Nandi Formentin, Helena (Durham University and University of Campinas) | Vernon, Ian (Durham University) | Avansi, Guilherme Daniel (University of Campinas) | Caiado, Camila (Durham University) | Maschio, Célio (University of Campinas) | Goldstein, Michael (Durham University) | Schiozer, Denis José (University of Campinas)
Reservoir simulation models incorporate physical laws and reservoir characteristics. They represent our understanding of sub-surface structures based on the available information. Emulators are statistical representations of simulation models, offering fast evaluations of a sufficiently large number of reservoir scenarios, to enable a full uncertainty analysis. Bayesian History Matching (BHM) aims to find the range of reservoir scenarios that are consistent with the historical data, in order to provide comprehensive evaluation of reservoir performance and consistent, unbiased predictions incorporating realistic levels of uncertainty, required for full asset management. We describe a systematic approach for uncertainty quantification that combines reservoir simulation and emulation techniques within a coherent Bayesian framework for uncertainty quantification.
Our systematic procedure is an alternative and more rigorous tool for reservoir studies dealing with probabilistic uncertainty reduction. It comprises the design of sets of simulation scenarios to facilitate the construction of emulators, capable of accurately mimicking the simulator with known levels of uncertainty. Emulators can be used to accelerate the steps requiring large numbers of evaluations of the input space in order to be valid from a statistical perspective. Via implausibility measures, we compare emulated outputs with historical data incorporating major process uncertainties. Then, we iteratively identify regions of input parameter space unlikely to provide acceptable matches, performing more runs and reconstructing more accurate emulators at each wave, an approach that benefits from several efficiency improvements. We provide a workflow covering each stage of this procedure.
The procedure was applied to reduce uncertainty in a complex reservoir case study with 25 injection and production wells. The case study contains 26 uncertain attributes representing petrophysical, rock-fluid and fluid properties. We selected phases of evaluation considering specific events during the reservoir management, improving the efficiency of simulation resources use. We identified and addressed data patterns untracked in previous studies: simulator targets,
We advance the applicability of Bayesian History Matching for reservoir studies with four deliveries: (a) a general workflow for systematic BHM, (b) the use of phases to progressively evaluate the historical data; and (c) the integration of two-class emulators in the BHM formulation. Finally, we demonstrate the internal discrepancy as a source of error in the reservoir model.
Reservoir characterization is the key to success in history matching and production forecasting. Thus, numerical simulation becomes a powerful tool to achieve a reliable model by quantifying the effect of uncertainties in field development and management planning, calibrating a model with history data, and forecasting field production. History matching is 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. History matching by simultaneous calibrations of different OFs is necessary because all OFs must be within 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 OFs 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, avoiding the geological discontinuities, to match the reservoir numerical model. The proposed methodology comprises a geostatistical method to model the spatial reservoir-property distribution on the basis of the well-log data; numerical simulation; and adjusting conditional realizations (models) on the basis of geological modeling (variogram model, vertical-proportion curve, and regularized well-log data). In addition, reservoir uncertainties are included, simultaneously adjusting different OFs to evaluate the history-matching process and virtual wells to perturb geological continuities. This methodology effectively preserves the consistency of geological models during the history-matching process. We also simultaneously combine different OFs to calibrate and validate the models with well-production data. Reliable numerical and geological models are used in forecasting production under uncertainties to validate the integrated procedure.
The global energy production reflects an excessive percentage of non-renewable energy. Therefore, a great volume of investment on renewable energy became a serious matter in order to mitigate the damage caused by the consumption of energy from fossil sources. On the other hand, Brazil's oil companies are living a decisive moment in terms of investments in energy bases, because there is a great demand for capital resources to develop the latest discoveries of enormous
non-renewable supplies. In this context, this paper describes the impact of the projects announced by the oil and gas industries in the Brazilian energy matrix. The data comes from the plans presented by the major oil companies that operate in Brazil basins, mainly in pre-salt region, and from reports published by EPE (Energy Research Company - Government of Brazil).
This work involves projections from 2011 to 2020 and it was made assuming three global economic scenarios: recession, stability and economic growth. Projections are treated in a comprehensive knowledge by a simple econometric model and based on the investments announced by the oil industry of the country. Studies take into account the fossil and renewable energy projections under assumptions from the Ten Year Plan for Energy Expansion in 2020 produced by the National Council for Energy Policy (CNPE, in the Portuguese-language acronym).
The results indicate viable economic projects in the renewable energy for the Brazilian oil and gas companies (energy corporations), primarily biofuels, wind and solar power, in the expense of some oil production projects in their portfolios. Brazil is
experiencing a great time to develop the renewable energy sources that is made by other countries and if these changes accelerate, it will be the potential to improve its economy by creating millions of jobs.
As main conclusion, the decisions on investment in renewable energy with capital acquired from the pre-salt projects will determine the future of the Brazilian energy matrix. The main goal of this paper is to indicate a transition to a cleaner energy
matrix considering an alternative investment plan, and show some fundamental concepts to illustrate how the renewable energy can be important for the oil and gas companies' subsistence in the next generations.
Time-lapse seismic has been providing valuable information on identifying fluid movements, to locate bypassed oil and well placement optimization to reduce uncertainties in reservoir development and production management. Most of these are made through a qualitative approach, which limits seismic data integration in reservoir simulation studies due to different scales. In order to overcome this and to develop new data integration techniques, many studies involving these processes have the assumption that both frameworks are in the same scale. Nonetheless, the quantification of lost information in the scaling procedures needs to be evaluated and quantitative seismic data integration into the simulator should improve the mitigation of these problems improving the knowledge about the representativeness of the scaled data.
In this context, it is presented a technique regarding scale issues relating time-lapse seismic data and simulation models aiming to quantify the information lost due to scaling problems and how these troubles affect the history matching.
This paper describes a methodology involving seismic attributes and reservoir simulation. The lost information due to scaling procedures is evaluated through comparison between flux model properties and seismic attributes, at each respective framework.
This technique allows reliability improvement of reservoir parameters from seismic attributes regarding the loss of information due to scaling procedures and history matching.
It has been shown that it is possible to match the history production in order to improve the process considering the scaling issues between the seismic and simulation scale, increasing model quality and reliability on results through a quantitatively analysis involving scaling procedures between 4D seismic data and reservoir simulation integrated studies.
The main contributions of this technique are the integration procedures regarding scaling issues between seismic data, reservoir simulation models and history matching procedure by time-lapse seismic attributes.
Time-lapse seismic has been providing valuable information on identifying fluid movements, to locate bypassed oil and well placement optimization to reduce uncertainties in reservoir development and production management. Nevertheless, most of these are made through a qualitative approach, which limits seismic data integration in reservoir simulation studies due to different scales. In order to overcome this and develop new data integration techniques, many studies involving these processes have the assumption that both frameworks are in the same scale. Nonetheless, the quantification of the lost information in the scaling procedures needs to be evaluated and quantitative seismic data integration into the simulator should improve the mitigation of these problems improving the knowledge about the representativeness of the scaled data.
In this context, it is presented a technique regarding to scale issues relating time-lapse seismic data, geological and simulation models aiming to quantify the information lost due to the scaling problems.
This paper describes a methodology involving seismic attributes and reservoir simulation. Saturation and pressure trends derived from acoustic impedance behavior, which were obtained from seismic data. The lost information due to scaling procedures is evaluated through comparison between flux model properties and seismic attributes, at each respective framework, for estimating the best step to do it. This technique allowed the reliability improvement of the reservoir parameters from seismic attributes regarding to the lost information due to scaling procedures.
It has been shown that it is possible to improve the model and results reliability through a quantitative analysis involving scaling procedures between 4D seismic data and reservoir simulation integrated studies.
As main contributions of this technique are the integration procedures regarding to scaling issues between reservoir simulation models and time-lapse seismic information.
New techniques for reservoir development are essential for dealing with the complexities of geological models. In this sense,
numerical simulation is the tool used to define the quality of a production strategy. However, the process to define variables
such as well numbers, completion layer, open timeline and operational conditions demand several simulations due to the high
time consumption and computational effort. Sub-optimal results can be obtained from manual processes. Automatic
processes can mitigate this problem but the computational effort is increased as a result of the number of simulations
generated in the process.
In order to minimize this problem, this paper proposes an assisted procedure for its automatic part using proxy models to
accelerate the process. Furthermore, due to the reduced time to evaluate options, it allows a better evaluation of the solution
space using better optimization techniques. Proxies have been used in important applications such as risk analysis and history
matching but the use for definition of production strategy is not common.
The proposed methodology involves the following components: statistical methods, experimental planning to generate the
response surface methodology for the generation of proxies and consistency checking.
The results show that it is possible to apply proxy models for this type of problem and they can identify the best
production strategies, reducing the computational effort during the process. The suggested procedure is to use proxies in the
automatic part of the assisted procedure used in the optimization process.
The main contribution of this work was the demonstration that proxy models can be used for the definition of production
strategies, bringing an additional option to the decision analysis process linked with petroleum field development.
The efficient frontier of portfolios is an important tool for management because decision-makers can see how much return can be achieved using the total assets of the corporation and at the same time the level of risk. But, in order to create the efficient frontier, each project need to be analyzed previously - capital investment, production profile, capital cost etc. At the level of projects usually there are many real options available for management over the life of the project - drill additional wells, develop a satellite oil & gas field, oil production, make farm-out etc. When properly used, these real options have value and are an important tool for reducing the risk of NPV.
Then, a more realistic efficient frontier should consider the value of these real options, but in the practice of oil and gas business this does not always occur. In this paper, the study of the impact of some real options on the efficient frontier of portfolios intends to use an integrated methodology including real options, portfolio theory and Monte Carlo simulation. Three deep-water oil projects are combined in a portfolio, where the decisor has an option to sell the project if the payback period is not accomplished.
For the case of hypothetical situation, preliminary results indicated when there is availability of real options and management that can use them optimally, there are two main consequences: 1) the distribution of NPV is moved to the right and the efficient frontier is displaced towards more return and risk is reduced; 2) This way of thinking is important in order to a more effective policy for managing those variables that generate systematic risks (price, geology, etc). This model gives a better view of the true value of managerial flexibilities in the context not only of projects, but on the portfolio that is much more realistic.