Ana T.F.S. Gaspar

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.

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

Use of Well Indicators in the Production Strategy Optimization Process

The selection of an adequate strategy for oil production is a complex process due to the several parameters that must be taken into account: type, number and position of wells, production and injection capacities of platforms, which must be defined and optimized in order to achieve an exploitation strategy which provides the best possible economic return. Most works presented production strategy optimization methodologies based on production or Net Present Value (NPV). In general, well information is not used in the optimization processes. However, information on well behavior and performance is very important to improve the production strategy and to make the process more robust. The aim of this paper is to present a comparative analysis of the use of different indicators in order to assist the selection/optimization of a production strategy based on well information, using numerical simulation and economic analysis. Changes in the configurations of the production system can be made according to the behavior of the wells and evaluation of the indicators aiming to maximize the NPV of the field. The development of the equations of the well indicators is shown and the methodology is applied to two different geological models: an offshore sandstone reservoir, representing a gas solution drive model and a carbonate reservoir with typical characteristics of the pre-salt areas of Brazil. Introduction The optimization of the production strategy consists of alterations in several parameters on the configuration of the project. A production strategy presenting a low performance can be indicative of an unfavorable schedule, inadequate number, position and/or type of wells, high water production, low well productivity etc. There is a high number of possible combinations for solving the problem of production strategy optimization, consuming a high computation time. According to Wences et al. (2001), while performing an integrated reservoir study, statistics show that 93% of the time is consumed in defining reservoir description and performing calibration of the simulation model and only 7% of that total time is used for forecasting purposes. For this reason, the authors suggest a technique to guide the post calibration work of a reservoir simulation model to determine the best exploitation strategy. The technique is based on a graph in which the NPV is plotted against the number of infill wells showing a maximum value, followed by a step by step methodology, which includes: determination of the fluid saturation distribution at the end of the forecast; choice of an aggressive exploitation plan, positioning the largest number of infill drilling wells based on the remaining fluid distribution; economic evaluation of the forecast considering only the incremental production over the base case; plotting the net present value obtained versus the number of additional infill wells used in the case etc. Usually, the optimization process is made considering an objective-function of the whole field, not taking into account indicators of individual wells in the process. Works such as Nakajima (2003) and Nogueira (2009) show that the performance analysis of wells is very important to make the process more efficient. However, in general, this is not done by the complexity of the process, especially when there are uncertainties involved. Analysis of well indicators can provide important and useful information about the field during the optimization process. This can be carried out as an attempt to automatize and give agility to the process. Considering uncertain scenarios of heterogeneous fields, a same well may behave very differently in each scenario due to the differences among the regions in the simulation models (Botechia, 2012). This kind of analysis provides new alternatives of strategy production or more robustness during the decision-making process. It is emphasized that, although the indicators that will be described here are used to assist the optimization process, this paper does not focus on the general optimization process, but aims to make an analysis and comparison of these well economic indicators, and in which cases they can be used appropriately, using reservoir simulation. Details of the optimization process

UNISIM-II-D: Benchmark Case Proposal Based on a Carbonate Reservoir

Brazilian pre-salt reservoirs are mainly carbonate formations and they represent a great opportunity for research development. There is an increasing need of synthetic simulation models that reproduce these Pre-salt flow features for research development in reservoir simulation. This work presents a simulation benchmark model available as public domain data that represents Brazilian pre-salt trends and add a great opportunity to test new methodologies for reservoir development and management using numerical simulation. The work structure is divided in three steps: development of a reference model with known properties, development of a simulation model under uncertainties considering a specific date that represents the field development phase, and, elaboration of a benchmark proposal for studies related to the oil field development and production strategy selection. The reference model, treated as the real field, is a fine grid model in order to guarantee a high level of geologic details. The simulation model under uncertainties is a large scale model, a result of a development project considering an initial stage of field management. The benchmark model is based in a combination of Pre-salt characteristics and Ghawar field information given its diagenetic events and flow features close to Pre-salt. Based on the available information, several uncertainty attributes were considered in structural framework, facies, petrophysical properties, discrete fracture network. Economic and technical uncertainties were also considered. There is an increasing need of synthetic simulation models that reproduce these Pre-salt flow features for research development in reservoir simulation. This work presents a simulation benchmark model available as public domain data that represents Brazilian pre-salt trends and add a great opportunity to test new methodologies for reservoir development and management using numerical simulation. The main result of this project is achieved: the construction of a reference model and the construction of a simulation model under uncertainties assuming the well log information from three wells. This work provides a great contribution for further research development in reservoirs with geologic and dynamic pre-salt trends. Introduction Pre-salt carbonate reservoirs from Santos Basin, Brazil, represent a great opportunity and an important recent oil discovery. Challenges in pre-salt of Santos Basin can be classified into three main categories: (1) description and representation of reservoirs fluids and rock heterogeneities, (2) selection of the best production strategy according to the reservoir characteristics and (3) prediction of the future reservoir performance. Formation test in ultra deep SAG reservoirs have presented very high flow rates with no indication of barriers and high vertical heterogeneity in permeability (Beltrão et al, 2009). One of the biggest challenges is how to represent the most important heterogeneities in reservoir simulation models. The reservoirs are non-conventional, quite heterogeneous, with many uncertainties and with very few analogues in the world, especially considering the environment and expected production profiles. The reservoir characterization and modeling is restricted to a few wells and consequently core volume information, resulting in a high amount of uncertainty (Nakano et al, 2009). Many of these uncertainties are related to reservoir characterization: facies distribution, hydraulic connection and existence of high permeability layers (Super-k features). The term Super-k features was first introduced to Ghawar field and is related to very thin layers with high permeability and porosity. An early partial dolomitization provides preferential fracturing allowing later diagenetic fluids to penetrate the formation and dissolve away non-dolomitized material, creating a permeable and porous material, which is mainly dolomite. Some of these dolomites are related to high flow zones, named then Super-k. Although fractures were absent in cores, their presence is proposed as a mechanism for creating Super-k properties (Swart et al, 2005). The introduction of these thin layers in reservoir simulation leads to specific challenges: (1) management of early water breakthrough which may constrain the future placement of water injection or production wells; (2) evaluation of possible drilling of complex wells with intelligent completions; (3) upscaling and numerical simulation skills to incorporate these thin high flow features for reservoir simulation purposes; (4) correct modeling of behavior of Water-Alternating-Gas (WAG) method which can significantly enhance the recovery for highly heterogeneous reservoirs; (4) make uncertainty analysis given the complex genesis of these flow features and lack of information (pre-salt reservoirs). Therefore, there is an increasing need of synthetic simulation models that reproduce these Pre-salt flow features for research activities on reservoir simulation. An example of a simulation model built for benchmark studies include UNISIM-I (Avansi and Schiozer, 2015) which represents a siliciclastic reservoir model. Objectives The purpose of this work is to develop a benchmark case (UNISIM-II) that involves a simulation model with geological trends and rock/fluid data with characteristics of the Pre-salt known behavior for reservoir simulation purposes. Given the lack of geologic information, the reservoir model is a combination of Pre-salt reservoirs data and information from Ghawar Field, a carbonate reservoir with geologic trends close to Pre-salt features. The work structure is divided in three steps: (1) development of a refined grid model with known characteristics called UNISIM-II-R, providing an opportunity to test methodologies for reservoir development; (2) build of a simulation model under uncertainties, called UNISIM-II-D, for the initial stage of field development; (3) elaboration of a benchmark proposal, also called UNISIM-II-D, for studies related to the oil field development and production strategy selection. Model Data The geologic and rock/fluid data combines Pre-salt data, Ghawar field information, real carbonate reservoir (Field A) and synthetic data. The field information taken into account for UNISIM-II-R is: ● Carbonate Reservoir of microbial origin, partially dolomitized (Pre-salt and Ghawar); ● High vertical heterogeneity in permeability (Pre-salt and Ghawar); ● High flow rates with no indication of barriers (Pre-salt and Ghawar); ● High permeability thin zones named Super-k (Pre-salt and Ghawar); 2 SPE-177140-MS

Impact of the Use of Intelligent Wells on the Evaluation of Oilfield Development and Production Strategy

Intelligent wells are widely used around the world and they have the potential to significantly improve oil production or control water production of wells and fields. However, in many cases, the definition of the number and position of valves is still made considering only the well without evaluating if the decision to use them can change other important aspects of the production strategy. This article presents a study to evaluate some relevant aspects of the inclusion of intelligent wells in a more global study of production strategy selection. Such inclusion is an important step in the precise evaluation of the benefits of intelligent valves. The methodology comprises the economic optimization of a production strategy under different limits of platform flow capacity, the optimization of the number and position of valves (intelligent wells), including and excluding conventional well operation. This study was applied to the UNISIM-I-D benchmark case, starting with a previously optimized production strategy, regarding type, number and position of wells, well-opening sequence and platform flow capacity in 9 different geological scenarios. The optimization methodology uses a complex workflow to test different strategy alternatives using a genetic algorithm and a methodology to optimize the number and position of valves. We showed that the use of intelligent wells can significantly alter the water flow capacity and the operational design of wells. However, for this specific case, the use of intelligent wells was not able to modify oil production and water injection flow capacity. Intelligent well application was viable for 7 out of 9 geological scenarios with the number of valves varying from 1 to 14. The intelligent-well application improved the total NPV from 0% to 1.5%. The platform water flow capacity could be reduced by at least 30% if intelligent valves were implemented. These results are quite different when a less precise optimization methodology is applied, yielding an overestimation of an intelligent well. To conclude, the application of intelligent wells was viable for most of the scenarios. Although intelligent wells present low impact on NPV, they can modify the design of platform capacity significantly. This fact suggests that the optimization of intelligent wells must be combined with the optimization of the platform water flow capacity and the conventional well operation optimization. This work provides important information for reservoir engineers who use reservoir simulation to optimize production strategy. Currently, in many cases, intelligent wells are only evaluated after the selection of the platform design. We have proved that the combined optimization can yield a different production strategy design. In addition, we have also proved that the evaluation of intelligent wells viability without an adequate optimization of conventional well operation overestimated the number and the value of valves.

Impact of the New Brazilian Fiscal System on Development of Oil Production Strategy

Production strategy is an important component of the oil reservoir development phase. Among,the main parameters are the geometry, number and position of wells and platform liquid capacity, influencing the level of the project investment required, which all depend on geological characteristics, economic scenarios and fiscal regimes. In the oil industry, companies produce under a fiscal system imposed by the government, which has a strong impact on economic and operational indicators, influencing production strategy. Recently, the Brazilian government established a law changing its fiscal terms on pre-salt areas from Royalty and Tax (R&T) to Production Sharing Contract (PSC) to increase the government take. Previous works have shown that, in optimistic scenarios, an optimal recovery strategy presents low discrepancy in the production strategy configuration across both fiscal regimes. This study considers four economic scenarios for further evaluation. For this purpose, a simulation model was submitted to the production strategy selection process for both fiscal systems. In more pessimistic economic scenarios, the results indicate that the number of wells and the level of investment tend to be lower under PSC than under R&T system. Thus, the new system could lead to fewer industrial investments, which would reduce the government return compared to the former tax system. In the most pessimistic scenario considered, profitable production could be expected under R&T, while under PSC it would be unprofitable, generating lower revenues. It is still not clear whether a company, under PSC, will also be able to develop its strategy plan based on NPV or whether negotiation with the government regarding a minimum oil recovery factor will take place. Regardless, this study identifies the impact on production strategy selection for the new Brazilian PSC system compared to optimized strategy for R&T, and in both cases the objective-function is the company’s NPV. From the company’s point of view, depending on the economic scenario, the prevailing fiscal system will influence decisions at the level of the investment to be made. The results show the importance of considering the impact of the new fiscal system when selecting production strategies.