Denis José Schiozer

A new upscaling technique based on Dykstra-Parsons coefficient : Evaluation with streamline reservoir simulation

Abstract The loss of information is inevitable in any upscaling technique. The efficiency of a given method must take in account two basic aspects: the first is the agreement of the results obtained with the coarse grid when compared to the results obtained with the fine grid and the second is the upscaling computational performance. Upscaling is really justifiable for very fine-scale reservoir models, usually with more than 1 million blocks or when many runs are necessary and the computational performance is very important. Due to the size of such fine-scale models, two problems exist. The first problem is that application of a pressure solver technique (or numerical method, which is generally more appropriate) on the upscaling is very time-consuming. The second problem is that the flux simulation of the fine-scale model, in order to validate a given upscaling technique, is very difficult and sometimes impossible through traditional simulators. In this work, an upscaling technique based on a heterogeneity coefficient (Dykstra–Parsons), which is as efficient and faster than numerical methods, is proposed. All tested fine grid reservoir models were modeled by a streamline simulator. The proposed technique was applied to three case studies obtaining good agreement between coarse and fine grids, taking in account several production parameters.

Selection of Representative Models for Decision Analysis Under Uncertainty

The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request. HighlightsA new optimization-based method to select representative models in oil fields.A new mathematical function that captures the representativeness of a set of models.The mathematical function is combined with an optimization metaheuristic.The proposal was applied to the UNISIM-I-D benchmark problem to validate the methodology.Experts indicate that results are richer than those obtained by other approaches.

Application of Neural Network and Global Optimization in History Matching

This article presents the application of global optimizers combined with Artificial Neural Networks (ANN) to the history matching problem. An evolutionary algorithm is executed on the proxy generated through the ANN technique. The results obtained from evolutionary algorithms are fine-tuned by using a local optimizer based on the Hooke and Jeeves optimization method. The methodology is applied in two reservoir models and promising results were obtained.

Use of Oil Reservoir Simulation to Estimate Value of Flexibility

The selection of production strategy under uncertainties is a complicated task due to the high number of variables and uncertainties. While new information aims to reduce the uncertainty of one or more variables, consequently reducing the risk, flexibility may be used to change field operation in the future. The objective of this work is to estimate the value of flexibility through a risk-return analysis in which a company profile is taken into account represented by the iso-utility curve. The methodology is an extension from Value of Information (VoI) assessment under uncertainties. It comprises a complete uncertainty analysis, use of representative models, generation of risk curves, optimization steps to define the strategy without flexibility which is then simulated in several scenarios verifying the bottlenecks of the strategy that may be assessed through flexibility. Finally, the benefit of each selected flexibility is estimated through risk-return analysis. The work includes a Latin Hypercube technique to combine uncertain scenarios and the use of an assisted optimization procedure to select the production strategy. It is then applied to a 28 o API, low viscosity offshore oil field including production history. Results indicate that this methodology is able to identify flexibility, in this case, the expansion of production capacity, which is then added to the production strategy with two objectives: to mitigate risk and to increase value. The tested flexibility changes the project risk and return in both objectives and allows the company to produce more efficiently in different scenarios, by producing with a higher use of installed capacity. The main conclusions are that the flexibility of production capacity expansion can be used not only to mitigate risk, but also for value creation, allowing the company to adapt its production strategy as new information is revealed. The main contribution of this work is a new perspective in risk assessment from a probabilistic point of view, combining production strategy selection and optimization, numeric reservoir simulation and risk-return analysis. The flexibility is an alternative to information for risk mitigation, with the advantage of not holding the project back to collect new data. Furthermore, flexibilities can also be used to exploit the upside of the uncertainty if, during the production phase, such scenarios occur.

A New Approach to History Matching Using Reservoir Characterization and Reservoir Simulation Integrated Studies

Reservoir characterization is very important to the success of a history matching and production forecasting. Thus, numerical simulation becomes a powerful tool for the reservoir engineer in quantifying the impact of uncertainties in field development and management planning, calibrating a model with history data and forecasting field production, resulting in a reliable numerical model. History matching has been integrated into several areas, such as geology (geological characterization and petrophysical attributes), geophysics (4D seismic data), statistical approaches (Bayesian theory and Markov field), and computer science (evolutionary algorithms). Although most integrated history-matching studies use a unique objective function (OF), this is not enough. A history matching by simultaneous calibrations of different OF is necessary because all wells must have their OF near the acceptance range as well as maintain the consistency of generated geological models during reservoir characterization. The main goal of this work is to integrate history matching and reservoir characterization; applying a simultaneous calibration of different OF in a history matching procedure and keeping the geological consistency in an adjustment approach to reliably forecast production. We also integrate virtual wells and geostatistical methods into the reservoir characterization to ensure realistic geomodels without creating the geological discontinuities to match the reservoir numerical model. The proposed integrated calibration methodology consists of using a geostatistical method for modelling the spatial reservoir property distribution based on the well log data, running a numerical simulator and adjusting conditional realizations (models) based on geological modeling (variogram model, vertical proportion curve and regularized well log data) and reservoir uncertainties, using a simultaneous adjustment of different OF to evaluate the history matching process and virtual wells to perturb geological continuities such as channels and barriers. In conclusion, we present an effective methodology to preserve the consistency of geological models during history matching process. In addition, we simultaneously combine different OF to calibrate and validate the models with well production data. Reliable numerical and geological models are used in the forecasting production under uncertainties to validate the integrated procedure. Introduction Reservoir modeling is an important step for reservoir prediction as it is directly involved in the reservoir management during the different stages of the field production life. Besides use in the construction of the numerical model, it helps create a consistent geological model, integrating with production data to ensure a reliable future forecast of the field. Thus, reservoir characterization is an important tool for reservoir engineering to improve geological and numerical modelling integration (Gosselin et al., 2003; Mezghani et al., 2004), estimating reservoir models consistent with multiple data types for the calibration and prediction period. Updating the reservoir model to match the current production is an important step, mainly in the initial stage of the field management because of the high level of uncertainties. It is also important to appropriately include uncertainties during the reservoir characterization phase. Then, a probabilistic approach is used to introduce and analyze the uncertainties in the reservoir characterization and history matching integrated studies. This process aims to integrate different data types and generate multiple reservoir scenarios (Behrens and Tran, 1998; Kazemi and Stephen, 2012; Skorstad et al., 2006; Suzuki and Caers, 2006). The acquired data are spatially dependent on static data such as well logs, core analysis; as well as dynamic data, such as production rate (Q) and bottomhole pressure (BHP). This study integrates the dynamic data of production wells in the history matching process. We define a way to quantify the quality of calibration represented by an objective function (OF), which is minimized through an automatic, assisted or

Application of Assisted Optimization to Aid Oil Exploitation Strategy Selection for Offshore Fields

Decision-making processes for selecting an oil exploitation strategy can be complex due to the high number of variables to be optimized. Many times, it can be unfeasible to search an optimal solution by evaluating a high quantity of variables simultaneously. In this case, assisted methods that involve engineering analyses and mathematical optimization algorithms are an alternative to obtain a good solution. This paper shows the application of an assisted method to optimize a large number of variables of an oil exploitation strategy. The proposed methodology is to order and combine different optimization procedures with practical engineering analysis. The optimization variables include number and position of wells, platform capacities, wells opening schedule and wells shut-in time. The methodology is applied to a reservoir model based on a Brazilian offshore oil field to discuss the results obtained. Results indicate an efficient procedure for evaluating deterministic scenarios, suggesting optimization procedures for each decision variable and enabling the achievement of good quality solutions with a reasonable number of simulation runs. This is useful in many practical cases, mainly those, which require runs with long simulation time. Introduction Under reservoir engineering point of view, an oil field development and production strategy is the specification of important characteristics of the production system (infrastructure) that significantly and interactively impacts the profit expectation of the whole field. These characteristics involve the design of many details of infrastructure and control that are required for other projects of other areas. In general, the specifications are determined by the use of different optimization processes to assess each element of the strategy, which may demand a multidisciplinary team. The efficiency of the strategy selection is straightly connected with a workflow that rules all evaluations and their interactions. Therefore, an important task for the reservoir engineering area is the organization of all studies required to define relevant aspects of the strategy. The complete infrastructure project designed by reservoir engineers requires the determination of components that can include size, location and arrangement of surface facilities, number, position and completion of wells, injection and production capacities, well opening schedules, use of intelligent wells, among others. In general, these alternatives are selected using different decision-making-processes, treated as variables of different optimization runs and limited by physical and technical constraints. In addition, there is a certain interactive level among the different aspects. As a consequence, the design of the infrastructure of an oil field can be complex and challenging due to the large number of alternatives. The selection of the oil development strategy is sometimes made taking into account just the experience and judgment of the professionals involved. However, this can lead to inadequate solutions due to the possible few evaluations of the problem in the wide solutions space. To deal with this problem, oil companies use several sophisticated methods to evaluate the many aspects of the strategy. Despite they can achieve good solutions for determined specification, the combination of them to solve the more global problem of strategy may result in an unfeasible process. The use of adequate methodologies to combine different methods of optimization for each different aspect of the strategy aids to find better solutions in an efficient way. Therefore, the knowledge of the problem, the choice of appropriate optimization methods and the way to link the methods input and output are part of the reservoir engineer tasks to build more efficient workflows. This work uses an assisted process that combines both reservoir engineering evaluations and mathematic methods to select the oil development and production strategy. In addition, the test example was conceived to be applied in the predevelopment phase. The pre-development phase is here defined as the period before the well development drilling. The

A New Automated History Matching Algorithm improved by Parallel Computing

This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s).

Decision-Making Process in Development of Offshore Petroleum Fields

Risk is inherent to all phases of a petroleum field lifetime due to geological, economic and technological uncertainties, which are very significant on oil recovery in development phase, the focus of this work. The acquisition of additional information of uncertain attributes and flexibility during the development are key points to risk mitigation. The Value of Information (VoI) is used to quantify the benefits of new information, giving more accuracy to the project. The Value of Flexibility (VoF) measures the benefits of adding flexibility to the project considering different possible scenarios. A new and reliable methodology has been proposed to quantify VoI and VoF based on the decision tree technique in order to combine the uncertain attributes. All reservoir models generated by the tree are submitted to parallel simulation and Geological Representative Models (GRM) are selected to represent geological uncertainties. The methodology includes the criteria used for selection of GRM, optimization of production strategies of each GRM considering the gathering of additional information and statistical treatment of the results. The methodology has been applied in a decision-making process of a giant offshore petroleum field. The field has been developed by blocks due to its physical limitations and intrinsic characteristics and the high investment necessary to develop a giant field. The contributions of this work are (1) to show the importance of VoI and VoF concepts in decision-making process in petroleum field development and the complexity of this type of decision, (2) to apply the proposed methodology in a giant offshore field modeled by parts, minimizing risks associated to the development of this type of field and (3) to evaluate the importance of the reservoir uncertainties in risk mitigation. An additional important contribution is to present the details of the use of reservoir simulation in the process, trying to obtain the best relationship between computation effort and reliability of the decision making process. Introduction All phases of a petroleum field are influenced by uncertainties. The uncertainties are, usually, associated to reservoir geological characteristics or economic and technological parameters. The geological uncertainties influence the economic results of the project; however they can be mitigated by acquisition of additional information. The economic uncertainties depend on the political, financial and economic scenarios of the E&P industry. Although, economic parameters, such as the oil price, can highly influence the project evaluation, they can’t be mitigated and have to be updated when they suffer significant variations. The technological parameters have influence mainly on production, investment and operational costs. The focus of this work is restricted to the reservoir geological uncertainties and consequently to flow characteristics. Considering offshore petroleum fields, the cost of additional information is high due to high investment and low flexibility. In such cases, the decision analyses process needs to be probabilistic, mainly when the production strategy is defined. Probabilistic methodologies have to be simplified since the process is complex; there are many possible decisions and the computational cost of the reservoir simulation, the tool employed to evaluate alternatives, is high. Each possible scenario is associated to probabilities, which are quantified trough risk analysis. The risk analysis can be applied to the various phases of the development process of a petroleum field (Santos and Schiozer, 2003). As decisions are different for each reservoir life phase, the methodologies and tools vary according to the phase. In exploration phase, the risk methodologies are well defined (Newendorp and Schuyler, 2000). In the transition from appraisal to the development phase, although the level of uncertainty is smaller, the importance of risk associated to the recovery factor may increase significantly. In this phase, various critical decisions, mainly related to the definition of the production strategy, have to be taken and the process complexity arises from high irreversible investments, large number of uncertainties, strong dependence of the results associated with the production strategy definition, and necessity of accurate reservoir behavior prediction (Schiozer et al., 2004). In this work, the decision-making process considers the uncertainty and risk associated to the geological and flow characteristics of a giant offshore field that is developed by modules. Some reasons to develop a giant field by modules are: its intrinsic characteristics, a strategy to reduce technical risks and budget and physical limitations. It is common

Scatter Search Metaheuristic Applied to the History-Matching Problem

Reservoir simulation plays an important role in man agerial decisions during the life of a reservoir, so it is v tal that they present an accurate prospect of real reservoir perf ormance. The main goal of the History Matching (HM) process i to improve the quality of numerical models by constrai ning simulated to observed data. A typical HM process ev aluates a chosen objective function (OF) comprised by linear functions on the uncertain reservoir properties. The OF can b e modeled in such a way that the HM process can be solved as an optimization problem. This paper discusses how the Scatter Search (SS) technique can solve the HM problem. The main feature of SS is that it works on a set of solution s called the reference set (RefSet). The idea is to improve the ov rall quality of the RefSet. New solutions are generated by a nonconvex combination of explored solutions. The goal f this paper is solve the HM with SS and to evaluate its performance. The proposed methodology was tested with two base synthetic reservoir models. The first is a homogeno us reservoir with 8 different horizontal permeability regions, while the second is a highly heterogeneous reservoi r m del where low quality background sand is crossed by hig h permeability canals. The results show that SS was q uite efficient, considering the quality of the generated solutions and the number of required numerical simulations. Most of the current HM methodologies do not perform well when the solution space is large and complex. The application of the SS methodology to the HM problem is a novel approach. Unlike most metaheuristics, SS can be effective even when the simulation time of each ten tative solution to the problem is long. Introduction Reservoir simulation has established itself as a ce ntral tool in studying different scenarios by which a petroleum r ese voir might be developed. Since so many decisions are tak en based on numerical simulation results, it is very importa nt that the simulation gives a good approximation of the perfor mance of the real reservoir. History Matching (HM) is the pr ocess of making changes on the properties of the reservoir m del so its simulation results better mirror real production/in jection data, in the expectation that the resulting model will al so yield good performance forecasts. The fact is that HM is, by nature, an ill posed inv erse problem which cannot be solved analytically, has a high degree of non-linearity, is usually comprised of a large number of variables, and, in most cases, accepts multiple so utions. These and other reasons make the HM problem one of the most time consuming phases on a reservoir simulatio n study, accounting for an average of one-third of the total study time [1]. Manual HM brings a lot of practical challenges with . There is always a large amount of data involved, ma ny simulation runs are needed, several comparisons bet ween simulated and observed data are required and manual modifications on the reservoir model are performed. All in all, it’s an error prone process. It is not likely that an efficient, fully automatic HM process is developed anytime soon because there are a lot of steps on the process which are subjective. The main subjective step is parameterization, i.e., which reservoir par ameters should be manipulated to tilt the simulated perform ance in the “right” direction. This part of the HM process rema ins in the realm of the reservoir engineer. Other tasks, howev er, are more repetitive, tedious and thus, subject to autom a ion. Once a set of reservoir parameters are chosen along with their variation intervals, the generation of reservoir mo dels containing different combinations of parameter valu es and the evaluation of how close a match results are tasks w hich can and should be automated to decrease the time spent in HM. This paper outlines how the HM problem can be model ed as a Combinatorial Optimization (CO) problem so it can be solved by meta-heuristic methods like Scatter Searc h (SS). After the SS implementation is described, two reser voir simulation models are adjusted using the proposed methodology. Optimization Background This section introduces a few terms and concepts co mm n in the optimization field which are important to bette r understand this work. SPE 102975 Scatter Search Metaheuristic Applied to the History-Matching Problem Sergio. H. G. Sousa, SPE, Célio Maschio, SPE, and Denis. J. Schiozer, SPE, UNICAMP

Quantifying the Impact of Grid Size, Upscaling, and Streamline Simulation in the Risk Analysis Applied to Petroleum Field Development

Geological uncertainties usually have a strong impact on the decisions applied to petroleum field development. Methodologies to quantify the impact of uncertainties are still not well established due to the amount of variables that have to be considered. The complete analysis usually depends on geological, economic and technological uncertainties that have different degrees of impact in the recovery process and may affect the decision process in different levels depending on the problem, reservoir characteristics, recovery mechanism and stage of field development. The objective of this paper is to evaluate the importance of the numerical simulation process in a risk analysis applied to a petroleum field development. The use of fast estimation of recovery factor can be used in the exploration risk analysis but, during appraisal and development phases, more reliable techniques must be used to predict the reservoir performance in order to quantify accurately the impact of uncertainties and evaluate risk. One of the drawbacks of the use of numerical simulations can be the required computational effort. Therefore, this paper evaluates the impact of using different degrees of accuracy in the procedure, calculating risk with different options of grid size, number of attributes, levels of uncertainties and also comparing the results with streamline simulation which is used also for very fine models. The proposed procedure is tested in the SPE 10th Comparative Solution and a comparison is presented for several different options. Finally some conclusions are taken based on accuracy, required computational effort, model scale, comparing also results from simulation through finite difference model and streamline. Introduction In the past, prediction of reservoir performance and field economic evaluation were normally carried out with one or a few possible models mainly due to restrictions in the computer resources. Nowadays, probabilistic forecasts are possible but due to the high number of uncertain variables and complexity of the models, simplifications are still necessary. Petroleum field development is always strongly related to uncertainties and risk. The complete economic analysis of the problem involves many uncertain parameters with different degrees of impact on the decisions that have to taken in the process. The most important uncertainties are related to geological models, economic parameters and technology. To evaluate the risk involved in the process, it is necessary to quantify the impact of these uncertainties, which is normally measured with objective functions. The analysis can be simplified if the uncertain parameters are combined in three groups: volumes in place (VHIP), recovery factor (RF) and economic model. Uncertainty related to volumes in place is a direct function of the geological characterization and it is normally reduced significantly during appraisal phase although it also may be responsible by high risk variations due to new and sometimes unexpected information. Considering the economic model, oil price is the major font of uncertainty. Investments and costs variations related to new technologies have also to be considered due to the long period of typical projects, especially in offshore fields. Regulatory issues also may affect in some cases. All parameters that affect reservoir behavior may be considered in the uncertainties related to recovery factors. In such cases, rock and fluid attributes, reservoir mechanism and production strategy are the most common parameters to be considered. These uncertainties are more important during appraisal and development phases when many important decisions related to production facilities are taken. One of the main goals of the risk evaluation is to identify the impact of each uncertainty in order to simplify the problem without significant accuracy reduction. The problem becomes more difficult because the impact of these uncertainties varies during the development of the fields. Most of the published works (Newendorp, 1975; Garb, 1988) related to risk measurement have the focus on the exploration phase where uncertainties due to reservoir performance prediction have small impact and probabilistic SPE 79677 Quantifying the Impact of Grid Size, Upscaling, and Streamline Simulation in the Risk Analysis Applied to Petroleum Field Development Eliana L. Ligero, SPE, UNICAMP; Célio Maschio, SPE, UNICAMP; Denis J. Schiozer, SPE, UNICAMP