bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Open Access Carbonate Reservoir Benchmarking Study for Reservoir Characterisation, Uncertainty Quantification & History Matching
Authors: Costa Gomes Jorge , Geiger Sebastian , P. Arnold Daniel 
Published: September 2019

Abstract
This work presents a new open access carbonate reservoir case study that uniquely considers the major uncertainties inherent to carbonate reservoirs using one of the most prolific aggradational parasequence carbonate formation set in the U.A.E; the Late Barremian Upper Kharaib Mb. as an analogue. The ensemble considers a range of interpretational scenarios and geomodelling techniques to capture the main components of its reservoir architectures, stratal geometries, facies, pore systems, diagenetic overprints and wettability variations across its platform-to-basin profile.
Fully anonymized data from 43 wells across 22 fields in the Bab Basin, U.A.E from different geo-depositional positions and height above FWL’s (specified to capture multiple structural positions) within an area of 36,000 km2 was used. The data comprises of a full suite of open hole logs and core data which has been anonymized, rescaled, repositioned and structurally deformed; FWL’s were normalized and the entire model was placed in a unique coordinate system. Our petrophysical model captures the geological setting and reservoir heterogeneities of selected fields but now at a manageable scale.
The novelty of this work has been to create semi-synthetic open access carbonate reservoir models which enable the geoscience and reservoir engineering community to analyse, study and test number of cases related to new numerical algorithms for reservoir characterisation, reservoir simulation, uncertainty quantification, robust optimization and machine learning. The value of this study is also to expose a model and a dataset to the reservoir simulation engineers so they can explore the impact of different fluid flow physics on sweep and recovery across multiple carbonate reservoir architectures with diverse lateral and vertical rock and fluid complexities – all of which can be history-matched against a ‘truth case’.
History Matching Using 4D Seismic in an Integrated Multi-Disciplinary Automated Workflow
Authors: Taha Taha , Ward Paul , Peacock Gavin , Heritage John , Bordas Rafel , Aslam Usman , Walsh Steve , Hammersley Richard , Gringarten Emmanuel 
Published: September 2019

Abstract
This paper presents a case study in 4D seismic history matching using an automated, ensemble-based workflow that tightly integrates the static and dynamic domains. Subsurface uncertainties, captured at every stage of the interpretative and modelling process, are used as inputs within a repeatable workflow. By adjusting these inputs, an ensemble of models is created, and their likelihoods constrained by observations within an iterative loop. The result is multiple realizations of calibrated models that are consistent with the underlying geology, the observed production data, the seismic signature of the reservoir and its fluids. It is effectively a digital twin of the reservoir with an improved predictive ability that provides a realistic assessment of uncertainty associated with production forecasts.
The example used in this study is a synthetic 3D model mimicking a real North Sea field. Data assimilation is conducted using an Ensemble Smoother with multiple data assimilations (ES-MDA). This paper has a significant focus on seismic data, with the corresponding result vector generated via a petro-elastic model. 4D seismic data proves to be a key additional source of measurement data with a unique volumetric distribution creating a coherent predictive model. This allows recovery of the underlying geological features and more accurately models the uncertainty in predicted production than was possible by matching production data alone.
A significant advantage of this approach is the ability to utilize simultaneously multiple types of measurement data including production, RFT, PLT and 4D seismic. Newly acquired observations can be rapidly accommodated which is often critical as the value of most interventions is reduced by delay.
Analysis of Evolutionary Algorithm and Discrete Cosine Transformation Components Influence on Assisted History Matching Performance
Authors: Al-Jenaibi Faisal , Shelepov Konstantin , Kuzevanov Maksim , Gusarov Evgenii , Bogachev Kirill 
Published: September 2019

Abstract
The application of intelligent algorithms that use clever simplifications and methods to solve computationallycomplex problems are rapidly displacing traditional methods in the petroleum industry. The latest forward-thinking approaches inhistory matching and uncertainty quantification were applied on a dynamic model that has unknown permeability model. The original perm-poro profile was constructed based on synthetic data to compare Assisted History Matching (AHM)approach to the exact solution. It is assumed that relative permeabilities, endpoints, or any parameter other than absolute permeability to match oil/water/gas rates, gas-oil ratio, water injection rate, watercut and bottomhole pressure cannot be modified.
The standard approach is to match a model via permeability variation is to split the grid into several regions. However, this process is a complete guess as it is unclear in advance how to select regions. The geological prerequisites for such splitting usually do not exist. Moreover, the values of permeability and porosity in different grid blocks are correlated. Independent change of these values for each region distortscorrelations or make the model unphysical.
The proposed alternative involves the decomposition of permeability model into spectrum amplitudes using Discrete Cosine Transformation (DCT), which is a form of Fourier Transform. The sum of all amplitudes in DCT is equal to the original property distribution. Uncertain permeability model typically involves subjective judgment, and several optimization runs to construct uncertainty matrix. However, the proposed multi-objective Particle Swarm Optimization (PSO) helps to reduce randomness and find optimal undominated by any other objective solution with fewer runs. Further optimization of Flexi-PSO algorithm is performed on its constituting components such as swarm size, inertia, nostalgia, sociality, damping factor, neighbor count, neighborliness, the proportion of explorers, egoism, community and relative critical distance to increase the speed of convergence. Additionally, the clustering technique, such as Principal Component Analysis (PCA), is suggested as a mean to reduce the space dimensionality of resulted solutions while ensuring the diversity of selected cluster centers.
The presentedset of methodshelps to achieve a qualitative and quantitative match with respect to any property, reduce the number of uncertainty parameters, setup ageneric and efficient approach towards assisted history matching.
Multidisciplinary Approach for Unconventional Reservoirs Characterisation by Integrating Wireline Openhole Logging Techniques – Electric and Sonic to Formation Testing
Authors: Kelkouli Mohammed , Hadj Arab Hedi , Bendali Amor Nasrine , Kecili Kanza , Mokhtari Nabil 
Published: September 2019

Abstract
This paper has an objective of identifying the nature of formation fluid from an extreme tight fractured reservoir. A good understanding of petrophysical properties of the reservoir rock as well as the fluid it contains constitutes a real challenge for tight reservoirs, that are the most common unconventional sources of hydrocarbons.
The front-line characterization mean is the Wireline logging which comes directly after drilling the well or while drilling, knowing that for low to extreme low porosity-permeability reservoirs any attempt of conventional well testing will not bring any added value not rather than a confirmation of reservoir tightness.
A tailored workflow was adopted to design the most appropriate formation testing module, select the best depths for fluid sampling, and distinguish hydrocarbon from water bearing intervals.
This workflow involves ultra-sonic and Electric Borehole Images in combination with Sonic Scanner for natural fractures detection, localization and characterization, integrating Dielectric recording and processing for petrophysical evaluation, then Formation Testing was carried out for fluid identification and sampling.
The use of borehole electric and sonic imager coupled with advanced sonic acquisition helped not only to identify the natural fractures depths, but also the nature of these fractures. This integration was used for selecting the sampling station. Successful fluid sampling was carried out in 4 different depths (2 gas and 2 water), then a dielectric measurement was integrated to map the continuity of the water zone and narrow the uncertainty on fluid contact.
This novel multidisciplinary approach that was adopted, integrates answer products from different domains to enable the interpreter, (the reservoir engineer, the geologist, and the Petrophysicist) to better understand and characterize the reservoir, toward a good reserve’s evaluation and appropriate development plan.
Advanced Eor Process Upscaling Using Multi-Scale Digital Verification Technology and FDP Application
Authors: Moreno Ortiz Jaime Eduardo , Klemin Denis , Savelyev Oleg , Gossuin Jean , Melnikov Sergey , Serebryanskaya Assel , Liu Yunlong , Gurpinar Omer , Salazar Melvin , Gheneim Herrera Thaer 
Published: September 2019

Abstract
Use of numerical models to characterize and evaluate reservoir potential is an industry wide practice, with increasingly more development decisions being substantiated by finite difference models. Advances on hardware and software, along with the ability to effectively incorporate accurate process physics, makes simulation a robust tool for field development decisions, particularly on complex operations such as enhanced oil recovery and/or reservoirs with challenging heterogeneity and pore structures. Use of these models does not come without its challenges where data requirements (and use of special characterization both at lab and field level) increase as does the reservoir characterization granularity and thus model sizes.
Unsurprisingly the increase of model precision and data requirements amplifies non-uniqueness of the numerical solutions obtained during any field evaluation including field development planning (FDP). Incomplete/inconsistent datasets pose a further challenge to the accuracy (and arguably risk) of the forecasts by introducing further uncertainty on the process characterization. Use of complementary technology such as digital rock, that would enable mitigate impact of such uncertainties in a timely manner -either at field or laboratory level, is thus highly desirable particularly when dealing with enhanced oil recovery. Compounding the non-linearity effect of the EOR agent characterization is the effect of the augmented numerical artifacts (dispersion, dilution, etc) of which complex chemical implementations are prone to, making the upscaling process from laboratory dimensions to field more complex.
This paper complements our previous investigation on the use of digital rock solutions and multi-scale upscaling and is addressing two complementing topics: Use of multiscale digital rock technology for field development – using a case study to illustrate the use of DR on field appraisal complementing otherwise unsampled facies, using a multi-nested approach to reconcile DR observations at different plug scalesEvaluate the impact of finite-difference numerical simulation grid on the surfactant injection performance- highlighting limitations and challenges of existing models as well as proposing potential upscaling alternatives.
It is our intention to further reconcile digital rock upscaling with other EOR methods such as polymer/CO2 injection and of course surfactant. While we were able to highlight the caveats of upscaling on complex chemical floods we continue to investigate and design a solution that would encompass combination of chemicals (surfactant, alkaline and polymer) as well as handle of concentration/salinity changes.
Fast-Track Completion Decision Through Ensemble-Based Machine Learning
Authors: Xue Han , Malpani Raj , Agrawal Shivam , Bukovac Tomislav , Mahesh Arathi L. , Judd Tobias 
Published: September 2019

Abstract
With the advent of high-resolution methods to predict hydraulic fracture geometry and subsequent production forecasting, characterization of productive shale volume and evaluating completion design economics through science-based forward modeling becomes possible. However, operationalizing a simulation-based workflow to optimize design to keep up with the field operation schedule remains the biggest challenge owing to the slow model-to-design turnaround cycle. The objective of this project is to apply the ensemble learning-based model concept to this issue and, for the purpose of completion design, we summarize the numerical-model-centric unconventional workflow as a process that ultimately models production from a well pad (of multiple horizontal laterals) as a function of completion design parameters. After the development and validation and analysis of the surrogate model is completed, the model can be used in the predictive mode to respond to the "what if" questions that are raised by the reservoir/completion management team.
Deriving Permeability and Reservoir Rock Typing Supported with Self-Organized Maps SOM and Artificial Neural Networks ANN - Optimal Workflow for Enabling Core-Log Integration
Authors: Saputelli Luigi , Celma Rafael , Boyd Douglas , Shebl Hesham , Gomes Jorge , Bahrini Fahmi , Escorcia Alvaro , Pandey Yogendra 
Published: September 2019

Abstract
Permeability and rock typing are two of the main outputs generated from the petrophysical domain and are particularly contributors to the highest degree of uncertainty during the history matching process in reservoir modeling, with the subsequent high impact in field development decisions. Detailed core analysis is the preferred main source of information to estimate permeability and to assign rock types; however, since there are generally more un-cored than cored wells, logs are the most frequently applied source of information to predict permeability and rock types in each data point of the reservoir model.
The approach of this investigation is to apply data analytics and machine learning to move from the core domain to the log domain and to determine relationships to then generate properties for the three-dimensional reservoir model with proper simulation for history matching. All wells have a full set of logs (Gamma Ray, Resistivity, Density and Neutron) and few have routine core analysis (Permeability, Porosity and MICP). On a first pass, logs from selected wells are classified into Self Organizing Maps (SOM) without analytical supervision. Then, core data is used to define petrophysical groups (PG), followed by linking the PG's to NMR pore-size distribution analysis results into pre-determined standard pore geometry groups, in this step supervised PGs are generated from the log response constrained by the relationship between pore-throat geometry (MICP) and pose-size distribution (NMR). Permeability-porosity core relationships are reviewed by sorting and eliminating the outliers or inconsistent samples (damaged or chipped, fractures or with local features). After that, the supervised PGs are used to train and calibrate a supervised neural network (NN) and permeability and rock type's relationships can be captured at log scale. Using dimensionality reduction improves the neural network relationships and thus data population into the petrophysical wells.
The result is a more robust model capable to capture over 80% of the core relationships and able to predict permeability and rock types while preserving the geological features of the reservoir. The application of this method makes possible to determine the relevance of core and log data sources to address rock typing and permeability prediction uncertainties. The applied workflows also show how to break the autocorrelation of variables and maximize the usage of logs.
This work demonstrates that the introduced data-driven methods are useful for rock typing determination and address several of the challenges related to core to log properties derivation.
Screening of Geological Uncertainty on Reservoir Dynamic Behavior with Statistical Learning Techniques
Authors: Barbiero Marco , Turri Fulvia , Anastasi Patrizia , Della Rossa Ernesto 
Published: September 2019

Abstract
A statistical screening methodology is presented to address uncertainty related to main geological assumptions in green field modeling. The goals are the identification of the entire range of uncertainty on production, learning which are the most impacting geological uncertain inputs and understanding the relationships between geological scenarios and classes of dynamic behavior.
The paper presents the methodology and an example application to a green field case study. The method is applied on an ensemble of reservoir models created by combining geological parameters across their range of uncertainty. The ensemble of models is then simulated with a selected development strategy and dynamic responses are grouped in classes of outcome through clustering algorithms. Ensemble responses are visualized on a multidimensional stacking plot, as a function of the geological input, and the most influential parameters are identified by axes sorting on the plot. Geological scenarios are then classified on dynamic responses through classification tree algorithms. Finally, a representative set of models is selected from the geological scenarios.
The example study application shows a final oil recovery uncertainty range of 4:1, which is reasonable for a green field in lack of data. Such high range of uncertainty could hardly be found by common risk assessment based on fixed geological assumptions, which often tend to underestimate uncertainty on forecasts. Ensemble outcomes are grouped in four classes by oil recovery, plateau strength, produced water, and breakthrough time. The adoption of such clustering features gives a broad understanding of the reservoir dynamic response. The most influential geological inputs among the examined structural and sedimentological parameters in the example application result to be the fault orientation and channel fraction. This screening result highlights the main drivers of geological uncertainty and is useful for the following scenario classification phase. Classification of the geological scenarios leads to five classes of geological parameter sets, each linked to a main class of dynamic behavior, and finally to five representative models. These five models constitute an effective sampling of the geological uncertainty space which also captures the different types of dynamic response.
This paper will contribute to widen the engineering experience on the use of machine learning for risk analysis by presenting an application on a real field case study to explore the relationship between geological uncertainty and reservoir dynamic behavior.
Potential Applicability of Miscible N2 Flooding in High-Temperature Abu Dhabi Reservoir
Authors: Mogensen Kristian , Xu Siqing 
Published: September 2019

Abstract
Gas injection is a proven EOR method in the oil industry with many well-documented successful field applications spanning a period of more than five decades. The injected gas composition varies between projects, but is typically hydrocarbon gas, sometimes enriched with intermediate components to ensure miscibility, or carbon dioxide in regions such as the Permian Basin, where supply is available at an attractive price.
Miscible nitrogen injection into oil reservoirs, on the other hand, is a relatively uncommon EOR technique because nitrogen often requires a prohibitively high pressure to reach miscibility. Unlike other injection gases, the minimum miscibility pressure for nitrogen decreases with increasing temperature. In fact, in deep, hot reservoirs containing volatile oil, nitrogen may develop miscibility at a pressure similar to the MMP for hydrocarbon gas or carbon dioxide. The phase behavior is more complicated than what can be captured by correlations and hence requires equation-of-state calculations.
Results from a recent EOR screening study in ADNOC indicate that a couple of high-temperature oil reservoirs in Abu Dhabi may be potential targets for miscible nitrogen injection. This paper discusses key aspects of the EOS modeling. Advanced gas injection PVT data are available to enable a fair comparison between nitrogen, carbon dioxide and lean hydrocarbon gas. In this work, we have modelled and analyzed the phase behavior of two volatile oil systems with respect to nitrogen, hydrocarbon gas, and carbon dioxide injection, as part of a reservoir simulation study, which will be covered in a subsequent publication; see Mogensen and Xu (2019). Nitrogen behaves differently from hydrogen carbon gas, despite the fact that the two gases lead to similar minimum miscibility pressures. At the prevailing reservoir pressure, the swelling factor with hydrocarbon gas is four times higher than for nitrogen. Furthermore, the reservoir fluid density increases during swelling with nitrogen, whereas it decreases as a result of hydrocarbon gas swelling. The same trend is observed for viscosity. Injection gas blends with various proportions of nitrogen and carbon injection shows that the MMP is constant when more than 35-40% nitrogen is present in the blend.
Comparing Advanced Discretization Methods for Complex Hydrocarbon Reservoirs
Authors: Hjeij Dawood , Abushaikha Ahmad 
Published: September 2019

Abstract
Most commercially available simulators use the trivial two-point flux approximation (TPFA) method for flux computation. However, the TPFA only gives consistent solutions when used for K-orthogonal grids. In general, multi-point flux approximation (MPFA) methods perform better under both heterogeneous and anisotropic conditions. The mimetic finite difference (MFD) method is designed to preserve properties on unstructured polyhedral grids, and its development for simulating full tensor permeabilities is also crucial step. This paper compares the performance, accuracy, and efficiency of these schemes for simulating complex synthetic and realistic hydrocarbon reservoirs.
Agile Approach to Optimize Field Development Plan with Maximum Leverage of an EPS Phase Learnings in Offshore Abu Dhabi
Authors: Agrawal Pawan , Zahaf Kamel , Sinha Amit Kumar , Draoui Elyes 
Published: September 2019

Abstract
Field presented here is located in offshore Abu Dhabi, consisting of multi-stacked reservoirs with different fluid and reservoir properties. In this paper, field development plan of one of reservoir has been presented which was initially planned to be developed with pattern water injection by more than 50 horizontal wells penetrating all the ten oil bearing layers from 9 well head towers. Reservoir consists of under-saturated oil with low gas-oil ratio and low bubble point.
Initial 2 years of production was considered as Early Production Scheme (EPS period), during which significant amount of early production data consisting of downhole pressure measurement, time-lapse MDT, vertical interference data, PLT have been collected. Based on EPS data simulation model has been updated. Simulation fits well with the observed pressure gauge and time-lapse MDT data. Updated model gives good prediction for a year of blind test data (including saturation, MDT and porosity) collected from different wells several kilometers away from current development area reflecting a high level of confidence in areal and vertical connectivity representation. Considering other reservoir uncertainties different Development plans have been screened using updated model in order to improve recovery factor and economics. Based on development plan screening study, optimized development option has been chosen for Full Field Development.