bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leveraging Data Analytics with Numerical Modeling for Optimizing Oil Field Development and Management
Authors: Alklih Mohamad Yousef , Tengku Ab Hamid Tengku Mohd , Alshabibi Tariq Ali , Mohagheg Shahab 
Published: September 2019

Abstract
Data-Driven subsurface modeling technology has been proven, for the past few years, to yield technical and commercial success in several oil fields worldwide. A data-driven model is constructed for the first time for an oil field onshore Abu Dhabi, and used for evaluation of a reservoir with substantial reserves and comprehensive development plan; for the purpose of predicting production rates, dynamic reservoir pressure and water saturation, improving reservoir understanding, supporting field development optimization and identifying optimum infill well locations. The objective is to provide the asset with a decision-support tool to make better field development planning and management.
The subject reservoir is a low permeability carbonate reservoir and characterized by lateral and vertical variations in its reservoir rocks and fluid properties. More than 8 years of Phase-I development and production/injection data and extensive amount of well tests and log data (SCAL, PVT, MDT) from more than 37 wells were used to construct the Data Driven Model for this asset.
This new modeling technology, (TDM), integrates reservoir engineering analytical techniques with Artificial Intelligence, Machine Learning & Data Mining in order to formulate an empirical and spatiotemporally calibrated full field model. In this work, it is leveraged with other conventional reservoir modeling and management tools such as streamline modeling, isobaric maps and flooding conformance.
Several analyses were performed using the full field data-driven model; complementing the existing conventional numerical model. The accomplishments of the data-driven reservoir model for this project included, but not limited to, comprehensive history matching (including blind validation) and then forecast of Oil rate, GOR, WC, reservoir pressure and water saturation, injection optimization, and choke size optimization. The results generated by the data-driven model proved to be quite eye-opening for the asset management; as the model was able to identify potential areas of improving field efficiency and cost reduction.
When combined with numerical techniques, the calibrated data-driven model assist to obtain a reliable short term forecast in a shorter time and help make quick decisions on day-to-day operational optimization aspects. The use of facts (all field measurements) instead of human biases, pre-conceived notions, and gross approximations distinguishes data-driven modeling from other existing modeling technologies. Its innovative combination of Artificial Intelligence and Machine Learning (the technologies that are transforming all industries in the 21st century) with reservoir engineering, reservoir modeling and reservoir management clearly demonstrates the potentials that these pattern recognition technologies offer to the upstream oil and gas industry for its realistic digital transformation.
Seismic Characterisation using Artificial Intelligence Algorithms for Rock Property Distribution in Static Modeling
Authors: Mata García Sergio Roberto , Carrasco Hernández Javier , Ortiz López José L. 
Published: September 2019

Abstract
This study examines which is the margin of usability for Artificial Intelligence (AI) algorithms related to the rock properties distribution in static modeling. This novel method shows a forward modeling approach using neural networks and genetic algorithms to optimize correlation patterns among seismic traces of stack volumes and well rock properties. Once a set of nonlinear functions is optimized in the well locations, to correlate seismic traces and rock properties, spatial response is estimated using the seismic volume. This seismic characterization process is directly dependent on the error minimization during the structural seismic interpretation process, as well as, honoring the structural complexity while modeling. Previous points are key elements to obtain an adequate correlation between well data and seismic traces. The joint mechanism of neural networks and genetic algorithms globally optimize the nonlinear functions and its parameters to minimize the cost function. Estimated objective function correlates well rock properties with seismic stack data. This mechanism is applied to real data, within a high structural complexity and several wells. As an output, calibrated petrophysical time volumes in the interval of interest are obtained. Properties are used initially to generate a geological facies model. Subsequently, facies and seismic properties are used for the three-dimensional distribution of petrophysical properties such as: rock type, porosity, clay volume and permeability. Therefore, artificial intelligence algorithms can be widely exploited for uncertainty reduction within the rock property spatial estimation.
A Method to Quantitatively Characterize Tight Glutenite Reservoir Pore Structure
Authors: Dong Xuemei , Zhang Ting , Yao Weijiang , Hu Tingting , Li Jing , Jia Chunming , Guan Jian 
Published: September 2019

Abstract
Pore structure is of great importance in tight reservoirs identification and validation evaluation, especially for formations with developed fractured. However, the conventional pore structure evaluation method based on nuclear magnetic resonance (NMR) logging lost its role. This is because the fractures with width lower than 2mm did not have response in the NMR T2 spectrum. Whereas the porosity spectrum, which extracted from the FMI data, was considered to be effective in fractured reservoir pore structure evaluation. In this study, to quantitatively characterize tight glutenite reservoir pore structure in the Jiamuhe Formation in northwest margin of Junggar Basin, northwest China, 90 core samples were drilled for lab mercury injection capillary pressure (MICP) measurement, and the XRMI data (acquired by the Halliburton and be similar with FMI) was processed to acquire the porosity spectrum. The relationship between the MICP curve and the corresponding inverse cumulative curve of porosity spectra was analyzed, and the model of piecewise power function, which can be used to transform the porosity spectrum as pseudo capillary pressure (Pc) curve, was established. By using this model, consecutive pseudoPc curves can be constructed in the intervals with which XRMI data was acquired, and the corresponding pore structure evaluation parameters, such as the average pore throat radius, the maximum pore throat radius, the threshold pressure, and so on, can also be predicted. Meanwhile, a permeability prediction model based on the Swanson parameter, also established. By combining with the constructed consecutive pseudoPc curves, the pore structure evaluation parameters and permeabilities, several hydrocarbon production potential formations were identified, and this was verified by the drill stem test (DST) data.
Smart Processing and Analysis of Image Log Data: A Digital Approach for a Robust Facies Modelling in Heterogeneous Carbonate Reservoirs
Authors: Galli Maria-Teresa , Berto Roberto , Buongiovanni Giuliana , Pirrone Marco 
Published: September 2019

Abstract
This paper discusses the use of a novel data-driven method for automated facies classification and characterization of carbonate reservoirs. The approach makes an extensive use of wireline and while drilling electrical borehole image logs and provides a direct and fast recognition of the main geological features at multi-scale level, together with secondary porosity estimation. This embodies an unbiased and valuable key-driver for rock typing, dynamic behavior understanding and reservoir modeling purposes in these puzzling scenarios.
The implemented methodology takes advantage of a non-conventional approach to the analysis and interpretation of image logs, based upon image processing and automatic classification techniques applied in a structural and petrophysical framework. In particular, the Multi-Resolution Graph-based Clustering (MRGC) algorithm that is able to automatically shed light on the significant patterns hidden in a given image log dataset. This allows the system to perform an objective multi-well analysis within a time-efficient template. A further characterization of the facies can be established by means of the Watershed Transform (WT) approach, based on digital image segmentation processes and which is mainly aimed at quantitative porosity partition (primary and secondary).
The added value from this data-driven image log analysis is demonstrated through selected case studies coming from vertical and sub-horizontal wells in carbonate reservoirs characterized by high heterogeneity. First, the MRGC has been carried out in order to obtain an alternative log-facies classification with an inherent textural meaning. Next, the WT-based algorithm provided a robust quantification of the secondary porosity contribution to total porosity, in terms of connected vugs, isolated vugs, fractures and matrix contribution rates. Finally, image log-facies classification and quantitative porosity partition have been integrated with production logs and pressure transient analyses to reconcile the obtained carbonate rock types with the effective fluid flows and the associated dynamic behavior at well scale.
The presented novel methodology is deemed able to perform an automatic, objective and advanced interpretation of field-scale image log datasets, avoiding time-consuming conventional processes and inefficient standard analyses when the number of wells to be handled is large and/or in harsh circumstances. Moreover, secondary porosity can be proficiently identified, evaluated and also characterized from the dynamic standpoint, hence representing a valuable information for any 3D reservoir models.
Distribution Mechanism of Asphaltene Deposits in CO2 Flooding Path: Interpretation by Numerical Model Based on Experimental Observation
Authors: Yonebayashi Hideharu , Iwama Hiroki , Takabayashi Katsumo , Miyagawa Yoshihiro , Watanabe Takumi 
Published: September 2019

Abstract
CO2 injection is one of widely applied enhanced oil recovery (EOR) techniques, moreover, it is expected to contribute to the climate change from a viewpoint of storing CO2 in reservoir. However, CO2 is well known to accelerate precipitating asphaltenes which often deteriorate production. To understand in-situ asphaltene-depositions, unevenly distributed in composite carbonate core during a CO2 flood test under reservoir conditions, were investigated through numerical modelling study.
Tertiary mode CO2 core flood tests were performed. A core holder was vertically placed in an oven to maintain reservoir temperature and to avoid vertical segregation. A composite core consisting of four Ø1.5" × L2.75" plug cores, which had similar porosity range but slightly varied air permeabilities, was retrieved from a core holder after the flooding test. The remaining hydrocarbon was extracted by Dean-stark method, and heptane insoluble materials were extracted from each plug core via IP-143 method to observe distribution of asphaltene deposits. The variation of asphaltene mass in plug cores was investigated to explain its mechanism thermodynamically.
The core flood test was completed to achieve a certain additional oil recovery by 15 pore volume CO2 injection without any unfavorable differential pressure. The remaining asphaltene mass in each plug core revealed a trend in which more asphaltene collected from the inlet-side core. We assumed a scenario to explain the uneven asphaltene distribution by incorporating the vaporized-gas-drive and CO2 condensing mechanism. Namely, asphaltenes deposited immediately when pure CO2 contacted with oil. The contact between more pure CO2 and oil might be more frequently occurred in inlet-side core. To reproduce the scenario, a cubic-plus-association (CPA) model was generated to estimate asphaltene precipitating behavior as injected gas composition varied. In the first plug core, more pure CO2 gas was considered to contact with fresh reservoir oil compared with the downstream cores which might have less pure CO2 because of its condensation. The light-intermediate hydrocarbon gas vaporized by CO2 was also considered to emphasize the trend of more asphaltene deposits in upstream-side cores. The CPA model revealed consistent phenomenon supporting the scenario.
A Pragmatic Approach to Reservoir Simulation Optimisation Under Uncertainty
Authors: Kathrada Muhammad , Azri Khairul 
Published: September 2019

Abstract
Reservoir simulation optimization under uncertainty typically invokes a sense of anxiety mainly because of a lack of a systematic criterion to choose between different development scenarios under uncertainty, how to go about doing well placement and optimizing well controls in the face of a large uncertainty ensemble of static realisations, and most of all the large number of simulation runs that potentially needs to be conducted. This is exacerbated when the models are large and require many hours to run. Moreover, even with the prevalence of distributed and parallel computing clusters, there is still a limited amount of computing resources available when spread out over the number of reservoir engineers within a company. Time and budget constraints also contribute to complicating this process. Furthermore, with the requirement of an inordinately large number of simulation runs comes the dilemma as to which optimizer to choose that would help speed up the process.
This paper first starts off with a brief background into historical attempts at tackling this problem by delving into the literature. Then it discusses a rigorous criterion for optimization under uncertainty viz. stochastic dominance, hitherto little known or used in the industry. A commonly used greenfield case study which is an ensemble set of uncertainty realisations is then introduced, which the rest of the paper will be based on. The ensemble is a pre-generated set of fifty realisations designed specifically for this problem. Two challenging areas will then be addressed viz. well placement optmisation under uncertainty, and well controls optimization under uncertainty.
Finally, a comparison between the simplex, proxy response surface, differential evolution and particle swarm optimization methods is made in the optimization of well controls. Hence the paper aims to give a complete picture on how to go about reservoir simulation optimization under uncertainty, with a drastically reduced amount of computational runs that needs to be conducted. Practical and sensible formulation of the optimization problemcan go a long way to making this process more understandable and easier to implement.
Geology-Based Porosity-Permeability Correlations in Carbonate Rock Types
Authors: Dernaika Moustafa , Masalmeh Shehadeh , Mansour Bashar , Al Jallad Osama , Koronfol Safouh 
Published: September 2019

Abstract
In carbonate reservoirs, permeability prediction is often difficult due to the influence of various geological variables that control fluid flow. Many attempts have been made to calculate permeability from porosity by using theoretical and empirical equations. The suggested permeability models have been questionable in carbonates due to inherent heterogeneity and complex pore systems. The main objective of this paper is to resolve the porosity-permeability relationships and evaluate existing models for predicting permeability in different carbonate rock types.
Over 1000 core plugs were studied from 7 different carbonate reservoirs across the Middle East region; mainly cretaceous reservoirs. The plugs were carefully selected to represent main property variations in the cored intervals. The data set available included laboratory-measured helium porosity, gas permeability, thin-section photomicrographs and high-pressure mercury injection. Plug-scale X-ray CT imaging was acquired to ensure the samples were free of induced fractures and other anomalies that can affect the permeability measurement. Rock textures were analyzed in the thin-section photomicrographs and were classified based on their content as grainy, muddy and mixed. Special attention was given to the diagenesis effects mainly compaction, cementation and dissolution.
The texture information was plotted in the porosity-permeability domain, and was found to produce three distinct porosity-permeability relationships. Each texture gave unique poro-perm trend, where the extent of the trend was controlled by diagenesis. Rock types were defined on each trend by detailed texture analysis and capillary pressure. Three different permeability equations (Kozney, Winland, Lucia) were evaluated to study their effectiveness in complex carbonate reservoirs. A new permeability equation was proposed to enhance the prediction results of the experimental data.
Rock types were successfully classified based on porosity, permeability, capillarity and textural facies. Conclusive porosity-permeability relationships were obtained from textural rock properties and diagenesis, which were linked to rock types using capillary pressure. The texture-diagenesis based rock types provided more insight into the effects of geology on fluid flow and saturation. Available models may not fully describe permeability in heterogeneous rocks but they can improve our understanding of fluid flow characteristics and predict permeability in un-cored wells.
An Adjoint Inexact Trust Region Method for Nonlinear Constraint Production Optimization
Authors: Chakra Chithra , Al Kobaisi Mohammed 
Published: September 2019

Abstract
Production optimization is the method for seeking the best possible well control and schedule plans in order to enhance reservoir performance under a given state and economic constraints. Determining the optimal injection and production control strategies through adjoint gradient-based optimization is a well-known practice in today’s modern reservoir management. However, apt handling of nonlinear control inputs, state and output constraints can be quite tedious with effects on the computational efficiency of the optimization algorithms used in practical production optimal control problems. In this paper, we develop an adjoint based interior-point inexact trust filter sequential quadratic programming (IITRF-SQP) method for solving constrained production optimization problems. Inexact trust-region is an extension of a filter trust region approach, which is used when the control input constraints Jacobians are of high dimension and are expensive to compute. The output constraints are handled using an interior-point method called- modified barrier-augmented Lagrangian, in which inequality constraints are treated by a modified barrier term and equality constraints with augmented Lagrangian terms. The algorithm we present uses the approximate information of Jacobians achieved through composite-step computation, which eliminates the cost of direct calculation of Jacobians and Hessians (gradients). The gradient information that provides criticality measure of the objective function is calculated using the adjoint method. Two numerical experiments on optimal water-flooding are presented. Performance comparisons of the proposed IITRF-SQP method with Lagrangian barrier method and sequential linear quadratic programming (SLQP) for solving production optimization problem are carried out. Results indicate that the gradient-based adjoint coupled with IITRF-SQP was able to improve net present value (NPV) through optimal production profiles with better computational efficacy via reduced convergence time and number of gradient and objective function evaluations.
Closed-Loop Integrated Time-Lapse Seismic Feasibility in Amberjack Field – Deepwater Offshore Gulf of Mexico
Authors: Thibodeaux Brandon , Ramsay Travis , Segovia Felix , Hernandez Luisalic , Ibrahim Mohammed 
Published: September 2019

Abstract
A flow simulation-driven time-lapse seismic feasibility study is performed for the Amberjack field that leverages existing multi-vintage 4D time-lapse seismic data. The focus is a field consisting of stacked shelf and deepwater reservoir sands situated in the Gulf of Mexico in Mississippi Canyon Block 109 in 1,030 ft of water. The solution leverages seismic interpretation, seismic inversion, earth modeling, and reservoir simulation [including embedded petro-elastic modeling (PEM) capabilities] to enable the reconciliation of data across multiple seismic vintages and forecast the optimal future seismic survey acquisition in a closed-loop. The overarching feasibility solution is integrated and simulation-driven involving multi-vintage seismic inversion, spatially constraining the petrophysical property model by seismic inversion, and performing reservoir simulation with the embedded PEM. The PEM is used to compute P-impedance and Vp/Vs dynamically, which enables tuning to both historical production and multi-vintage seismic data. The process considers a hybrid fine-scale 3D geocellular model in which the only upscaling of petrophysical properties occurs when the P-impedance from seismic inversion is blocked to the 3D geocellular grid. This process minimizes resampling errors and promotes direct tuning of the simulator response with registered seismic that has been blocked to a geocellular earth model grid. The results illustrate a three-part simulation-to-seismic calibration procedure that culminates with a prediction step which leads to a simulation-proposed time-lapse seismic acquisition timeline that is consistent with the calibrated reservoir simulation model. The first calibration tunes the model to historical production profiles. The second calibration reconciles the dynamic P-impedance estimate of the simulated shallow reservoir with that of the seismic inversion blocked to the 3D geocellular grid. The combination of these two steps outline a seismic-driven history matching process whereby the simulation model is not only consistent with production data but also the subsurface geologic and fluid saturation description. Large and short wavelength disparities in the P-impedance calibration existing between the simulator response and the time-lapse seismic data are attributed to resampling errors as a result of seismic inversion-derived P-impedance being blocked to the 3D geocelluar grid, as well as sparse well control in the earth model which leads to the obscuring of some asset-specific characteristics. The results of the third calibration step show how the time-lapse seismic feasibility solution accurately confirms prior seismic surveys undertaken in the asset. Given this confirmation, the solution achieves a suitable prediction of seismic-derived rock property response from the reservoir simulator as well as the optimal future time-lapse seismic acquisition time.