A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

Abstract

Accurately characterizing fractures is complex. Several studies have proposed reducing uncertainty by incorporating fracture characterization into simulations, using a probabilistic approach, to maintain the geological consistency, of a range of models instead of a single matched model. We propose a new methodology, based on one of the steps of a general history-matching workflow, to reduce uncertainty of reservoir attributes in naturally fractured reservoirs. This methodology maintains geological consistency and can treat many reservoir attributes. To guarantee geological consistency, the geostatistical attributes (e.g., fracture aperture, length, and orientation) are used as parameters in the history matching. This allows us to control Discrete Fracture Network attributes, and systematically modify fractures. The iterative sensitivity analysis allows the inclusion of many (30 or more) uncertain attributes that might occur in a practical case. At each uncertainty reduction step, we use a sensitivity analysis to identify the most influential attributes to treat in each step. Working from the general history-matching workflow of Avansi et al. (2016), we adapted steps for use with our methodology, integrating the history matching with geostatistical modeling of fractures and other properties in a big loop approach. We applied our methodology to a synthetic case study of a naturally fractured reservoir, based on a real semi-synthetic carbonate field, offshore Brazil, to demonstrate the applicability in practical and complex cases. From the initial 18 uncertain attributes, we worked with only 5 and reduced the overall variability of the Objective Functions. Although the focus is on naturally fractured reservoirs, the proposed methodology can be applied to any type of reservoir.

Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

“…Then, all simulation models are simulated and the models are prepared for the data assimilation step. To carry out the data assimilation in step 5, several methods are available, according to the complexity of data (Costa et al 2018;Davolio and Schiozer 2018;Maschio and Schiozer 2016;Oliveira et al 2018;Mahjour et al 2020c). During this step, a subset of generated simulation models is selected based on the past reservoir performance (Gaspar et al 2016).…”

Model-based decision analysis applied to petroleum field development and management

“…Data assimilation: history match and reduce the number of scenarios with dynamic and seismic data. Several techniques are available (Avansi and Schiozer, 2015a;Bertolini et al, 2015;Costa et al, 2018;Davolio and Schiozer, 2018;Maschio and Schiozer, 2008, 2016Oliveira et al, 2018) depending on the complexity of the case and the available data. From the accepted models, a Base Case is selected for the following steps (Base1).…”

Evaluation of an uncertainty reduction methodology based on Iterative Sensitivity Analysis (ISA) applied to naturally fractured reservoirs

“…Regarding history matching problem, Tolstukhin et al (2012) applied a sensitivity analysis to a portion of the Ekofisk field (North Sea, south of Norway) and identified the eight most important attributes for history matching, six being fracture related: fracture distribution, orientation, width, width-to-length ratio, permeability, and density. Costa et al (2018) applied an Iterative Sensitivity Analysis (ISA) approach on an uncertainty reduction of global attributes of a complex naturally fractured reservoir.…”

“…This approach is the ISA. An example of application is given by Costa et al (2018), where they applied the ISA approach on an uncertainty reduction of global attributes of a complex naturally fractured reservoir. The high uncertainty of strongly influential attributes might disguise the influence of others (less influential).…”