A Fast Gridding Method for Capturing Geological Complexity and Uncertainty

Summary In this paper, we introduce an efficient method to generate reservoir simulation grids and modify the fault juxtaposition on the generated grids. Both processes are based on a mapping method to displace vertices of a grid to desired locations without changing the grid topology. In the gridding process, a grid that can capture stratigraphical complexity is first generated in an unfaulted space. The vertices of the grid are then displaced back to the original faulted space to become a reservoir simulation grid. The resulting inversely mapped grid has a mapping structure that allows fast and easy fault juxtaposition modification. This feature avoids the process of updating the structural framework, which may be time-consuming. There is also no need to regenerate most of the reservoir properties in the new grid. To facilitate juxtaposition updates within an assisted history matching workflow, several parameterized fault throw adjustment methods are introduced. Grid examples are given for reservoirs with Y-faults, overturned beds, and complex channel-lobesystems.

A Fast Gridding Method for Capturing Geological Complexity and Uncertainty

In this paper, we introduce an efficient method to generate reservoir simulation grids and modify the fault juxtaposition on the generated grids. Both processes are based on a mapping method to displace vertices of a grid to desired locations without changing the grid topology. In the gridding process, a grid that can capture stratigraphical complexity is first generated in an unfaulted space. The vertices of the grid are then displaced back to the original faulted space to become a reservoir simulation grid. The resulting reversely mapped grid has a mapping structure that allows fast and easy fault juxtaposition modification. This feature avoids the process of updating the structural framework and regenerating the reservoir properties, which may be time-consuming. To facilitate juxtaposition updates within an assisted history matching workflow, several parameterized fault throw adjustment methods are introduced. Grid examples are given for reservoirs with Y-faults, overturned bed, and complex channel-lobe systems.

Successful Application of Honey-Bee Optimization Technique in Reservoir Engineering Assisted History Matching: Case Study

This paper provides the field application of the bee colony optimization algorithm in assisting the history match of a real reservoir simulation model. Bee colony optimization algorithm is an optimization technique inspired by the natural optimization behavior shown by honeybees during searching for food. The way that honeybees search for food sources in the vicinity of their nest inspired computer science researchers to utilize and apply same principles to create optimization models and techniques. In this work the bee colony optimization mechanism is used as the optimization algorithm in the assisted the history matching workflow applied to a reservoir simulation model of WD-X field producing since 2004. The resultant history matched model is compared with with those obtained using one the most widely applied commercial AHM software tool. The results of this work indicate that using the bee colony algorithm as the optimization technique in the assisted history matching workflow provides noticeable enhancement in terms of match quality and time required to achieve a reasonable match.

Integrated Assisted History Matching and Forecast Optimisation Under Uncertainty for More Robust Mature Field Redevelopment Project

Redevelopment of a mature field enables reassessment of the current field understanding to maximise its economic return. However, the redevelopment process is associated with several challenges: 1) analysis of large data sets is a time-consuming process, 2) extrapolation of the existing data on new areas is associated with significant uncertainties, 3) screening multiple potential scenarios can be tedious. Traditional workflows have not combatted these challenges in an efficient manner. In this work, we suggest an integrated approach to combine static and dynamic uncertainties to streamline evaluating of multiple possible scenarios is adopted, while quantifying the associated uncertainties to improve reservoir history matching and forecasting. The creation of a fully integrated automated workflow which includes geological and fluid models is used to perform Assisted History Matching (AHM) that allows the screening of different parameter combinations whilst also calibrating to the historical data. An ensemble of history matched models is then selected using dimensionality reduction and clustering techniques. The selected ensemble is used for reservoir predictions and represents a spread of possible solutions accounting for uncertainty. Finally, well location optimisation under uncertainty is performed to find the optimal well location for multiple equiprobable scenarios simultaneously. The suggested workflow was applied to the Northern Area Claymore (NAC) field. NAC is a structurally complex, Lower Cretaceous stacked turbidite, composed of three reservoirs, which have produced ~170 MMbbls of oil since 1978 from an estimated STOIIP of ~500 MMstb. The integrated workflow helps to streamline the redevelopment project by allowing geoscientists and engineers to work together, account for multiple scenarios and quantify the associated uncertainties. Working with static and dynamic variables simultaneously helps to get a better insight into how different properties and property combinations can help to achieve a history match. Using powerful hardware, cloud-computing and fully parallel software allow to evaluate a range of possible solutions and work with an ensemble of equally probable matched models. As an ultimate outcome of the redevelopment project, several prediction profiles have been produced in a time-efficient manner, aiming to improve field recovery and accounting for the associated uncertainty. The current project shows the value of the integrated approach applied to a real case to overcome the shortcomings of the traditional approach. The collaboration of experts with different backgrounds in a common project permits the assessment of multiple hypotheses in an efficient manner and helps to get a deeper understanding of the reservoir. Finally, the project provides evidence that working with an ensemble of models allows to evaluate a range of possible solutions and account for potential risks, providing more robust predictions for future field redevelopment.

Applications of Honey Bee Optimization in Reservoir Engineering Assisted History Matching

Bee colony optimization technique is a stochastic population-based optimization algorithm inspired by the natural optimization behavior shown by honey bees during searching for food. Bee colony optimization algorithm has been successfully applied to various real-world optimization problems mostly in routing, transportation, and scheduling fields. This paper introduces the bee colony optimization method as the optimization technique in reservoir engineering assisted history matching procedure. The superiority of the proposed optimization algorithm is validated by comparing its performance with two other advanced nature-inspired optimization techniques (genetic and particle swarm optimization algorithms) in three synthetic assisted history matching problems. In addition, this paper presents the application of the bee colony optimization technique in assisting the history match of a full field reservoir simulation model of a mature gas-cap reservoir with 28 years of history. The resultant history matched model is compared with those obtained using a manual history matching procedure and using the most widely applied optimization algorithm used in assisted history matching commercial software tools. The results of this work indicate that employing the bee colony algorithm as the optimization technique in the assisted history matching workflow yields noticeable enhancement in terms of match quality and time required to achieve a reasonable match.