bluuebunny/crossref_metadata_2025 huggingface dataset:
Lend a Hand to Save a Hand: An Innovative Communication Campaign to Reduce Hand and Finger Injuries
Authors: Baistrocchi Monica
Published: February 2011

Hands and fingers are essential for working in the oilfield industry as well as to our daily life. In 2009, Halliburton's HSE statistics in Continental Europe revealed an alarming trend: 51% of injury-related incidents were a result of injuries to hands and fingers—almost one hand and finger (H&F) injury per month! Those injuries occurred mostly during rig up and rig down, where the use of hand tools increases the risk of injury to hands and fingers (Fig. 1). Although injured personnel had more than 7 years of service experience, the majority of H&F injuries were a result of poor safety habits. In an effort to reduce hand and finger injuries, a goal was set to reduce H&F injuries by 50% in 2010, and for this purpose, we launched the "Lend a Hand 2 Save a Hand" campaign.
Managing Use of Local Content in Upstream Oil & Gas
Authors: Marshall Jessica M. , Serwinowski Mark A.
Published: February 2011

Description of the Material

There have been growing concerns among stakeholders and policy makers on the need to increase local content capacity and participation in the oil and gas industry.

As new markets develop and host countries develop local content policies and performance expectations, the oil and gas development companies face increasing complexities in supply chain integration of local content. In many instances, performance assurance with local content performance expectations is becoming a threshold for obtaining and/or maintaining a company's social license to operate.

This paper chronicles one company's approach to leverage local content as part of their supply chain management strategy to grow capacity while managing and fulfilling stakeholder expectations. It documents how one upstream organization integrated use of local content into their own methodology for HSE & SR performance by evaluating the strategy, training, processes, and management systems employed within their own organization and deploying to their supply chain.

The Application

The findings will show how any oil and gas organization - by evaluating the execution of their local content / supply chain strategy – can improve performance of there development projects through local content capacity building of suppliers.

The Results, Observations and Conclusions

The paper will detail the existing performance of the organization and how they developed a 90 day - 5 year plan for improving their own HSE & SR performance as well as the performance of their suppliers. It will evaluate the strategy employed by the organization, the processes, the management systems and tools as well as the training and human capabilities.

Significance of the Subject Matter

Building local capacity positions a company to leverage knowledge, skill and capacity of local markets. Performance equates to risk mitigation (reputational risk; safety incident avoidance through engagement of local skilled worker; lower cost of capital) to business model innovation (operations efficiency; lower lifting cost, faster time to market).
Multidimensional Cell Based Schemes for Hyperbolic Systems with Gravity in porous media on Structured and Unstructured Grids
Authors: Lamine Sadok , Edwards Michael G.
Published: February 2011

Standard reservoir simulation schemes employ single-point upstream weighting for approximation of the con-vective fluxes when multiple phases or components are present. These schemes introduce both coordinate-line numerical diffusion and crosswind diffusion into the solution that is grid and geometry dependent. Families of locally conservative cell-based multidimensional upwind schemes that reduce both directional and crosswind diffusion are presented for convective flow approximation in porous media. The schemes are coupled with full-tensor Darcy flux approximations and handle general flow situations involving counter current gravity flows and systems of hyperbolic equations.

Characteristic vector upwind approximations are proposed and compared with the classical upstream weighting upwind schemes for the case of gravity segregated flows. When dealing with systems of hyperbolic equations, characteristic wave decomposition upwind is used in combination with different limiting strategies involving conservative, primitive and characteristic variables. Alternate wave vector tracing approximations are proposed based on phase velocities and characteristic velocities and comparisons are presented. The cell-based multidimensional formulations are designed for unstructured grids and are constructed such that are stable subject to conditions on the tracing direction and satisfy a local maximum principle that ensures solutions are free of spurious oscillations.

Benefits of the resulting schemes are demonstrated for gravity segregated flows involving three component two phase flow systems. The cell based multidimensional schemes are shown to effectively reduce crosswind diffusion, leading to improved resolution of concentration and saturation fronts.
Building Centroidal Voronoi Tessellations For Flow Simulation In Reservoirs Using Flow Information
Authors: Merland R. , Lévy B. , Caumon G. , Collon-Drouaillet P.
Published: February 2011

The generation of reservoir grids has to take into account numerous flow parameters, static and dynamic, from the fine-scale geological models to minimize discretization errors. These parameters are generally encoded separately as constraints on cell size, orientation and aspect ratio. In this paper, we propose to encode them all at a time in a Riemannian metric tensor field and to apply a global optimization method. This method is based on Centroidal Voronoi Tesselation algorithms under Lp norm and generates unstructured hex-dominant reservoir grids, optimum in terms of sampling.

We apply these principles to generate flow-based reservoir grids. We use a fine-scale velocity field to compute the norm and the directions of the metric tensor: the generated grids are refined in regions of high flow and the cell facets are oriented along the streamline directions. The grids are therefore suitable to a discretization with two-point-flux approximation. The simulation results obtained with these grids are then compared with those computed on a standard Cartesian grid of the same size. These first results are encouraging and need further investigation.

The method is general, and can account for other dynamic parameters, such as vorticity, that can be weighted and introduced in the metric tensor. Furthermore, CVT algorithms can be adapted to take into account fine-scale static features in the grid generation process. Because the gridding is fully automatic, a possible extension of this work is to update the grid between simulation time steps to reflect changes in boundary conditions.
A Chemical Manufacturer's Experience of REACH First Phase Registration
Authors: Henson P. D. , Pritchard M. D. , Weare R..
Published: February 2011

Europe's Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) Regulation (Official Journal of European Union, 2006) is the most wide-ranging piece of legislation that has ever been applied to the chemical industry. However its impact is not confined to that industry alone because REACH is designed to control not only which chemicals may be manufactured and imported into the European Union but also where and how they may be used. There are, therefore, significant implications for the oil and gas industry, which is a substantial user of chemicals.

Every item supplied to the oil and gas industry is composed of chemicals and therefore governed by REACH. However, the focus of this paper is on the speciality chemicals that are used in drilling and production operations. Many of these chemicals are complex and ill-defined materials that are designed to achieve a certain level of performance rather than to meet specific chemical composition requirements. Consequently the manufacturers and suppliers of such materials find themselves challenged by legislation that is better suited to well-defined chemical species.

The deadline for completion of first phase registrations under the transitional arrangements for existing materials expired at the beginning of December 2010. During this initial phase, manufacturers/suppliers were confronted with a number of scenarios that have not arisen previously. They have been obliged to cooperate with their suppliers, competitors and customers in formal data sharing arrangements while ensuring strict compliance with competition rules.

Such efforts have been hindered by the difficulties involved in analysing very complex materials and determining what is an acceptable degree of sameness between them. In addition, manufacturers have had to cope with the intricacies of environmental testing protocols to satisfy the regulatory endpoints.

This paper outlines the challenges and describes how one company has addressed them.
Towards Multiobjective History Matching: Faster Convergence and Uncertainty Quantification
Authors: Hajizadeh Yasin , Christie Mike , Demyanov Vasily
Published: February 2011

Today we have 50 years of research in assisted history matching that bring us many fascinating frameworks to obtain multiple history matched models. Recently the evolutionary optimization algorithms for history matching have enjoyed an increasing popularity in our community. However these methods are often criticized for their high computational demands. We are looking to improve the convergence speed of these algorithms while maintaining their ability to obtain multiple history-matched models to have realistic uncertainty quantification.

In this paper, we discuss a new approach to history matching and uncertainty quantification using multiobjective stochastic population-based optimization. The current practice in industry is to sum the individual match results from wells and minimize a global misfit value in order to get a history matched model. This work proposes a methodology where we can handle different objectives of history matching separately. We extend original differential evolution (DE) algorithm to optimize multiple objectives. The new algorithm is called differential evolution for multiobjective optimization using Pareto ranking (DEMOPR). We couple this algorithm with Bayesian uncertainty quantification framework to estimate the uncertainty in future recovery.

The DEMOPR algorithm is tested for history matching and uncertainty quantification of the PUNQ-S3 reservoir. We have discussed the benefits of new multiobjective approach in fulfilling the targets of PUNQ-S3 problem: obtaining good history matching results and accurate prediction of the ultimate oil recovery. Results are very promising and show better final misfit values and simultaneously, two times increase in the speed of history matching in comparison with the original DE algorithm. We also study the number of models required to reach a stable Bayesian credible interval in quantifying the production uncertainty. We show that multiobjective approach stabilizes faster than original DE which means we need fewer simulations to have a reliable uncertainty estimate in the proposed workflow.
Estimation of Distribution Algorithms Applied to History Matching
Authors: Abdollahzadeh Asaad , Reynolds Alan , Christie Mike , Corne David , Williams Glyn , Davies Brian
Published: February 2011

The topic of automatically history-matched reservoir models has seen much research activity in recent years. History matching is an example of an inverse problem, and there is significant, active research on inverse problems in many other scientific and engineering areas. While many techniques from other fields such as Genetic Algorithms, Evolutionary Strategies, Differential Evolution, Particle Swarm Optimisation, and the Ensemble Kalman Filter have been tried in the oil industry, some more recent and effective ideas have yet to be tested. One of these relatively untested ideas is a class of algorithms known as Estimation of Distribution Algorithms (EDAs). EDAs are population-based algorithms, which use probability models to estimate the probability distribution of promising solutions, then to generate new candidate solutions. EDAs have been shown to be very efficient in very complex high-dimensional problems.

An example of a state of the art EDA is the Bayesian Optimisation Algorithm (BOA), which is a multivariate EDA employing Bayesian Networks for modelling the relationships between good solutions. The use of a Bayesian Network leads to relatively fast convergence as well as high diversity in the matched models. Given relatively limited number of reservoir simulations used in history matching, EDA-BOA offers the promise of high quality history matches with a fast convergence rate.

In this paper, we introduce EDAs and describe BOA in detail. We shows results of EDA-BOA algorithm on two history matching problems. First, we tune the algorithm and demonstrate convergence speed and search diversity on the PUNQ-S3 synthetic case. Secondly, we apply the algorithm to a real, North Sea, turbidite field with multiple wells. In both examples, we show improvements in performance over traditional population-based algorithms.
Simulation of Flow in Discrete Deformable Fractured Porous Media
Authors: Monteagudo Jorge E. , Rodriguez Adolfo A. , Florez Horacio
Published: February 2011

We investigate the coupled flow/geomechanics behavior of naturally fractured permeable rocks. Model and simulation of these processes is very challenging due to the multi-scale nature of the problems and the strong coupling that exists between flow and mechanical behaviors. We present a numerical procedure for the simulation of two-phase immiscible and compressible flow coupled to mechanical deformation in two-dimensional discrete-fractured porous media. To achive this coupling, we implemented an iterative procedure between the flow and the mechanical deformation simulations in which the fluid pressure in the fracture is used as boundary condition at fracture walls. The deformation problem is solved by discretizing the poroelastic equilibrium equations following a Galerkin Finite Element approach, while the flow part was solved using the discrete fracture control volume method. For the flow simulation fractures are assumed to be n-1 dimensional elements where n is the dimension of the overall problem. We considered continuity in capillary pressure and the implied discontinuity in saturation. The model introduces a coupling between mechanical behavior and flow properties by relating absolute permeabilities to the fracture deformation. This approach allows us to study the effects of the coupling on the oil recovery in stress sensitive fractured porous media. We present results for the simulation of synthetic cases to illustrate some of the geomechanical effects that may arise during oil recovery in fractured reservoirs.
Are Reduced Methods for EOS Calculations Worth the Effort?
Authors: Haugen Kjetil B. , Beckner Bret L.
Published: February 2011

EOS calculations are a time consuming part of reservoir simulation. For some time, various authors have presented encouraging speed improvements based on different versions of the reduced method. The fundamental idea is to lower the computational cost by reducing the number of independent variables. This paper presents a careful comparison of conventional and reduced EOS algorithms, applying identical solution strategies and the same level of optimization. The results show smaller performance gains than in previously published material. The main reason for the less optimistic speed-up numbers seems to be that more performant conventional EOS algorithms are used as a reference.
Modeling Aqueous Phase Behavior and Chemical Reactions in Compositional Simulation
Authors: Nghiem Long , Shrivastava Vijay , Kohse Bruce
Published: February 2011

Compositional simulators are being increasingly used for modeling of green house gas storage in deep saline aquifers. It requires handling of chemical equilibrium reactions that occur between the injectant (normally CO2), reservoir water and the minerals. The composition of injected fluids, reservoir water, minerals and the operating conditions of pressure and temperature assume significance in such a scenario. This paper presents a framework for inclusion of chemical equilibrium reactions into an equation-of-state compositional simulator. Two main methods for modeling chemical equilibrium reactions, viz., element balance and primary species approaches are discussed and it is shown that both the approaches are equivalent. Methods for modeling gas solubility into the aqueous phase, and vaporization of H2O into gas phase are also presented along with examples showing the effect of aquifer mineralogy on the mineralization of CO2 and the dry-out and salt deposition due to water vaporization.
Parallel Simulation of CO2 Sequestration with Rock Deformation in Saline Aquifers
Authors: Winterfeld P. H. , Wu Y. S.
Published: February 2011

We developed a massively parallel reservoir simulator for modeling CO2 flow and transport in saline aquifers coupled with geomechanical processes that would occur during CO2 sequestration. These geomechanical processes, namely rock deformation, affect rock permeability, porosity, and bulk volume. The theories of hydrostatic and linear poroelasticity, and correlations from the literature, are used to obtain dependencies of rock permeability, porosity, and bulk volume on pore pressure and average stress. These dependencies are incorporated into the simulator mass and energy conservation equations, which determine pressure, temperature, and fluid composition. The simulator is based on the TOUGH2-MP code (Zhang et al. 2008) with the ECO2N module (Pruess, 2005), which calculates properties of H2O-NaCl-CO2 mixtures. These mixtures consist of one aqueous and one CO2-rich phase, salt can precipitate and dissolve in the aqueous phase, and the mixture temperature may change. The simulator is fully implicit, three-dimensional, the grid is unstructured, and the linear equations associated with the conservation equations are solved in porous and fractured media. The simulator code is parallelized and uses MPI for processor communication, the METIS software package for domain partitioning, and the Aztec solver package for linear equation solution.

The simulator formulation, numerical implementation, and computational efficiency are verified using example problems from the literature, and options for the above dependencies are also illustrated. One example problem (Birkholzer et al., 2008) is a study of the volume influenced during and after CO2 injection into a saline aquifer. Another (Rutqvist and Tsang, 2002) addresses coupled hydromechanical changes during CO2 injection into an aquifer–caprock system. In this example, geomechanical processes were modeled by coupling two simulators, TOUGH2 (Pruess et al., 1999), which simulates fluid flow and heat transport, and FLAC3D (FLAC3D, 1997), which simulates rock and soil mechanics with thermomechanical and hydromechanical interactions. Finally, the last example problem (Kumar et al., 2005) is a simulation of a prototypical CO2 sequestration project in a deep saline aquifer using CMG's GEM simulator (Nghiem, 2002).
Sequential Quadratic Programming (SQP) for Solving Constrained Production Optimization — Case Study from Brugge Field
Authors: Dehdari Vahid , Oliver Dean S.
Published: February 2011

Normally only about 30% of the oil in a reservoir can be extracted, but using secondary production methods such as water or gas injection it is often possible to increase that percentage significantly and maintain the production rate of a reservoir over a longer period of time. In reservoirs under water or gas injection additional gains can be obtained through an efficient strategy management of front movement and reservoir sweep. The objective of reservoir production optimization is to maximize an outcome such as sweep efficiency or net present value (NPV) through the control of completion rates or pressures. Using optimization methods, it is possible to compute control settings that result in increased oil production and decreased water production compared to production from standard practices. In this paper, we focus on optimization using SQP with an ensemble-based approach to estimate the gradient for the optimization. Although uncertainty in reservoir properties is usually important for the computation of optimal controls, here we use a single realization of the reservoir to evaluate the efficiency of the optimization algorithm.

The most expensive aspect of gradient-based optimization is usually the computation of gradients. Most practical production optimization problems involve large-scale, highly complex reservoir models with thousands of constraints, which makes numerical calculation of the gradient time consuming. Here, we use an ensemble-based approach for finding gradients and use localization to improve estimation of the gradient from a small number of realizations. The BFGS method is used for maximizing the objective function, with the Hessian estimated from a sequence of estimates of the gradient. Improving the gradient approximation using localization results in improvement to the Hessian approximation. A second important aspect of the efficiency of the method is the identification of active constraints. In this paper, we use a method for eliminating non-negativity constraints to decrease computation time and an updating procedure to solve each iteration of SQP much faster than the base case. Both the speed of the algorithm and the final NPV were increased significantly.

We evaluate the method by applying it to optimization of control settings in the Brugge field. Brugge is a 3D synthetic model designed by TNO with 20 vertical producers and 10 vertical peripheral water injectors. All of the producers and injectors are smart wells whose downhole chokes must be adjusted to optimize NPV. The total number of completion flow rates to be controlled is 84 at each time step, with 40 time steps (every six months). There are 1200 inequality constraints on total well liquid rates and 3360 non-negativity constraints on completion liquid rates. There are also inequality constraints on the bottom-hole pressure for wells at each time period.
Meshless Multi-Point Flux Approximation of Fluid Flow in Porous Media
Authors: Lukyanov Alexander A.
Published: February 2011

In the last decade, the multi-point flux approximations have found significant interest. However, non-physical oscillations can appear in the developed multi-point flux approximations when the anisotropy is really strong. It has been found that the oscillations are closely related to the poor approximation of pressure gradient in the flux computation. In this paper, the meshless multi-point flux approximation (MMPFA) for general fluid flow in porous media is proposed. The MMPFA is based on a gradient approximation commonly used in thermal, viscous, and pressure projection problems and can be extended to include higher-order terms in the appropriate Taylor series. The proposed MMPFA is combined with the mixed corrections which ensure linear completeness. The mixed correction utilizes Shepard Functions in combination with a correction to derivative approximations. Incompleteness of the kernel support combined with the lack of consistency of the kernel interpolation in conventional meshless method results in fuzzy boundaries. In corrected meshless method, the domain boundaries and field variables at the boundaries are approximated with the default accuracy of the method. The resulting normalized and corrected MMPFA scheme not only ensures first order consistency O(h) but also alleviates the particledeficiency (kernel support incompleteness) problem. Furthermore, a number of improvements to the kernel derivative approximation are proposed. The primary attraction of the present method is that it provides a weak formulation for Darcy's law which can be of use in further development of meshless methods. The SPH model can be used to model three-dimensional miscible flow and transport in porous media with complex geometry, and for large (field) scale simulation of transport in porous media with general permeability distributions. To illustrate the performance of the MMPFA, a modelling of a single phase fluid flow in fully anisotropic porous media is presented.
RAINBOW: An Innovative Approach for Quantitative Risk Assessment of Blowout Events
Authors: Borello L.. , De Ghetto G.. , Blotto P.. , Andreussi P.. , Bonuccelli M.. , Fratini M.. , Moretti M..
Published: February 2011

The uncontrolled eruption of a well introduces safety risks for field operators, potential health injury to people living in proximity of the field and impacts on the environment. The availability of validated predictive tools for quantitative risk assessment of the event is essential not only in design activities or authorization procedures, but also to support the emergency response.

Thanks to a decennial collaboration between eni E&P and TEA Sistemi, RAINBOW, an integrated approach for the analysis of a blowout event, has been developed. Through an easy-to-use interface, the user may define fluid characteristics, well geometry, fluid flow-path and environment characteristics to compute the blowout flowrate and to determine the toxic, flammable, heat radiation and oil contamination maps. Alternatively, the release flowrate and characteristics can be computed separately and supplied to the software externally.

RAINBOW consists of a Graphic User Interface that puts different models together for simulating separately each phenomenon characterizing a blowout event: multiphase release, spray formation and evaporation, jet development, light or dense gas dispersion, oil rain-out and soil contamination. Some of these models have been specifically developed through R&D activities to fill a gap highlighted by the technical literature. Others are worldwide recognized free software, assessed and selected through experimental and Computational Fluid Dynamics activities. The RAINBOW main structure allows the refined and complex computational tools to be applied without requiring specific skills to the user.

Thanks to an in-house module for risk recombination, user may choose to perform simple consequence analysis or complete QRA, in both short and long term approaches. A specifically developed protocol is included in the software for conservative and robust frequency assessment of various well types (onshore, offshore, normal, HTHP) during the different well operational phases, including all credible flow-paths associated with each operation. Risk areas are presented as maps, reporting Local Specific

Individual Risk thresholds.

Its easiness to use and high running velocity make RAINBOW a suitable support tool for any phases of field development, Risk Management and Emergency Response planning.
Robust Optimization of SAGD Operations under Geological Uncertainties
Authors: Yang Chaodong , Card Colin , Nghiem Long , Fedutenko Eugene
Published: February 2011

Various studies have shown that optimization of SAGD well locations and operating conditions has a great potential to improve the economics of SAGD operations. However, such optimization often ignores geological uncertainties of the reservoir and is based on a single realization. Due to the significant impact of reservoir properties on SAGD performance, the optimal solution obtained based on a single realization may deviate severely from the actual optimality.

This paper presents a SAGD optimization workflow which takes into account geological uncertainties of the reservoir. To capture geological uncertainties, a large number of realizations need to be generated honoring geological constraints. Due to the high computational cost of SAGD simulations, it is impractical to use all realizations in the optimization workflow. So the first step of the workflow is to select a small set of realizations that represent the overall uncertainty of the reservoir. This is achieved by ranking all realizations according to the performance of each realization in terms of net present value (NPV) under the base operating conditions. Based on the ranking, a small set of representative realizations are chosen to represent the overall uncertainty of the reservoir. With the selected representative realizations, a robust optimization methodology is applied to account for the uncertainty of the reservoir model. In robust optimization, the decision-maker seeks an optimal risk weighted solution that is most likely to give good performance for any realization of the uncertainty in a given set. The robust optimization objective consists of two components: the expected value and standard deviation of NPV over the set of representative realizations. The weighting of the standard deviation term can be adjusted to reflect the risk tolerance of the decision-maker. Finally, the robust optimization problem is solved using an optimization algorithm assisted by second-order polynomial proxy models.

The robust optimization procedure is applied to a SAGD model with three well pairs and 100 realizations. The results are compared with the optimization results obtained from a single realization (nominal optimization). The comparison shows that the robust optimization workflow not only increases the average NPV but also increases all the NPV percentile values (P10, P20, …, P90). This indicates the increased robustness of the optimal solution under geological uncertainties and thus, adoption of the robust optimization procedure can significantly reduce the project risk.
Use of Approximate Dynamic Programming for Production Optimization
Authors: Wen Zheng , Durlofsky Louis J. , Van Roy Benjamin , Aziz Khalid
Published: February 2011

In production optimization, we seek to determine the well settings (bottomhole pressures, flow rates) that maximize an objective function such as net present value. In this paper we introduce and apply a new approximate dynamic programming (ADP) algorithm for this optimization problem. ADP aims to approximate the global optimum using limited computational resources via a systematic set of procedures that approximate exact dynamic programming algorithms. The method is able to satisfy general constraints such as maximum watercut and maximum liquid production rate in addition to bound constraints. ADP has been used in many application areas, but it does not appear to have been implemented previously for production optimization. The ADP algorithm is applied to two-dimensional problems involving primary production and water injection. We demonstrate that the algorithm is able to provide clear improvement in the objective function compared to baseline strategies. It is also observed that, in cases where the global optimum is known (or surmised), ADP provides a result within 1-2% of the global optimum. Thus the ADP procedure may be appropriate for practical production optimization problems.
New Phase Behavior Algorithm for Simulation of CO2 Storage
Authors: Lazaro-Vallejo L. , Leahy M. , Dance T. , LaForce T.
Published: February 2011

Trapping carbon dioxide underground is considered a key technology to mitigate climate change on a large scale. Numerous studies have shown how injection strategies and geological uncertainty have an impact on how much CO2 can be trapped as residual saturation (Qi, 2009a; Qi, 2008 and Obi, 2004). In cases of large uncertainty such as deep saline aquifers, simulations are run on a high number of possible geological realisations to compensate for the lack of data. Consequenty, computation times can become prohibitive. As a result, it is necessary to develop fast and more physically accurate methods for CO2 storage simulations. Streamline-based simulations are a good tool for this as they divide the 3D field into a set of multiple 1D problems that are easier and quicker to solve. Typical streamline-based simulators perform advective-flow transport on the streamlines while the rest of the physical processes are calculated on the underlying grid. This makes the simulation dependent on the number of pressure time-steps. This paper shows that the number of pressure time-steps can affect the convergence of results due to phase behavior errors. We propose two new phase behavior algorithms that assume thermodynamic equilibrium along the streamlines and use them to extend our existing in-house 3-component 2-phase streamline simulator. In order to validate our new algorithm, a 1D simulation was compared against 1D analytical Buckley-Leverett solution. All the algorithms were validated on 2D and 3D sections of the SPE 10th Comparative Model using water alternating gas (WAG) injection followed by chase brine. Results show that both new thermodynamic algorithms were faster in terms of CPU cost and had a faster convergence of results. Based on the validated model, we performed simulations on a geological model of the stage 2 CO2CRC Otway Project to test residual trapping. The injection strategy followed was similar to that described in Zhang et al. (2010) and the aim was to compare our results to TOUGH2 results. Streamline and TOUGH2 predicted similar CO2 profiles. The results indicate that more than 79% of the CO2 injected can be trapped as residual saturation in the deep saline aquifer.
Application and Variance Based Sensitivity Analysis of Surfactant-Polymer Flood
Authors: Mollaei Alireza , Lake Larry W. , Delshad Mojdeh
Published: February 2011

We study the performance and behavior of surfactant-polymer (SP or micellar-polymer, MP) flooding enhanced oil recovery (EOR) using an analytical chemical flood predictive model (CFPM). The research has two parts based on the deterministic and the stochastic nature of the problem.

In a deterministic study, the SP flood performance (ultimate recovery efficiency and oil-rate vs. time) of TORIS reservoir database (theTertiary Oil Recovery Information System) was predicted using the modified CFPM. Results helped to determine the best candidates for SP flooding based on each reservoir's rock and fluid properties. From there we can determine the effect of different parameters (reservoir rock and fluid properties, injection design variables) on ultimate recovery efficiency and peak oil rate of an SP flood, which gives good clues about the sensitivity of output results to different input parameters.

To recognize the behavior of the model under for uncertain inputs we used a variance-based sensitivity analysis (SA) method known as Winding Stairs (WS), which needs much fewer runs than traditional Monte-Carlo (MC) and Latin Hypercube methods. The results of the SA method helped us to identify the most important sources of uncertainty of SP floods either through direct influences or interactions with other parameters. Based on these results we can reduce the uncertainty of output results of SP flood significantly by reducing the uncertainty of the input parameters that cause the largest uncertainty.
Numerical Experiments with AMG Solver in Reservoir Simulation
Authors: Mishev Ilya , Shaw Jason , Lu Pengbo
Published: February 2011

AMG (Algebraic Multi-Grid) solver proved to be one of the most efficient solvers handling linear systems from discretization of the pressure equation in IMPES and Sequential Implicit formulations. We share our experience in using it in production simulations. We also discuss how AMG solver can be used as a component in composite solvers for other formulations or in parallel algorithms.

Classical AMG (Ruge-Stuben) approach is very well suited for monotone matrices from finite volume discretizations, but cannot solve efficiently linear systems with the facility unknowns included because of the different nature of these equations. We have chosen to alleviate this problem by using the multiplicative Schwarz framework. One application of this preconditioner consists of solving the facility system to a relatively loose tolerance followed by one AMG V-cycle on the pressure system. Our experiments confirm there is no loss of efficiency due to the facility unknowns. Moreover, the same approach can be extended naturally to Adaptive Implicit (AIM) formulation with different parts of the matrix handled by different linear solvers. AMG is also very efficient when used as a sub-domain solver in the overlapping additive Schwarz domain decomposition algorithm. The straightforward generalization of the serial algorithm produced unexpected results with respect to the robustness of the algorithm. The theoretical analysis provided valuable insight on the interplay of the multiplicative and additive Schwarz methods and showed us how to reorganize the algorithm to correct the problems. We also report our new findings on the influence of the overlap size between the sub-domains on the convergence of additive Schwarz method.
Interior-Point Formulation for the Constrained Ensemble Kalman Filter
Authors: Phale Hemant A. , Oliver Dean S.
Published: February 2011

The standard formulation of the ensemble Kalman filter (EnKF) does not take into account the physical constraints on state variables during the data assimilation step so constraint violations are often handled heuristically. The physical constraints often contain valuable information about the system which are critical for the valid initialization and forecast. A successful enforcing of these constraints during the data assimilation step of the EnKF is necessary for valid estimation of reservoir properties.

A number of solutions to the problem of constraint violation have been proposed in the past including the constrained EnKF (CEnKF), iterative EnKF, transformation of state variables, reparameterization, and truncation. In this paper, we present an interior-point method for incorporating the inequality constraints into the EnKF methodology (IPCEnKF). In this approach, the objective function for data assimilation is reformulated by adding a barrier function to penalize proximity of the state variables to the boundaries of the feasible region and to set a barrier on the state variables against leaving the feasible region. The proposed method is efficient compared to the CEnKF as it does not require the identification of the active constraints. Although the method is iterative to reduce the effect of the barrier term at each assimilation step, the iterations do not require running the simulator.

The method is successfully tested first on a 1D linear example to illustrate the performance when nonlinearity is not an issue, then on a more realistic 3D, three-phase reservoir flow assimilation problem based on the modified SPE9 model. Results from the reservoir problem show the effectiveness of the newly proposed IPCEnKF method in matching the observations and honoring the inequality constraints on phase saturations. The proposed method is able to achieve a better estimate of reservoir properties than is obtained using only truncation with the standard EnKF.
Multiscale parameterization with adaptive localization for improved assimilation of time-lapse data
Authors: Chen Yan , Nourazari Sara , Oliver Dean S.
Published: February 2011

Time-lapse seismic or electromagnetic data are similar to production data in that they are generally strongly sensitive to averages of porosity and permeability over extended regions, but only weakly sensitive to the properties of individual gridblocks. Consequently, updates of model parameters from the ensemble Kalman filter (EnKF) will be subject to substantial error due to spurious correlations when the size of the ensemble is small. Adaptive localization methods will reduce the magnitude of the spurious correlations, but may also eliminate the true, but weak, correlations. For this type of data, there appears to be a relationship between the spatial scale of the model variables and the sensitivity of observations to the variables. By introducing a multiscale expansion with adaptive localization, we are able to increase the sensitivity to the larger scale variables and more accurately compute their magnitudes, while eliminating updates of the small scale variables with weak correlations.

In this paper we compare three approaches to data assimilation for observation operators with weak, nonlocal sensitivity to model variables. The first approach is the EnKF without localization of any kind. The second approach is the EnKF with adaptive bootstrap localization or screening of the Kalman gain. In the third approach, we apply the EnKF with adaptive bootstrap localization to the problem of updating a multiscale expansion of the model variables. These approaches are illustrated through application to several spatially distributed data assimilation problems. The first application is a one-dimensional vector of correlated random variables for which the observations are averages over intervals. The second example is a two-dimensional correlated array of permeability values for which the data are observations of pressure in an observation well at two times. The final example is also a two-dimensional flow problem, but one in which the concentration of an injectant is observed at several times. In the first two examples, the ensemble Kalman filter approach with a multiscale expansion and adaptive localization gave the best results. In the final example, results from adaptive localization with and without the multiscale expansion are essentially equivalent because the correlation of data limits the scale of the features in the Kalman gain matrix.
Multiblock methods for coupled flow-transport and compositional flow through porous media - Applications to the simulation of transport of reactive species and carbon sequestration
Authors: Thomas S. G. , Wheeler M. F.
Published: February 2011

This paper presents multiblock methods for coupled multiphase flow and species transport modeling in porous media applications. These methods provide local mass conservation and a continuous approximation of fluxes across inter-element faces and sub-domain (inter-block) interfaces and can treat non-matching grids, allowing for a flexible choice of grid refinements. Furthermore, they lend themselves naturally to parallel implementations of multiphysics, multinumerics, multiscale applications of porous media flow and transport.

The paper briefly introduces mortar mixed finite element methods (MMFEM) [1] for coupled porous media flow and transport applications, followed by the main emphasis of the paper which is a novel extension of an enhanced velocity mixed finite element method (EVMFEM) [2] to similar problems flow and reactive transport. The paper also presents the formulation of a recent application of EVMFEM to such challenging problems as compositional flow simulations of CO2 sequestration, that is now widely accepted as the most feasible solution to address the growing environmental concerns due to global warming, as well as a very effective means for enhanced oil and gas recovery.

Computational experiments with EVMFEM suggest that it is advantageous to apply grid refinements around wells and where plumes of chemical species are expected to be transported. Allowing for variable grid refinements greatly reduces the simulation cost (wall clock times), compared with single block fine-grid everywhere strategies, while preserving overall accuracy of the solution. Furthermore, initial studies indicate that the implementation is scalable in parallel simulations. For completeness, a few significant analytic results on convergence of the method are stated and referenced, omitting proof.

This work is significant in advancing the understanding and application of multiblock methods in reservoir simulation development. Problems such as transport of chemical species in multiphase flow and CO2 sequestration have begun to assume significant importance in decisions regarding the preservation of our environment and in the safe and reliable means of delivering energy. This paper offers useful methods and some innovative future directions to address the huge computational costs involved in solving such complex problems.
History Matching for Non-Gaussian Random Fields Using the Probabilistic Collocation Based Kalman Filter
Authors: Zhang Dongxiao , Li Heng , Chang Haibin
Published: February 2011

In this work, an approach for estimating non-Gaussian permeability field is developed, through a probabilistic collocation based Kalman filter (PCKF). In this approach, the polynomial chaos expansion is used to parameterize the non-Gaussian permeability field. The state variables are expressed by the polynomial chaos expansion whose coefficients are sequentially updated when observations are available. The probabilistic collocation method is employed to solve for the coefficients of the polynomial chaos expansion. The probabilistic collocation method is non-intrusive to the reservoir model, thus it allows the forward simulations to be performed independently with existing reservoir simulators, as in the Monte Carlo simulation.

The applicability of the approach is demonstrated with black oil problems in spatially heterogeneous reservoirs. Continuous production data are used as observations for sequentially updating the permeability field. The PCKF approach is also compared with the ensemble Kalman filter (EnKF) for investigating the accuracy, efficiency and applicability. It reveals that the PCKF other than the EnKF can be used to efficiently estimate the non-Gaussian permeability field. While keeping the advantages of EnKF, such as the sequential updating and parallelism, the PCKF is more suitable for permeability fields characterized by non-Gaussian random fields.
Three-Phase Compositional Modeling of CO2 Injection by Higher-Order Finite Element Methods with CPA Equation of State
Authors: Moortgat Joachim , Li Zhidong , Firoozabadi Abbas
Published: February 2011

Injection of CO2 in water-flooded hydrocarbon reservoirs and in saline aquifers may require models based on high-order accurate numerical methods. For such applications, cubic equations of state (EOS) and Henry's law may not be sufficiently accurate. The association of water molecules and cross-association of water and CO2 molecules affects the phase behavior of CO2-water mixtures and requires a cubic-plus-association (CPA) equation of state (EOS). In this work, we present a new higher-order three-phase compositional model with the following features: 1) species transfer among the three phases, in particular CO2 solubility in water, 2) an implicit-pressure, explicit-composition scheme that uses the mixed hybrid finite element method to simultaneously solve for the pressure and the total volumetric flux, and a discontinuous Galerkin approximation to update mass transport, 3) Fickian diffusion, 4) efficient solution of the CPA-EOS, 5) advancement in the two-dimensional bisection method for robust phase amount computation, and 6) consistent boundary conditions for the three-phase fractional flow formalism. We present a number of numerical examples with broad applications in improved oil recovery in water-flooded oil reservoirs, and CO2 sequestration.