Abstract:
Energy demand has increased considerably with the growth of world population, increasing the interest in the hydrocarbon reservoir management problem. Companies are concerned with maximizing oil recovery while minimizing capital investment and operational costs. A first step in solving this problem is to consider optimal well placement. In this work, we investigate the Differential Evolution (DE) optimization method, using distinct configurations with respect to population size, mutation factor, crossover probability, and mutation strategy, to solve the well placement problem. By assuming a bare control procedure, one optimizes the parameters representing positions of injection and production wells. The Tenth SPE Comparative Solution Project and MATLAB Reservoir Simulation Toolbox (MRST) are the benchmark dataset and simulator used, respectively. The goal is to evaluate the performance of DE in solving this important real-world problem. We show that DE can find high-quality solutions, when compared with a reference from the literature, and a preliminary analysis on the results of multiple experiments gives useful information on how DE configuration impacts its performance.

Abstract:
Determining optimal well placements and controls are two important tasks in oil field development. These problems are computationally expensive, nonconvex, and contain multiple optima. The practical solution of these problems require efficient and robust algorithms. In this paper, the multilevel coordinate search (MCS) algorithm is applied for well placement and control optimization problems. MCS is a derivative-free algorithm that combines global search and local search. Both synthetic and real oil fields are considered, and the performance of MCS is compared to the generalized pattern search (GPS), the particle swarm optimization (PSO), and the covariance matrix adaptive evolution strategy (CMA-ES) algorithms. Results show that the MCS algorithm is strongly competitive, and outperforms for the joint optimization problem and with a limited computational budget. The effect of parameter settings are compared for the test examples. For the joint optimization problem we compare the performance of the simultaneous and sequential procedures.

Abstract:
In the well placement problem, as well as in other field development optimization problems, geological uncertainty is a key source of risk affecting the viability of field development projects. Well placement problems under geological uncertainty are formulated as optimization problems in which the objective function is evaluated using a reservoir simulator on a number of possible geological realizations. In this paper, we present a new approach to handle geological uncertainty for the well placement problem with a reduced number of reservoir simulations. The proposed approach uses already simulated well configurations in the neighborhood of each well configuration for the objective function evaluation. We use thus only one single reservoir simulation performed on a randomly chosen realization together with the neighborhood to estimate the objective function instead of using multiple simulations on multiple realizations. This approach is combined with the stochastic optimizer CMA-ES. The proposed approach is shown on the benchmark reservoir case PUNQ-S3 to be able to capture the geological uncertainty using a smaller number of reservoir simulations. This approach is compared to the reference approach using all the possible realizations for each well configuration, and shown to be able to reduce significantly the number of reservoir simulations (around 80%).

Abstract:
Optimal well placement and optimal well control are two important areas of study in oilfield development. Although the two problems differ in several respects, both are important considerations in optimizing total oilfield production, and so recent work in the field has considered the problem of addressing both problems jointly. Two general approaches to addressing the joint problem are a simultaneous approach, where all parameters are optimized at the same time, or a sequential approach, where a distinction between placement and control parameters is maintained by separating the optimization problem into two (or more) stages, some of which consider only a subset of the total number of variables. This latter approach divides the problem into smaller ones which are easier to solve, but may not explore search space as fully as a simultaneous approach. In this paper we combine a stochastic global algorithm (Particle Swarm Optimization) and a local search (Mesh Adaptive Direct Search) to compare several simultaneous and sequential approaches to the joint placement and control problem. In particular, we study how increasing the complexity of well models (requiring more variables to describe the well's location and path) affects the respective performances of the two approaches. The results of several experiments with synthetic reservoir models suggest that the sequential approaches are better able to deal with increasingly complex well parameterizations than the simultaneous approaches.

Abstract:
Optimum implementation of non-conventional wells allows us to increase considerably hydrocarbon recovery. By considering the high drilling cost and the potential improvement in well productivity, well placement decision is an important issue in field development. Considering complex reservoir geology and high reservoir heterogeneities, stochastic optimization methods are the most suitable approaches for optimum well placement. This paper proposes an optimization methodology to determine optimal well location and trajectory based upon the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES) which is a variant of Evolution Strategies recognized as one of the most powerful derivative-free optimizers for continuous optimization. To improve the optimization procedure, two new techniques are investigated: (1). Adaptive penalization with rejection is developed to handle well placement constraints. (2). A meta-model, based on locally weighted regression, is incorporated into CMA-ES using an approximate ranking procedure. Therefore, we can reduce the number of reservoir simulations, which are computationally expensive. Several examples are presented. Our new approach is compared with a Genetic Algorithm incorporating the Genocop III technique. It is shown that our approach outperforms the genetic algorithm: it leads in general to both a higher NPV and a significant reduction of the number of reservoir simulations.

Abstract:
We present an Evolutionary Placement Algorithm (EPA) for the rapid assignment of sequence fragments (short reads) to branches of a given phylogenetic tree under the Maximum Likelihood (ML) model. The accuracy of the algorithm is evaluated on several real-world data sets and compared to placement by pair-wise sequence comparison, using edit distances and BLAST. We test two versions of the placement algorithm, one slow and more accurate where branch length optimization is conducted for each short read insertion and a faster version where the branch lengths are approximated at the insertion position. For the slow version, additional heuristic techniques are explored that almost yield the same run time as the fast version, with only a small loss of accuracy. When those additional heuristics are employed the run time of the more accurate algorithm is comparable to that of a simple BLAST search for data sets with a high number of short query sequences. Moreover, the accuracy of the Evolutionary Placement Algorithm is significantly higher, in particular when the taxon sampling of the reference topology is sparse or inadequate. Our algorithm, which has been integrated into RAxML, therefore provides an equally fast but more accurate alternative to BLAST for phylogeny-aware analysis of short-read sequence data.

Abstract:
Recent developments in cloud computing and big data have spurred the emergence of data-intensive applications for which massive scientific datasets are stored in globally distributed scientific data centers that have a high frequency of data access by scientists worldwide. Multiple associated data items distributed in different scientific data centers may be requested for one data processing task, and data placement decisions must respect the storage capacity limits of the scientific data centers. Therefore, the optimization of data access cost in the placement of data items in globally distributed scientific data centers has become an increasingly important goal. Existing data placement approaches for geo-distributed data items are insufficient because they either cannot cope with the cost incurred by the associated data access, or they overlook storage capacity limitations, which are a very practical constraint of scientific data centers. In this paper, inspired by applications in the field of high energy physics, we propose an integer-programming-based data placement model that addresses the above challenges as a Non-deterministic Polynomial-time (NP)-hard problem. In addition we use a Lagrangian relaxation based heuristics algorithm to obtain ideal data placement solutions. Our simulation results demonstrate that our algorithm is effective and significantly reduces overall data access cost.

Abstract:
This paper collects heuristics of Go Game and employs them to achieve coverage of dense wireless sensor networks. In this paper, we propose an algorithm based on Go heuristics and validate it. Investigations show that it is very promising and could be seen as a good optimization. Keywords: Go Heuristics, Wireless Sensor Networks, Coverage, Density

The Artificial Bee Colony (ABC) is one of the numerous stochastic algorithms
for optimization that has been written for solving constrained and unconstrained
optimization problems. This novel optimization algorithm is very efficient
and as promising as it is; it can be favourably compared to other optimization
algorithms and in some cases, it has been proven to be better than
some known algorithms (like Particle Swarm Optimization (PSO)), especially
when used in Well placement optimization problems that can be encountered
in the Petroleum industry. In this paper, the ABC algorithm has been modified
to improve its speed and convergence in finding the optimum solution to
a well placement optimization problem. The effects of variations of the control
parameters for both algorithms were studied, as well as the algorithms’
performances in the cases studied. The modified ABC (MABC) algorithm
gave better results than the Artificial Bee Colony algorithm. It was noticed
that the performance of the ABC algorithm increased with increase in the
number of its optimization agents for both algorithms studied. The modified
ABC algorithm overcame the challenge posed by the use of uniformly generated
random numbers with very rough NPV surface. This new modified ABC
algorithm proposed in this work will be a great tool in optimization for the
Petroleum industry as it involves Well placements for optimum oil production.

Abstract:
this work presents and evaluates the performance of a simulation model based on multiagent system technology in order to support logistic decisions in a harbor from oil supply chain. the main decisions are concerned to pier allocation, oil discharge, storage tanks management and refinery supply by a pipeline. the real elements as ships, piers, pipelines, and refineries are modeled as agents, and they negotiate by auctions to move oil in this system. the simulation results are compared with results obtained with an optimization mathematical model based on mixed integer linear programming (milp). both models are able to find optimal solutions or close to the optimal solution depending on the problem size. in problems with several elements, the multiagent model can find solutions in seconds, while the milp model presents very high computational time to find the optimal solution. in some situations, the milp model results in out of memory error. test scenarios demonstrate the usefulness of the multiagent based simulator in supporting decision taken concerning the logistic in harbors.