bluuebunny/crossref_metadata_2025 huggingface dataset:
Maximising the Use of Horizontal Well Data in the Structural Framework of the Reservoir Modelling Workflow: A Case Study of a Middle East Carbonate Reservoir
Authors: Contreras Perez David Rafael , Al Zaabi Ruqaya Abdulla , Viratno Bernato , Sellar Christopher , Susanto Maria Indriaty
Published: September 2019

This paper summarizes an efficient workflow for building a reliable static model reference case by improving the accuracy of well placement in a hydrocarbon bearing structure. This is beneficial in optimising upcoming well target position and trajectory planning as well as during the dynamic history matching process. In a non-operated venture, the ability to generate an up-to-date static model that maintains pace with operations, provides valuable insight to advise the operator on the upcoming drilling plan and continuously supports the dynamic model for reserves booking, is highly sought after.

The systematic approach described in this paper is applied to a geo-model from a Middle East carbonate reservoir consisting of over 50 wells with good quality PSDM seismic data. The workflow presented begins with seismic mapping, utilizing volume-based modelling techniques, followed by structural element correction using borehole images (e.g. structural formation dip and true stratigraphic thickness estimate) and finally introduces alternative control points, which enable drilled wellbore trajectories to be structurally anchored, based on layer thicknesses and structural trends within the target reservoir.

Using this approach it is possible to generate a consistent structural model that honours geological markers, measured dip ranges and structural trends seen from seismic data and image logs. During the process one learns more about data quality (e.g. scale of data resolution and depth of investigation), associated with specific fields and carbonate reservoirs through the interaction between geological, geophysical and petrophysical disciplines and ensures their correct use. Data are used to improve the raw interpreted seismic horizons by calibrating mapped thickness distribution against the well tops. 2D visualizations are generated on a well-by-well basis, including map views, curtain sections (along each horizontal well), composite cross-sections and 3D visualizations to show inter-well relationships within different geological layers. As a result the well is placed in the correct structural position. Correct well placement, especially of highly deviated/horizontal wells, provides more accurate identification of reservoir sweet spots, leading to improved well target position and trajectory planning for upcoming wells, and a robust baseline to achieve production/well test history match during the dynamic modelling process.
Digital Pore Space Study of a Middle-Eastern Carbonate Rock Sample
Authors: Tallec Gwenole
Published: September 2019

Objectives/Scope

While Image processing is still an area of research, standard workflows have emerged and are routinely used in Oil&Gas companies.

However, while hardware capabilities have increased consequently, allowing large samples to be scanned with a high fidelity, permeability simulations are still limited to small samples unless having access to HPC. Direct simulations are known to be more flexible in terms of type of rocks, but limited in terms of sample size, while Pore Network Model based allow much larger sample sizes but less rock types.

Methods

In this study, we will focus on the pore space analysis of a middle-eastern carbonate sample. The rock sample is 7.5 cm tall and has a diameter of 3.8 cm.

It has been acquired at 3 different resolution: a microCT scan at 16μm, a microCT scan of a 10 mm of diameter subsample at 5 μm, and a 10 mm of diameter SEM section at 2μm.

This study will propose a methodology to mix the different scales in order to get an accurate pore space analysis of the largest possible sample size.

Results

As micro porous regions are visible at every scale, bringing uncertainty to the segmentation step, the first part of our analysis will consist of determining the most accurate pore space at the three different resolutions. We will rely on image registration (2D to 3D and 3D to 3D) and image based upscaling methods, further validated by simulation results.

Given the large numerical size of the samples, specific workflows involving large data 3D visualization and processing will be presented.

Then, different measures will be conducted: porosity and connected porosity, absolute permeability with three different methods (Lattice Boltzmann, Finite Volume, Pore Network Modeling), relative permeability curves using a Pore Network Model simulator. A new pore network model generation applicable to highly concave pore spaces such as carbonates ones will also be introduced.

Novel

A scalable method using automation will be presented, so that repeating the simulations on different samples of different space origins and size is easy.

We will expose the results and limits of every method and will determine which size is bringing a convergence of the results. We will especially look at the convergence of direct based simulations and pore network model based ones, such that expanding the size prior to Pore Network Model generation can be reliable.

In addition to the benchmark of the different simulation methods and their associated limits, the results will help us determining the representative elementary volume at different resolutions and the associated uncertainty depending on whether sub-resolution acquisitions are available or not.
Grid and Fluid Independent Description for Multilateral Horizontal Well in Dynamic Simulation
Authors: Bogachev Kirill Yu. , Erofeev Vladislav D. , Piskovskiy Evgeny V.
Published: September 2019

The method for modeling of a multilateral well design that is completely independent on the simulation grid and fluid properties is proposed. The method takes into account friction in the lateral branches and crossflow between them. Well parameters, such as trajectory, perforation intervals, roughness and diameter, are directly used to calculate pressure distribution along the wellbore at the current fluid composition and tubing head pressure (THP).

Well connections with grid blocks in a finite volume approximation for dynamic model should be created. The automatic creation of the well connections during dynamic simulation based on specified well trajectory and completion intervals is proposed. The connection factor is suggested to be calculated based on length of completion intersection with the block, trajectory direction and rock properties during the run time. To calculate pressure drop on well track intervals between connections and the well track intervals between top completion and tubing head the well-known correlations are utilized. The correlations are used for the current fluid composition in the wellbore in each connection using information for well trajectory, roughness and diameter.

Such an approach makes it possible to get rid of the use of the tabulated bottomhole pressure (BHP) as a function of tubing head pressure for a number of phase compositions. Such traditional use of phase compositions gives a non-physical response in compositional models, where the component composition of the product varies significantly throughout the life of the field. Usage of real coordinates (x, y, z) for setting well trajectory and perforation intervals, instead of the traditional grid block numbers (i, j, k), allows to calculate layer intersection, connection factors and pressure distribution along wellbore with arbitrary changes in the dynamic model grid, for example, when introducing local grid refinement or dynamic grid and rock properties variation used to describe hydraulic fracturing.

The proposed method is successfully used for modeling of a multilateral well design in dynamic simulation. The results of such dynamic simulation are consistent with the real samples from reservoir.
Machine Learning for 3D Image Recognition to Determine Porosity and Lithology of Heterogeneous Carbonate Rock
Authors: Al-Farisi Omar , Zhang Hongtao , Raza Aikifa , Ozzane Djamel , Sassi Mohamed , Zhang TieJun
Published: September 2019

Automated image processing algorithms can improve the quality and speed of classifying the morphology of heterogeneous carbonate rock. Several commercial products have worked to produce petrophysical properties from 2D images and with less extent from 3D images, relying on image processing and flow simulation. Images are mainly micro-computed tomography (μCT), optical images of thin-section, or magnetic resonance images (MRI). However, most of the successful work is from the homogeneous and clastic rocks. In this work, we have demonstrated a Machine Learning assisted Image Recognition (MLIR) approach to determine the porosity and lithology of heterogeneous carbonate rock by analyzing 3D images form μCT and MRI. Our research method consists of two parts: experimental and MLIR. Experimentally, we measured porosity of rock core plug with three different ways: (i) weight difference of dry and saturated rock, (ii) NMR T2 relaxation of saturated rock, and (iii) helium gas injection of rock after cleaning and drying.

We performed MLIR on 3D μCT and MRI images using random forest machine-learning algorithm. Petrophysicist provided a set of training data with classes (i.e., limestone, pyrite, and pore) as expert knowledge of μCT Image intensity correspondence to petrophysical properties. MLIR performed, alone, each task for identifying different lithology types and porosity. Determined volumes have been checked and confirmed with three different experimental datasets. The measured porosity, from three experiment-based approaches, is very close. Similarly, the MLR measured porosity produced excellent results comparatively with three experimental measurements, with an accuracy of 97.1% on the training set and 94.4% on blind test prediction.
Evaluation of CO2-EOR Project for an Oil Field in Abu Dhabi, UAE Based on Industry Best Practices
Authors: Chirkov Maxim , Coughlin Matthew Kent , Ponte Pineda Wuilmer Jose
Published: September 2019

Enhanced oil recovery ("EOR") by means of CO2 injection has become a globally-used method of increasing oil recovery. Interest in the UAE has increased in recent years due to the proven effectiveness of the process, and due also to government plans to initiate a world-class CO2 capture campaign.

EOR by means of CO2 injection is associated with many challenges. The majority of EOR projects are conducted in onshore fields since offshore EOR experience is limited by technical and operational challenges, as well as by higher economic hurdles. A larger resource base is required to justify an offshore EOR project than a project that is located onshore. Apart from that, the decision to embark on a CO2 injection project is often complicated by the lack of multidisciplinary integration. Uncertainty analysis should be included in the evaluation since it is critical to understanding pilot objectives, identifying model limitations, proper scaling of results from the pilot area to the field, and managing expectations. The economics of the proposed project are strongly dependent on proper baseline definition – possible only by means of advanced methods of reservoir characterization – and by state-of-the-art methods of dynamic reservoir simulation using a fully-compositional model and robust equations of state to characterize the process physics.

The oilfield discussed in this paper is a digital multi-reservoir field being developed by horizontal and highly-deviated wells equipped by inflow control devices (ICD). Primary depletion will be augmented by reservoir-specific water injection and hydrocarbon gas injection. Field characterization is being done based on data from comprehensive open- and cased-hole log suites, seismic data, MDT runs, PTA, and an array of SCAL and PVT tests necessary to fully describe the process physics.

The purpose of this paper is to describe the workflow and methods used to design, and estimate the efficiency of a CO2 injection project for a carbonate reservoir complex in the offshore area of the UAE. Recommendations for pilot project and surveillance program designs based on best practices and lessons learned from prior projects, developed to overcome the challenges described above, are discussed.
Efficient Optimization of Field Management Strategies in Reservoir Simulation
Authors: Ahmed Elfeel Mohamed , Ali Samad , Giddins Marie Ann
Published: September 2019

Standard approaches to optimization under uncertainty in reservoir simulation require use of multiple realizations, with variable parameters representing operational constraints and actions as well as uncertain scenarios. We will show how appropriate use of local optimization within the simulation model, using customized logic for field management strategies, can bring improved workflow flexibility and efficiency, by reducing the effort needed for uncertainty iterations.

To achieve meaningful forecasts for an ensemble of uncertain scenarios, it is important to distinguish between different types of decision. Investment decisions, such as facilities sizing, depend on global unknowns and must be optimized for the complete ensemble. Operational actions, such as closing a valve, can be optimized instantaneously for individual scenarios, using measurable information, although subject to constraints determined at a global level. In this study, we implement local optimization procedures within simulation cases, combining customized objective criteria to rank reactive or proactive actions, with the ability to query reservoir flow entities at appropriate frequencies.

The methods presented in the paper can be used for reactive response modeling for smart downhole control; optimization of ESP/PCP pump performance; and implementation of production plans subject to defined downstream limits. For selected cases, we compare the advantages and disadvantages of the local optimization approach with standardized "big-loop" uncertainty workflows. The methodology can significantly reduce optimization costs, particularly for high-frequency actions, achieving similar objective function values in a fraction of the time needed for post-processing optimizers. Use of tailored scripting provides the capability to modernize the logic framework for field management decisions, with realistic representation of smart field equipment and flow entities at any level of complexity.

Use of efficient workflows as described in this paper can reduce the cost of multiple realization studies significantly, or enable engineers to consider a wider range of possible scenarios, for deeper understanding and better risk mitigation.
Transition Zone Characterization for Simulation Models of Gas Bearing Carbonate Reservoir
Authors: Faizova Tatiana , Butorina Mariia , Goncharov Alexey , Nekhaev Sergey
Published: September 2019

Nowadays demand makes gas one of the desirable hydrocarbon sources of energy. However reservoirs size and quality of new discoveries or old preserved one are poor. This fact set the task for simulation of accurate prediction in order to evaluate economic reasonability of production.

This study concerns the question of changes gas production profile at the gas-bearing carbonate formation due to presence of transition zone. The different ways of simulation models initialization are implemented. Then the changes of the total output are compared and analyzed. In addition the advantages of initialization by means of J-function over capillary pressure curve assignment for the carbonate formation with highly heterogeneous flowing properties are explained. All results are coupled to vertical flow performance of wells which were matched at the tests.

The study shows that transition zone should be taken into account in simulation modeling of the gas-bearing no less than for the oil-bearing formations. Also the total gas production compared with and without the transition zone implementation due to its production is sensitive to presence any liquid. Accurate J-function matching at the core data provide better result for water breakthrough thus the performance of wells and finally the total production profile. The vertical flow performance curves adjustment and subsequent use for the simulation is the additional need which considered because in the case of the surface equipment restriction, such as the tubing head pressure limits, the pressure loss in the presence of gas and water mixture could be huge which cause early shutdown of the wells.

It is shown that gas bearing is not the reason to neglect the importance of the capillary force and the presence of the transition zone which has essential impact on the total formation performance.
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

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

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

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

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

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

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

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

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

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

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

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.
Assisted History Matching and Uncertainty Analysis Workflow for a Large Oilfield in Middle East
Authors: Ismail Ahmed , Hazem Yasser , Al Obaidi Mazna Naji , Bogachev Kirill , Gusarov Evgenii , Shelepov Konstantin , Kuzevanov Maksim
Published: September 2019

This paper proposes an assisted history matching (AHM) and uncertainty analysis workflow that was applied to facilitate the history matching of a giant carbonate reservoir in Middle East. The objective was to identify and quantify reservoir uncertainties and assess their impact on the field performance. In addition, to create a sufficient number of realizations to allow combinations of all uncertainties to capture a combined effect.

A real field case is represented by a consistent workflow that iteratively updates the ranges and number of reservoir uncertainties constrained by the actual measurements. The process has the following steps: definition of global uncertainty, sensitivity analysis, exclusion of less influential parameters, experimental design, revision of uncertainty matrix, and run optimization algorithms. The approach was firstly implemented at a global level and then continued to a regional level. The primary objective function is consisted of oil and water production mismatches, and the plan is to upgrade the objective function to include more parameters for further model HM enhancements.

Initially, the workflow was based on five uncertainty parameters. Ten sensitivity analysis cases were performed and tornado chart analysis suggested excluding some parameters that have less impact on the match quality, hence the objective function. Next, experimental design using Latin Hypercube was performed which allows seeing a combined effect of uncertainty parameters. During several experimental design iterations, the uncertainty parameter matrix was revised and a total number of uncertainty parameters was increased from 5 to 17. Finally, a total number of 260 experimental cases were completed, however, no good history match case was obtained. Therefore, a transition from the global level to a regional level was performed. The most sensitive identified uncertainties at global level were absolute permeability, vertical permeability anisotropy, pore volume and fault transmissibility. At the regional level, additional permeability multipliers for well regions were added to the uncertainty matrix. After that, a good quality matched cases were obtained.

Field scale and complexity were the main drive to implement AHM workflow. In a giant carbonate reservoir with long history and complex geology, a classical history matching method with unique solution cannot assure an accurate model predictability. The key advantages of this approach were the facilitating of the HM process and reducing of the total calculation time.
Cardiovascular Risk Factor mapping and distribution among adults in Mukono and Buikwe districts in Uganda: small area analysis
Authors: Musinguzi Geofrey , Ndejjo Rawlance , Ssinabulya Isaac , Bastiaens Hilde , Marwijk Harm van , Wanyenze Rhoda K.
Published: September 2019

Introduction Sub-Saharan Africa (SSA) is experiencing an increasing burden of Cardiovascular Diseases (CVDs). Modifiable risk factors including hypertension, diabetes, obesity, central obesity, sedentary behaviours, smoking, poor diet (characterised by inadequate vegetable and fruit consumption), and psychosocial stress are attributable to the growing burden of CVDs. Small geographical area mapping and analysis of these risk factors for CVD is lacking in most of sub-Saharan Africa and yet such data has the potential to inform monitoring and exploration of patterns of morbidity, health-care use, and mortality, as well as the epidemiology of risk factors. In the current study, we map and describe the distribution of the CVD risk factors in 20 parishes in two neighbouring districts in Uganda. Methods A baseline survey benchmarking a type-2 hybrid stepped wedge cluster randomised trial design was conducted in December 2018 and January 2019. A sample of 4372 adults aged 25-70 years was drawn from 3689 randomly selected households across 80 villages in 20 parishes in Mukono and Buikwe districts in Uganda. Descriptive statistics and generalized linear modelling controlled for clustering were conducted for this analysis in Stata 13.0, and a visual map showing risk factor distribution developed in QGIS. Results Mapping the prevalence of selected CVD risk factors indicated substantial gender and small area geographic heterogeneity. Patterns and clustering were observed for some major risk factors for CVDs including hypertension, physical inactivity, smoking, and risk factor combination. Prevalence of unhealthy diet is very high across all parishes with no significant observable difference. Conclusion Modifiable cardiovascular risk factors are common in this low income context. Moreover, across small area geographic setting, it appears significant differences in distribution of risk factors exist. These differences suggest that underlying drivers such as sociocultural, environmental and economic determinants may be promoting or inhibiting the observed risk factor prevalences which should be further explored.
The characteristics of lumbar paraspinal muscle degeneration based on the Roussouly classification: a retrospective study
Authors: Yang Shuo , Xu Guangyu , Zou Fei , Jiang Jianyuan , Lu Feizhou , Ma Xiaosheng , Xia Xinlei
Published: September 2019

Background:To analyse the characteristics of lumbar paraspinal muscle degeneration in individuals with different Roussouly types. Methods: Eighty patients (20 patients for each Roussouly type) with degenerative lumbar disease were retrospectively enrolled from January 2017 to February 2018. The cross-sectional area (CSA) and percentage of fat infiltration (FI) of the paraspinal muscles at the L1-S1 levels were measured using T2-weighted axial MRI and ImageJ software. The measured data were analysed with independent samples t-tests. Results: Degeneration of the paraspinal muscles of the upper lumbar region was significantly severer in Type 1 than in Type 3, the degrees of paraspinal muscle degeneration of Type 2 appeared to be more evenly distributed in all segments than Type 3, and Type 4 showed severer degeneration in L5-S1 than Type 3. Conclusion: The degree of paraspinal muscle degeneration in each segment was different across the four Roussouly types, but the degrees of degeneration were consistent with the characteristics of degenerative disease within each type.