BR 
EN Closed-loop field development and management (CLFDM) is defined as a periodic update of an uncertain field model using the latest measurements (data assimilation), followed by production optimization aiming mainly at maximizing the field economic value. This paper provides a review of the concepts and methodologies in the CLFDM. We first discuss different types of uncertainty encountered in field development and management. Then, concepts, components, and elements of CLFDM are presented. We then discuss and compare different automated methodologies for data assimilation, followed by explaining a hierarchy of different decision variables for production optimization including design variables (G1), life-cycle control rules (G2L), short-term controls (G2S), and revitalization variables (G3). We continue with explanations for the use of closed-loop in both the development and management phases of a field project. We also discuss and compare different methodologies for production optimization. Afterwards, objective functions for production optimization are presented, followed by the description of concepts and different approaches for selecting representative models to speed up solutions. This paper also highlights the necessity of integrated modeling of reservoir and production systems in CLFDM, and also the need for a standardized stepwise approach to apply the CLFDM by discussing one method from the literature. Finally, we summarize all the previous CLFDM studies on the basis of aspects covered in this paper, and suggest open areas for future research to enhance the use of CLFDM.
Autor: EPIC Energy
Disciplina de Pós-Graduação em Captura, Uso e Armazenamento de Carbono
O EPIC, convida todos para a aula inaugural da disciplina que oferecerá no primeiro semestre de 2021. O tema desta primeira aula será “Energy scenario, diversification and transition to a low carbon society”, que será ministrada por Rannfrid Skjervold, da Equinor, e Asgeir Tomasgard, da NTNU.
Esta aula inaugural acontecerá dia 18/03/2021, às 9hs (BRT). Mais informações podem ser obtidas no banner abaixo.
Experimental investigation on the performance of Electrical Submersible Pump (ESP) operating with unstable water/oil emulsions
Electrical Submersible Pump (ESP) is one of the most commonly used artificial lift methods in petroleum production, due to its capacity to operate in several conditions with two or three-phase flows. When the ESP operates with emulsion flow, its performance is degraded, and operational instabilities occur. Therefore, this paper aims to carefully investigate phase inversion and to present, by the first time, the ffective viscosity of unstable mineral oil/water emulsions, both within the ESP. The first part of this work analyzes the phase inversion phenomenon for two oil types in three viscosities, five ESP rotational speeds, and three mixture flow rates. Logistic functions were fitted using the dimensionless head as a water cut function to determine the phase inversion within the ESP. The continuous phase inversion model, developed for emulsion pipe flow, did not present a satisfactory agreement to flow conditions tested. An indirect method to determine the emulsion effective viscosity within the ESP was proposed, which was obtained from the water/oil emulsion performance curves. The viscous performance data were used to determine the geometric coefficients of a dimensionless head empirical model for the tested ESP. Thus, the calculated values were compared with the effective viscosity obtained with oil and water emulsions, as well as the ESP performance, operating with emulsion and oil, which provides similar values for low rotational speeds. The different behavior of the effective viscosity between the pipeline flow and within the ESP was observed for water-in-oil emulsions and may be related to the high centrifugal field in the ESP.
Information, robustness, and flexibility to manage uncertainties in petroleum field development
Development decisions for petroleum fields are costly and difficult because they involve long-term projects with complex systems and high level of uncertainties. Decision makers may reduce reservoir uncertainties with the acquisition of additional information or protecting the system against uncertainty with flexibility and robustness. Although often preferred, acquiring additional information may be insufficient to mitigate all uncertainties or may be suboptimal when compared to robustness and flexibility. This study proposes a decision framework to estimate the best approach to manage uncertainty at early stages of field development considering the combination of information, robustness, and flexibility. With a predefined set of uncertain scenarios and specialized production strategies optimized for representative models, we can automate the process. We identify the best way to manage uncertainties by assessing system sensitivity and what controls production strategy selection. We improve the traditional estimation of the expected value of information (EVoI), robustness (EVoR), and flexibility (EVoF), by accounting for all changes in the risk curve and weighing the decision maker’s attitude. Our proposal was validated in a controlled environment where the reference response is known (a reference model, called UNISIM-I-R, that is not part of the uncertain ensemble, called UNISIM-I-D). The best approach to manage uncertainty in UNISIM-I-D combined features of robust and specialized strategies, resulting in a flexible strategy with robustness. This solution increased oil recovery and the economic return in UNISIM-I-R when compared to taking no action to manage uncertainty, but was suboptimal. These results agree with the rationale behind investing in actions to manage uncertainty (such as flexible and robust strategies), which is to mitigate risks or exploit upsides of uncertainty (i.e., they are intended to prevent or explore extreme outcomes). Investing in such actions is a risky decision in itself because they may not be needed, depending on the features of the real reservoir. Our results in UNISIM-I-D demonstrate the EVoI, EVoR, and EVoF are not additive. Our results in UNISIM-I-R reveal that inherent flexibility in operations (such as in drilling of horizontal wells) may mitigate some risks, reducing the EVoI, EVoR, and EVoF. Our proposal is a good starting point for more quantitative and objective decision-making at the early stages of field development and ultimately prevents discarding attractive solutions based on biases or insufficient metrics. Some improvements may be necessary for EVoI, EVoR, and EVoF estimates depending on the stage of the lifetime of the field, among other factors.
Multi-scale meshing for 3D discrete fracture networks
The geometric description of a Discrete Fracture Network (DFN) in the context of multi-scale methods, involves the ability of inserting multiple fractures in a predefined coarse mesh, while building volumetrical elements of smaller scale around the surface of these fractures in order to create sub-meshes inside the coarse elements. This paper presents an approach for automatic finite element meshing of fractured reservoirs suited to Multi-scale Hybrid-Mixed methods (MHM). The code is written in C++ and largely relies on two open source finite element libraries: NeoPZ and Gmsh. The main steps to the method involve: locating intersections and re-fining elements at those points, building a data structure that associates each element of a fracture surface to the coarse volume that encloses it, and then generate a sub-mesh of fine elements around the fractures to fill these coarse elements, without altering originally defined nodes in the coarse mesh. In order to improve the quality of geometrical elements to be generated, strategies of moving intersection points and features simplifications are also presented. Results show that the proposed technique can efficiently construct adequate 3D meshes. While relying on neighbourhood information and consistent element topologies available from NeoPZ’s geometric meshes, enables optimization of multiple algorithms of geometric search that would, otherwise, require a considerable amount of floating-point operations.
Error estimations for multiscale hybrid-mixed finite element methods for Darcy’s problems on polyhedral meshes
A posteriori error estimation for multiscale hybrid-mixed formulations for Darcy’s problems is discussed. The method adopts two-scale finite element spaces: refined discretizations are adopted inside polygonal subregions, but flux approximations are constrained over the mesh interfaces by a given coarse normal trace space. For stability, pressure and flux approximations are divergence compatible. The error estimation is based on potential reconstruction, which is a popular technique for this kind of analysis in the context of mixed methods. Numerical experiments are presented in order to illustrate the efficiency of the proposals.
Oil & Gas Production Forecasting based on LSTMs
The oil & gas (O&G) industry is one of the most important activities that support the economy in the world. Reservoir production management is a challenge with several facets and, surely, one of great interest refers to the difficult forecasting of O&G production in a reservoir. Most models in the prior art are physics-based, and try to predict the reservoir behavior based on fluid dynamics simulation. The problem with these models is the high computational cost footprint as models can take several hours/days/weeks of computation to obtain an accurate simulation (Zhang, 2018). On the other hand, Machine Learning (ML) algorithms have been implemented in several applications, and they can lead to breakthroughs in several areas. Recently, they have been used in time series forecasting, where we try to predict a variable from historical records. It is very promising for O&G management as ML methods could be exploited for production forecasting. One advantage is the computational footprint of ML algorithms that are smaller as we can deploy most tasks in GPUs so highly parallelizing them. ML-based reservoir models have been classified into two major classes: first, the Surrogate Reservoir Models (SRM), in which the simulation is based on synthetic numerical models and try to reproduce accurate replicas of traditional reservoirs; and second, Top-down Models, when an ML model is built from actual field data such as historical production, seismic attributes, well production, etc. (Mohaghegh, 2011). The main challenge on all these models is to find the optimal input set that allows us to perform an accurate production forecasting. Sometimes it is difficult to find relationships, or correlations, between input and production variables. Common methods of production forecasting are based on regression algorithms such as Support Vector Machines (Noshi et al. 2019), Random Forests (Maucec and Garni, 2019), or Radial Basis Functions models (Memon et al. 2014). All these ML models share a similar methodology: first, they seek correlations between input values (i.e., injection wells, bottom-hole pressure, well logs, etc.); second, they define a training set from the input and the production variables; third, they train an ML model; and, finally, they validate the model with a testing set. If results from the regression model are close to the ones in the testing set, we can say the ML model has learned properly the mapping function between the input variables and the target variable, and the trained model can be used in forecasting. In this work, we present a forecasting model for O&G production based on a data-driven approach and supported by ML algorithms. Our model takes advantage of a long-short term memory (LSTM) methodology capable of finding correlations in the data not only in the recent data points of a time series but also more subtle ones present in longer time intervals. The results show promising perspectives for forecasting a short-term context for oil, gas, water, and liquid production on a synthetic, but realistic, benchmark.
Two-Stage Scenario Reduction Process for An Efficient Robust Optimization
Well-positions in an oil field have a key role in production performance and financial interests. Defining the location of wells is challenging due to rock-fluid interaction, adjacent wells effects, petrophysical variables, and so on (Janiga et al., 2019). Hence, to overcome the problems and gain maximum economic profits, the optimization of well placement is required (Rahim and Li, 2015). In well placement optimization problems, reservoir flow simulation is normally used to integrate geological (static) and dynamic data, and evaluate the objective functions which are normally related to the economic performance of the field (i.e., the NPV). However, reservoir uncertainties strongly affect the accuracy and reliability of reservoir simulation and optimization outcomes. In the following subsections, we explained the required concepts in the robust well-placement optimization under uncertainties. Reservoir uncertainties arise when there are some constraints in the understanding of the reservoir properties (Hutahaean et al., 2019). Hence, instead of optimizing a deterministic model, robust well placement optimization is performed to optimize the objective functions over a reservoir model set (Badru and Kabir, 2003; van Essen et al., 2009; Yang et al., 2011and Chang et al., 2015). During robust optimization, the decision-maker looks for an optimal risk-weighted solution that has good performance for all reservoir models under reservoir uncertainty (Yang et al., 2011). Reservoir uncertainties can be divided into two groups including (1) geological (static) uncertainties related to geological and petrophysical properties, (2) dynamic uncertainties associated with flow properties, production system accessibility, and oil price fluctuation (Santos et al., 2018a). Static uncertainties in well placement optimization are commonly considered by generating numerous geological realizations (static reservoir models) while dynamic uncertainties are taken into account by building multiple simulation models (dynamic reservoir models). Monte Carlo (MC) and Latin Hypercube (LH) sampling methods are standard tools (Santos et al., 2018b) for generating the geological realization. To combine the static uncertainties with dynamic uncertainties and build the simulation model set, Discretized Latin Hypercube Sampling with Geostatistical realizations (DLHG) has been widely used during the last decade (Almeida et al., 2014; Avansi et al., 2015; Bertolini et al.,2015 and Schiozer et al., 2015).
Selecting representative models for ensemble-based production optimization in carbonate reservoirs with intelligent wells and WAG injection
Production optimization under uncertainty is complex and computationally demanding, a particularly challenging process for carbonate reservoirs subject to WAG injection, represented in large ensembles with high simulation runtimes. Search spaces of optimization are often large, where reservoir models are complex and the number of decision variables is high. The computational costs of ensemble-based production optimization can be decreased by reducing the size of the ensemble with representative models (RM). The validity of this method requires that the RM maintain representativeness throughout the optimization process, where the production strategy changes at each evaluation. Many techniques of RM selection use production forecasts of the ensemble for an initial production strategy, which raises questions about the robustness of the RM. This work investigates approaches to ensure the consistency of RM in ensemble-based long-term optimization. We use a metaheuristic optimization algorithm that finds sets of RM that represent the ensemble in the probability distribution of uncertain attributes and the variability of production, injection, and economic indicators (Meira et al., 2020). Our case study is a benchmark light-oil fractured carbonate with features of Brazilian pre-salt reservoirs and many reservoir and operational uncertainties. We obtained production, injection and economic indicators using different approaches to provide valuable insight for RM selection. We inferred about RM fitness for production optimization based on their adequacy for uncertainty quantification for varying production strategies. Despite the effects of changing decision variables on RM representativity, our results suggest the possible use of RM for ensemble-based production optimizations with limitations related to the estimation of the probabilistic objective function due to mismatches in the probabilities of occurrence. Using production indicators obtained from a base production strategy decreased RM representativeness when compared to RM selection based on a more robust evaluation of reservoir performance using a wide-covering well pattern and no restrictions from production facilities. Finally, our results suggest valid RM selection using production forecasts for intermediate dates of the simulation period, an important contribution for ensembles with very high simulation runtimes. We also provide a broad theoretical background on the uncertain reservoir system and on approaches to obtain reduced ensembles and their
applications.