bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Chemical Manufacturer's Experience of REACH First Phase Registration
Authors: Henson P. D. , Pritchard M. D. , Weare R.. 
Published: February 2011

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Summary
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.