical simulator.
Initially, we generate an ensemble of realizations via the reservoir simulator to train the different ML algorithms. The data set consists of a comprehensive set of uncertainty parameters and the corresponding simulation data across all wells. The system utilizes recent advances in deep learning based on deep neural networks, convolutional neural networks, and autoencoders to create machine-learning-based proxy models that predict production and injection profiles as well as the bottomhole pressure of all wells. Thus, the proposed workflows replace the time-consuming simulation process with fast and efficient proxy models.
In this work we provide a comparative study of various ML-based algorithms utilizing deep neural networks and convolutional neural networks for constructing a surrogate reservoir model. The trained models can simulate the behavior of the physics-based reservoir simulator by correlating uncertainty parameters to various history-matched reservoir properties. The algorithms were tested on a mature oilfield with a notable number of wells and several decades of production and injection data. We analyze the performance of each ML approach and provide recommendations on the optimal one.
The best performing workflow for building the ML proxy model consists of two steps. The first step uses stacked autoencoders to learn a low-dimensional latent space representation of the highly dimensional simulation data. This step allows to reduce the complexity of predicting the simulation data and enhances the prediction quality. The following step constructs an ML model to predict the latent space features from input uncertainty parameters and produces highly accurate results.
Reservoir simulation is of paramount importance for various reservoir engineering workflows. Traditional approaches require running physics-based simulators for multiple iterations, which results in time-consuming and labor-intensive processes. We implement and compare several deep-learning-based methods to construct ML proxy models that automate and remarkably reduce the runtime of the reservoir simulation process.
Electromagnetic Technologies for Reservoir Mapping: Technology Review
Authors: Komies Saleh , Alshaya Abdulaziz 
Published: January 2023

Abstract
Monitoring fluid saturation and reservoir surveillance are essential techniques for effective reservoir management. As reservoirs age and water sweep advances, it becomes more difficult to find infill well placements. Reservoir scale saturation surveillance technologies offer a novel and promising way to optimise hydrocarbon recovery through development decisions. In addition, it can help evaluate sweep efficiency and improve reservoir simulation models. There are currently various proven electromagnetism approaches for reservoir scale saturation monitoring, including cross-well-EM (X-well-EM), Surface to Borehole EM (STB-EM), Borehole to Surface EM (BTS-EM), and EM through the casing. This study will examine all EM technologies.
Each technology has unique benefits and drawbacks that determine its field applications and objectives. The X-well-EM technology offers a cross-sectional resistivity profile at the inter-well level, which may be used to map oil zones that have been bypassed. Similar to the surface to borehole and borehole to surface technologies, the surface to borehole and borehole to surface technologies detect resistivity changes between transmitters and receivers for the same purpose. The benefit of these technologies is that they may be utilised for both one-time and recurring surveys. This feature permits the implementation of a feasible flood monitoring system throughout the field's lifetime.
This paper will thoroughly discuss the design for each technology and the survey planning. In addition, the challenges and critical criteria for conducting a successful survey will be discussed, including measurement accuracy, inter-well spacing, depth, signal-to-noise ratio, operating cost and time, completion kinds, and the optimal frequency range. Finally, we will present the end result that makes electromagnetism an intriguing concept emerging in the field of reservoir surveillance. Examining and contrasting the various EM survey approaches in order to highlight the best practice and its potential for field growth.
Numerical Analysis of Condensate Blockage and its Mitigation by a Wettability Altering Nanofluid Treatment
Authors: Morgenstern Rico , Mukherjee Soujatya , Behr Aron , Rafiee Mohsen 
Published: January 2023

Abstract
During field development planning, the condensate blockage phenomena often receives minor attention leading to overestimation of the gas well deliverability. This study addresses the numerical analysis of both the risk potential of condensate blockage and the complex physiochemical processes of nanofluid treatment for two very different gas condensate fields.
Producing gas condensate reservoirs below the dew point results in condensate dropout and liquid holdup, which usually impairs the gas flow significantly. Wintershall Dea is currently investigating the feasibility of a nanofluid huff’n’puff treatment which aims to alter the wettability in the near-wellbore area, so that both water and condensate are repelled by the rock surface. For accurate representation of condensate blockage, Generalized Pseudo Pressure (GPP) along with local grid refinements (LGRs) were incorporated in the compositional model as well as high-velocity effects and a detailed post hydraulic fracture environment. The limitations of commercial simulation software in representing the nanofluid treatment in the dynamic reservoir model were overcome by combining the implementation of new hydrocarbon components, the surfactant model, tracers, chemical reactions, and special-purpose Python scripts.
The buildup and spatial distribution of the condensate in and around the well and the fracture was analyzed. The two key parameters influencing the severeness of condensate blockage comprise the composition of the reservoir fluid and the effective permeability of the gas phase, which are particularly influenced by multiphase flow behavior, formation and/or fracture damage, high-velocity flow effects, and subsurface equipment like gravel packs. In both reservoirs studied, wells were identified that will most likely experience significant productivity impairments due to condensate blockage, some already a few months after the start of production and some in the next few years.
The transport of the active nanofluid chemical was implemented by two different approaches: for one it was carried by a solvent system, and for the other it was dispersed as droplets in gas. Both approaches mainly differ in the depth of penetration and the propagation of the nanofluid as well as in the operational effort and the longevity of the wettability alteration. The nanofluid treatment cases demonstrated promising preliminary results with an increase in gas productivity index up to 30%.
The removal of condensate blockage using a novel wettability altering nanofluid formulation was initially conceptualized and subsequently analyzed for vertical wells and hydraulic fractures using numerical simulation. For the first time, a comparison between the injection of the active nanofluid within either a gas or solvent carrier was evaluated. The risk of condensate blockage and a potential treatment were analyzed for real case scenarios including promising business viability.
Rigorous Digital Rock Technology Validation through Cross-Sector Collaboration
Authors: Klemin Denis , Schlicht Peter , Distaso Ripalta Eleonora , Radaelli Francesco 
Published: January 2023

Abstract
Digital rock analysis (DRA) is a combination of experimental and numerical studies that helps to evaluate fluid transport and petrophysical properties at pore scale. We describe a concept of rigorous digital rock technology validation and present its workflow and application for a collection of reservoir rocks representing different lithologies, grain size and sorting, porosities, and permeabilities. The objective was to obtain relative permeability curves from a wide range of rocks (porosities ranging from 13% to 22% and absolute permeabilities from 3 to 570 md) using a direct hydrodynamic simulator that is based on the principles of density functional hydrodynamics and to verify the obtained results through a comparison with experimentally obtained relative permeability data.
Critical to the study was the separation between laboratory experimental procedures, performed by an energy company, and the digital experiments on the provided core imaging and fluid data performed by an energy technology service company. Each respective part of the study was performed independently, and data verification happened once both digital and physical data were obtained. The study was conducted in three phases. First, routine core analysis properties were established using DRA and experimentally. Second, a set of steady-state relative permeability digital experiments was performed using provided fluid data, flow rate, and P/T conditions that replicated experimental laboratory tests. Digital experiments were performed considering water-wet and mixed-wet scenarios. Third, a further sensitivity study compared basic (resolved meso- and micropore porosity) and advanced (resolved porosity plus submicron porous matrix and solid rock components) models to evaluate the effect of submicron porosity on multiphase transport properties.
All three phases were executed for each reservoir rock, and results were compared with independently obtained experimental data. An excellent match was observed on all studied rocks. Digital rock technology was proven to be a reliable technology for all studied rocks providing accuracy levels that are within the range of uncertainty of repeated laboratory test results on same core.
If applied in combination with the traditional laboratory analyses, DRA allows for a reduction of the cost and time for the petrophysical characterization of the rock and provides a better understanding of the transport mechanisms at the smallest scales.
New Insights into the Understanding of Capillary Trapping of Residual Oil in Carbonate Rocks Using Capillary Desaturation Curves
Authors: Siyal Amaar , AlAmeri Waleed , Al-Shalabi Emad W. , Ahmed Shehzad 
Published: January 2023

Abstract
Most of the oil remain trapped in the reservoir after both primary and secondary recovery stages. Enhanced oil recovery (EOR) techniques are usually implemented in the tertiary stage to recover the trapped oil. Accordingly, the inaccurate determination of residual oil saturation after waterflooding (Sorw) in the secondary stage affects the success and economics of the EOR processes in the tertiary stage. Thus, the capillary desaturation curve (CDC) is usually introduced as guidance to estimate the mobilized residual oil. The objectives of this study include determining the true Sorw for carbonate Indiana limestone outcrops under harsh conditions, then investigating the effect of trapping number, permeability, and initial oil saturation on Sorw, and finally characterizing the CDC for carbonate rocks by further reducing the Sorw using surfactant flooding. For this purpose, six carbonate Indiana limestone outcrop samples with different permeabilities (4-69 mD) and fluid samples i.e., field-representative oil, formation water, seawater, and surfactant solutions were utilized. The drainage process was performed systematically using a coreflooding system to establish initial water saturation by injecting heavy oil followed by crude oil and aging for two weeks. Afterward, all six cores were subjected to spontaneous imbibition using Amott cell. This was further followed by forced imbibition using both ultra-centrifuge and coreflooding systems for comparison purposes and achieving Sorw condition. Finally, forced imbibition was performed on all cores using coreflood to generate CDC using three different surfactants with varying IFT values.
The results showed that all rock samples achieved initial water saturation (Swi) in the range of 18-32% with no correlation between Swi and rock permeability. In addition, spontaneous imbibition tests showed slight oil production which reflect the oil-wetness of these cores used. It was noted that this slight production varied among cores with the same rock permeability range, which indirectly indicating the existence of heterogeneity within each permeability range. Furthermore, Sorw of 20-30% was reached using ultra-centrifuge and coreflooding method, indicating no correlation of permeability with Sorw. Based on the CDC studies, the critical trapping number was in the range between 10−5 and 10−4 for the tested cores, which is higher than the reported values in literature (10−8 to 10−6). This work provides a new insight into the understanding of capillary trapping effect on residual oil using CDC in carbonates. The complications in carbonate rocks, including the complex nature of high heterogeneity, mixed-to-oil wettability, high temperature, and high salinity, render accurate determination of true Sorw is a challenge at lab-scale. Sorw determination and CDC characterization aid in EOR screening to find the effective and economically viable methods for production enhancement.
Case Study Artificial Intelligence (AI) Assisted Numerical Reservoir Simulations in a Mature Reservoir Under Waterflood Development in the Sultanate of Oman
Authors: Shuaibi Fakhriya Abdullah , Hadhrami Munira Mohamed , Sheheimi Awadh Harbouq , Agarwal Binayak , Riyami Qassim Mohamed , Ruqaishi Mohammed , Habsi Naima , Mortezazadeh Emad , Mohajeri Sina 
Published: January 2023

Abstract
PDO is transforming its field development planning by adopting digital technologies and Artificial Intelligence (AI) to improve organizational efficiency and maximize business value through swift quality decisions and an evergreen forecast. In this context, the company has approached a number of third parties to bring in solutions in this domain. In 2021 one such collaboration with 3rd party contractor to test a novel solution involving data driven AI based dynamic simulator in a mature brown fiend setting. The objective was to test the tool, the efficacy and efficiency of the process, robustness and ease of use and its utility in current setting [1].
Existing dynamic modelling workflows with conventional simulators are extremely time consuming to update and upgrade in a mature brownfield setting. These conventional and lengthy iterative process of working might leave value on the table. It is time consuming to update history match with all the extra inputs and forecast; and optimizing the development with all the input parameters within a short timeframe is always a challenge.
The process employed in this approach was based on deep learning artificial neural networks (ANN) coupled with numerical simulators and along other static model inputs. The reservoir static and the flow dynamics were used as feature parameters to train the ANN, while the historical field production was used as the target parameters. The ANN training exercise identified the most contributed static and dynamic parameters to the historical production; therefore, these main parameters were given a higher weight in production forecasting and reservoir management.
This AI-simulation method was expected to be faster, data driven and allow a faster testing of multiple development strategies in short time.
This paper outlines the experience of an AI-assisted numerical simulation approach to unlock the potential of brown oil fields in south Oman by reducing the time spent on modelling and base case anchoring. It also enables evergreen forecasting by integrating AI techniques with numerical simulation. The AI-simulation was tried in a brown field with an existing FDP generated using conventional simulation tool where &gt;50% of the FDP propose wells have been drilled. The outcomes from the AI-simulation result were compared with conventional simulation and with Actual field performance. Optimization was also conducted to locate the sweet spots for future drilling and WRFM opportunities. This optimized workflow has the potential to enable step change improvements in time and value for brownfield development and optimization for future developments.
Deriving New Type Curves through Machine Learning in the Wolfcamp Formation
Authors: Mask Gene Michael , Wu Xingru 
Published: January 2023

Abstract
Forecasting production from unconventional reservoirs is a slippery slope that could lead to unrealistic results even though a model has been history matched with production history and vetted by experienced engineers. The reasons for failing conventional decline curves lie in convoluting and heterogeneous reservoir properties, advancing drilling and completion techniques, and dynamic production and operations management. However, the initial rates, declining rate, and ultimate recovery of a well can be viewed as relatively static and predetermined properties of a declining profile. This paper will propose a machine learning-based framework to determine these properties for unconventional reservoir development.
In the proposed algorithm, instead of directly data mining on the raw data from different categories and scales, we propose to convert these data into dimensionless variable groups to reduce the dimension of the problem. The dimensionless variables are developed using inspection analysis; most have physical meanings and are easy to upscale. In the case study, we used the production, completion, and petrophysical data to generate new type curves and developed a step-by-step process to explain the aspect of "engineering" code that incorporates physics into the machine learning (ML) process.
Dimensionless variables are used in the machine learning process giving physical meaning and reducing the number of predictors, thus improving the speed and efficiency of the code. The results show that the quantity of cumulative oil production over time can be determined using machine learning models with R2 &gt;= 0.90 for individual wells and R2&gt;=0.80 for cross-validated cumulative production forecasts. We can use these determined values to assess the quality of initial rates, declining rates, and ultimate recovery to derive new type curves that incorporate physics and engineering practices. The work emphasizes the importance of accounting for completion parameters, fluid properties and rock quality, thus improving the confidence in results obtained through traditional engineering methods. The machine learning model results provide credibility and support to rates and recoveries for DCA forecasted wells. When modeling hundreds if not thousands of wells, this work shows the importance of utilizing machine learning to harness the power of the data that has been collected on them.
The machine-learning-based declining profile is a promising technique and has some advantages over the classical methods based on averaging historical data. First, the determining parameters are highly scalable for newly drilled wells as the main input parameters are dimensionless variables derived from reservoir properties and well completions. Secondly, this algorithm explores not only the production data but also reservoir properties and completion data to capitalize on the advancing techniques.
Reservoir Characterization and 3D Architecture of Multi-Scale Vugular Pore Systems in Carbonate Reservoirs
Authors: Zeiza Adam Danur , Almeshari Saleh Khaled , Mansor Abdulmohsin Saleh , Tarabbia Paul Joseph 
Published: January 2023

Abstract
This paper outlines a novel approach to an integrated 1D to 3D characterization and geomodeling of Vugular pore systems (VPS) in carbonate reservoirs. The study focused on capturing the various scales of the VPS and how they related to the 3D architecture.
An integrated approach for detecting, characterizing and modeling the multi-scale VPS has been employed by utilizing multi-disciplinary datasets, that span from 3D seismic volumes, borehole images, production data to petrographic analyses. These datasets were analyzed and corroborated in the 1D, 2D and 3D domains to validate and define the occurrence and architecture of the VPS within the reservoir. Eventually, a 3D geo-cellular model of the VPS is constructed by honoring diagenetic proxies and experiments, VPS flags, fluid flow behavior and seismic attributes geobodies.
VPS are observed at nearly all scales of datasets (i.e. geoscience and reservoir engineering data). They also occur at different scales of pore size ranging from millimeters to multi-meters. Many of them are even beyond the resolution of conventional whole core and basic well logs. A complete set of static and dynamic data allows a classification of the VPS into several classes based on their size, intensity and effect on reservoir properties and flow rates. Diagenetic proxies, well-based experimental variogram analyses and seismic-based geobody extraction further confirmed their architecture and distribution within 3D space. Their ramified patterns within the proximity of structural crestal areas tie quite consistently with well and seismic data. This architecture is further supported by possible hydro-dynamic corrosive fluid behavior that potentially had longer residence time in the crestal areas during late burial diagenesis stages.
A modeling-while-interpreting workflow is also configured to model the VPS in 3D interactively and confirm the VPS occurrence on a well-by-well basis. This method is applied directly to the 3D full-field model and is linked to an interpretation platform. This unique approach contributes to the reduction of ambiguity in subsurface data and analysis.