bluuebunny/crossref_metadata_2025_split huggingface dataset:
Joule-Thomson Coefficients from Well Test Analysis Data
Authors: Meisingset Knut Kristian , Pedersen Karen Schou 
Published: September 2016

Abstract
The paper describes how the reservoir temperature can be simulated from well test data. The procedure is exemplified using data for four different reservoir fluids, one gas condensate, one oil mixture, and two volatile oils. The reservoir pressures vary from 150 to 800 bar and the reservoir temperatures are in the range of 70 - 130oC. The procedure requires knowledge of the reservoir pressure and it is a further requirement that well test pressure and temperature data at bottom-hole conditions exists for a fixed flow rate and a period long enough to have a negligible heat exchange with the surroundings. The well test data reveals that the temperature of the gas condensate will decrease when expanded in the near well bore area, while a temperature increase is seen for the oil mixtures. With negligible heat exchange between the flowing fluid and the surroundings, the flow process can be regarded an adiabatic expansion of the reservoir fluid from reservoir to bottom-hole conditions. The enthalpy at reservoir conditions is found from a Pressure-Temperature flash at bottom-hole conditions corrected for a possible height difference between bottom-hole and perforation interval. The reservoir temperature is found from a subsequent Pressure-Enthalpy calculation at reservoir pressure. The thermal calculations are carried out using a cubic equation of state. The simulated reservoir temperatures are in good agreement with the data material.
Rock Typing Classification and Hydraulic Flow Units Definition of One of the Most Prolific Carbonate Reservoir in the Onshore Abu Dhabi
Authors: Alhashmi N. F. , Torres K. , Faisal M. , Cornejo V. Segura , Bethapudi B. P. , Mansur S. , Al-Rawahi A. S. 
Published: September 2016

Abstract
Defining the flow and distribution of fluids in porous media has always been of key importance in modeling and predicting the performance of oil and gas reservoirs. Based upon the rock-fluid interactions, reservoir rocks have to be classified into separate flow units called rock types. This task is particularly complex in carbonates as they are generally impacted by diagenesis and cannot be represented by a single porosity permeability relationship per litho-facie.
Establishing accurate rock types in carbonates, therefore, requires integration of various petrophysical data with the available rock, fluid and geological information. Various techniques have evolved in the industry for formulating rock-types (Pittman, RQI, FZI, Lucia, Winland, etc.), each technique offering its benefit depending on the nature and variety of data available.
This paper presents a newly adopted workflow to formulate an RRT definition for a carbonate reservoir by integrating data from MICP, CCA, petrophysical logs and lithofacies information. The workflow involves associating the pore throat size distribution evaluated using MICP data with the measured porosity and permeability values utilizing the Winland R35 equation. Hydraulic flow units are identified using the Stratigraphic Lorenz Plot, based on the change of flow and storage capacity slopes. Pc, PTR, Phi and K discriminators were established and were used to as cut-offs for defining intervals representing good and poor facies.
The new methodology helped to achieve a very good match (&gt;80%) of water saturation from the initialized model with the log derived saturations in all wells drilled thus far in Reservoir-A. The methodology further helped optimize the number of effective rock types required to effectively delineate the field dynamic characteristics, helping reduce run time and anticipated convergence issues.
An Improved Methodology for Simulating Oil Recovery by Carbonated Water Injection: Impact of Compositional Changes
Authors: Al Mesmari Ali , Mahzari Pedram , Sohrabi Mehran 
Published: September 2016

Abstract
Enhanced Oil Recovery by carbonated water injection (CWI) has recently attracted significant attention. The main advantage of CWI, compared to conventional CO2 flood, is that it requires small amount of CO2 and it can be readily applied to oil fields with on-going or planned waterflood. The challenge with CWI is numerical simulation of the complex compositional changes that take place as a consequence of the transfer of CO2 from CO2-enriched (carbonated) water to crude oil under reservoir conditions. We have recently reported that these compositional changes result in the formation of a new gaseous phase within the oil and become the dominant mechanism controlling the performance of CWI.
In this investigation, utilising the results of novel direct visualisation experiments, a new and improved methodology for simulating the performance of CWI has been successfully developed that is capable of reproducing the physical processes observed in our micromodel and coreflood experiments. First, the parameters controlling the phase behaviour of crude oil and carbonated water were identified and an equation-of-state (EOS) was tuned to simulate the partitioning of CO2 between the aqueous phase and the crude oil. Furthermore, to properly account for the formation of the new phase during CWI, three-phase flow functions and kr were utilised for simulation of flow. Using an integrated automatic-history matching algorithm, the proposed methodology is then employed to examine the capability of commercial reservoir simulators to couple mass transfer and multi-phase flow during CWI. For this, the results of a series of consistent coreflood experiments were used where carbonated water was injected in secondary and tertiary modes.
The results of the history-matching exercises demonstrated that, to properly capture the underlying mechanisms of EOR by CWI, the phase behaviour and three-phase flow functions should be coupled in numerical simulation of the process. The results also revealed that the binary interaction coefficients between oil components and CO2 would control the extent of the gaseous-phase formation. Also, a relatively high value for critical gas saturation was obtained to history match the coreflood experiments, which was in agreement with the results of the direct visualisations experiments. Moreover, a variety of three-phase oil relative permeability functions were considered to replicate the movement of the gaseous-phase, which would be dictated by reconnection of the oil ganglia. The new phase formation outperforms other oil recovery mechanisms such as reduction of oil viscosity and oil swelling.
The results of the study help improve the accuracy of the numerical simulation of the oil recovery processes involving CO2 and carbonated water injection. This will in turn improve the quality of our reservoir performance predictions and the reliability of our economic calculations of these enhanced oil recovery techniques.
Visualizing Portfolio Tradeoffs With Perceptually Accurate Self-Organizing Maps
Authors: Kostuik K. , Mu Y. , El-Beltagy M. , Naiem A. 
Published: September 2016

Abstract
Multi-objective optimization and unsupervised machine learning were used to visualize relationships between portfolio composition and business performance tradeoffs. A multi­objective global optimization algorithm reduced the exponentially large space of possible portfolio outcomes to a set of Pareto optimal tradeoff frontiers for various combinations of decision and outcome constraints. The high-dimensional decision space was then reduced with an unsupervised neural network to generate Kohonen self-organizing maps (SOMs) for each tradeoff frontier. The resulting decision SOMs were rendered with a perceptually accurate color map and interactively linked with the tradeoff frontiers to enable visual data mining.
The interactive visualization revealed relationships between planning decisions and business outcomes in the complex and high-dimensional decision space. The interactive SOM automatically classifies similar strategies together and shows how neighboring strategies relate to each other. The analysis demonstrated that the proximity of portfolios on the tradeoff frontier does not imply similarity of underlying portfolio decisions, and that the mapping between decisions and outcomes becomes more complex with stricter constraints.
A Practical Workflow for Probabilistic History Matching and Forecast Uncertainty Quantification: Application to a Deepwater West Africa Reservoir
Authors: Yeh T. , Uvieghara T. , Jennings J. W. , Chen C. , Alpak F. O. , Tendo F. 
Published: September 2016

Abstract
Reliable reservoir uncertainty estimation is crucial to understanding its valuation and making robust decisions. Conventional practices where history matching and production forecast are performed on selected high-mid-low cases do not provide a reliable estimation of forecast uncertainty. This is typically reflected in a narrow range for ultimate recovery (UR) or net present value (NPV) predictions. In order to capture the inherent subsurface uncertainty, it is necessary to use an ensemble of models which spans the full uncertainty space.
The Probabilistic History Matching (PHM) workflow is an ensemble-based workflow, aimed to improve forecasting uncertainty estimation. One of the biggest challenges is that it typically requires a large ensemble size to span the uncertainty space due to the limited information (e.g. core data or well logs). This requirement may render the Assisted History Matching (AHM) exercise infeasible when computational resources are relatively limited. Therefore, it is necessary to reduce the number of models to a manageable size prior to performing AHM. Here we implement the Dynamic Fingerprinting workflow, to effectively select a set of representative models from the ensemble while preserving the uncertainty of the variables of interest. In this methodology, time-of-flight (TOF) and drainage time (DRT) information, which are direct estimates of swept and undrained volumes are used to characterize each model. A small subset of models is then selected based on their dissimilarity in flow pattern and used for AHM/forecasting.
The workflow was applied to a deepwater West Africa reservoir. An ensemble of 810 models was generated to represent the subsurface uncertainty. Ten models which were highly dissimilar in flow response were selected from the ensemble for AHM and estimating forecast uncertainties. The AHM was performed using an Experiment Design (ED) - Response Surface Modeling (RSM) - Markov-Chain Monte-Carlo (MCMC) workflow. For validation purposes, a different AHM workflow was performed on each of the 810 models using a derivative-free optimization algorithm. The comparison between the results supports the choice of the representatives from the Dynamic Fingerprinting work flow as well as the history matching conclusions from the ED-RSM-MCMC workflow.
Fast 2D Inversion for Enhanced Real-Time Reservoir Mapping While Drilling
Authors: Thiel Michael , Dupuis Christophe , Omeragic Dzevat 
Published: September 2016

Abstract
The new reservoir mapping-while-drilling technology provides enough data so that a real-time reservoir map can be created through inversion for a layered medium. These reservoir maps are used to optimize well placement and to improve reservoir understanding. Complex reservoirs can be imaged, but the assumption of a layered medium loses its validity when the reservoir cannot be approximated using 1D model locally, i.e., when the reservoir structure varies on the scale of the tool spacing. To improve reservoir mapping performance in such scenarios, an unbiased approximate 2D inversion is introduced that minimizes the error to the layer medium approximation of the reservoir. It is suited for any mildly 2D scenario in which the reservoir does not change abruptly. The method is validated on synthetic and field datasets.
Industrial Waste Injection Feasibility in North Dakota
Authors: Mohamed I. M. , Block G. , Abou-Sayed O. , Abou-Sayed A. S. 
Published: September 2016

Abstract
Class I and Class II waste re-injection are the most important methods for disposing of fluid in North Dakota: in 2007, more than 96% of produced water were disposed of using underground injection, and by 2012 all produced water was being managed by underground injection. While Class II injection covers waste produced from most Exploration & Production (E&P) activities, Class I injection wells are used for disposing of a special class of industrial wastes, including waste generated by petroleum refining, metal production, chemical production, pharmaceutical production, commercial disposal, and food production. Non-hazardous industrial waste and Naturally Occurring Radioactive Materials (NORM) not associated with E&P can also be injected using Class I wells. In all cases, the primary concern for permitting and safe operations is to (1) predict the movement of the injected waste to ensure that it stays within pre-defined formations, and (2) ensure that pore-pressure increases caused by injection do not impact neighboring offset wells.
Results from a geochemical study of the feasibility of disposal into the Dakota Sands (Inyan Kara formation) in North Dakota will be presented. Analyses were made using a compositional reservoir simulation (REVEAL) to predict the pore-pressure distribution, direction and movement of the injected fluid, as well as chemical reactions between formation brine/waste/formation rocks and the effect of these chemical reactions on formation injectivity and cap rock integrity. Forecasts indicate that over 50 years of injection the injected wastes will be completely trapped within the Dakota Sands (no fluid flow is expected to penetrate through the cap rock) and injection pressures are expected to remain well below the estimated fracture pressure. While the Inyan Kara formation is therefore a reasonable storage trap for industrial wastes, carbonate and sulfate scales may cause near wellbore formation damage and rising wellhead pressures which operators will need to address.
Estimation of Inter-Well Connections in Waterflood under Uncertainty for Application to Continuous Waterflood Optimization of Large Middle-Eastern Carbonate Reservoirs
Authors: Zhai Xiang , Wen Tailai , Matringe Sebastien 
Published: September 2016

Abstract
The management of large and mature waterfloods is a notoriously challenging exercise. The vast amount of data available usually cripples reservoir simulation efforts and operational teams usually revert to simple classical engineering calculations, diagnostics plots and maps to make their decisions. Some powerful technologies based on reduced-physics modeling have been developed over the past decade to address this issue. In this paper, we present one such approach that was designed for the management of a large Middle-East carbonate waterfloods.
The reservoir model used is based on the Surveillance Model proposed by Batycky et al. (2008) but differs from it in two aspects: the inter-well allocation factors are computed through the solution of a tracer equation rather than through streamline computations and the fractional flow behavior is estimated through an empirical model rather than computed numerically. Using the tracer allows an improved treatment of unstructured grids and dual-porosity systems, both features being important for the application of interest. Modifying the fractional flow model allows for the automation of the history-matching step. The model can thus integrate new data quickly and estimate the strength and efficiency of each inter-well connection.
An optimization algorithm is used to translate the reservoir management strategy of the asset team in terms of an objective function and a series of constraints at the well, well-group or facility level. Constraints such as voidage replacement ratios, surface facility limits, fracturing pressures can be integrated into the optimization engine to control the field.
A new uncertainty modeling process uses a Markov-Chain Monte-Carlo algorithm to evaluate the robustness of each recommended change. The less mature or less data-rich areas of the field are typically harder to calibrate and more uncertain. Decisions to change the rate of a producer or injector in those areas are more risky. The algorithm is able to quantify this risk to help the operator make a more informed decision. As the field gains in maturity, the algorithm shows how the model learns with new data and how the proposed decisions continuously gain in robustness.
The application of the methodology to giant Middle-East carbonate fields is discussed. The proposed methodology was able to integrate all relevant facility, well group, individual well and reservoir constraints but remains fast enough to be run daily as new data becomes available.
Effects of Confined Space on Production from Tight Reservoirs
Authors: Stimpson Brian C. , Barrufet Maria A. 
Published: September 2016

Abstract
A rigorous method is presented for incorporating confined space into thermodynamic modeling for use in compositional reservoir simulation. Capillary pressure is incorporated as a function of pore size, wettability, and fluid phase properties. Simulated reservoir production is compared for cases with and without including capillary pressure effects due to small pores. During thermodynamic modeling, oil and gas phases are assumed to exist at different pressures, which difference is the capillary pressure. This becomes significant for small pores and is described by the Young-Laplace equation. Including capillary pressure using this method effectively and rigorously couples pore size with the thermodynamic calculations involved in compositional reservoir simulation. The interfacial tension (IFT) required for capillary pressure calculations is determined using the Weinaug and Katz parachor method. These methods are incorporated into an in-house developed compositional reservoir simulator to predict changes in production. Results from modeling show changes in fluid behavior due to confinement. The phase envelope calculated including capillary pressure exhibits a decrease in the cricondenbar and increase in cricondentherm compared to the bulk space phase envelope. Therefore, the bubble point pressure decreases and two-phase flow is reached later in production from an oil reservoir. When the reservoir contains both oil and gas phases, the oil saturation is higher in confined space compared to bulk space. Simulation results show that in a tight oil reservoir, oil production rate including confinement is larger than when assuming no capillary pressure due to this higher liquid saturation. Therefore, produced gas-oil ratio is smaller. Implementation of capillary pressure into compositional reservoir simulation until present has been achieved by use of the Leverett j-function with and without IFT correction. The method proposed here is more rigorous in that capillary pressure is calculated based on pore size and fluid compositions. Thus, as fluid compositions in the reservoir change due to production, capillary pressure successfully reflects those changes.
A Rigorous Upscaling Procedure to Predict Macro-scale Transport Properties of Natural Gas in Shales by Coupling Molecular Dynamics with Lattice Boltzmann Method
Authors: Ning Yang , He Shuai , Liu Honglin , Wang Hongyan , Qin Guan 
Published: September 2016

Abstract
It is well known that shale formations exhibit multi-scale geological features such as nanopores in the formation matrix and natural fractures at multiple length scales. The key challenge in unconventional reservoir simulations is thus how to preserve fine-scale information in coarse-scale reservoir simulations for correct production forecasting and reserve estimation. Accurate prediction of shale permeability using numerical tools requires understanding of transport mechanisms in nano-scale, and upscaling from nano-scale to larger scale simulations. In our recent work (URTeC: #2459219), we presented the coupling of the molecular dynamics (MD) simulation with the lattice Boltzmann method (LBM) on multiple-scale digital rocks to estimate the transport property of shale matrix in micrometer scale. As an extension, this work is aimed to develop an upscaling workflow that integrates nanometer-scale simulations, micrometer-scale simulations and centimeter-scale simulations. The proposed approach allows calculating macro-scale transport properties of natural gas in shales with significantly reducing the loss of critical fine-scale (nano-scale) information.
The reconstructions of multi-scale shale digital rocks are performed using multiple imaging techniques, i.e. FIB-SEM, Nano-CT and Micro-CT. These experiments provide micro-scale pore architectures (∼nm), meso-scale mineralogical distribution (∼μm), and macro-scale natural-fracture network (∼cm), respectively. These multi-scale digital rock reconstructions are then utilized for the investigations of multi-scale transport properties of gas shales. This upscaling process can be summarized as the following three steps. (1) nano-scale transport properties in organic and inorganic structures are calculated using the non-equilibrium MD simulations. Representative organic (kerogen) and inorganic clay (montmorillonite) molecules are built upon their molecular formulas. Transport properties determined from MD simulations are then served as input parameters for LBM simulations in larger scale; (2) micro-scale properties of each component are mapped stochastically on its corresponding voxels in Nano-CT digital rocks. The meso-scale permeabilities of Nano-CT digital rocks are then calculated using the generalized LBM model in porous media; (3) the effective permeabilities of the macro-scale shale digital rock (Micro-CT) with micro-fracture networks are calculated using the generalized LBM model, in which the matrix permeabilities obtained from the step 2 and the transport properties of micro-fractures are served as simulation inputs in macro-scale.