Rapid Reservoir Modelling: Sketch-Based Geological Modelling with Fast Flow Diagnostics

Rapid Reservoir Modelling (RRM) is a software tool that combines geological operators and a flow diagnostics module with sketch-based interface and modelling technology. The geological operators account for all interactions of stratigraphic surfaces and ensure that the resulting 3D models are stratigraphically valid. The geological operators allow users to sketch in any order, from oldest to youngest, from large to small, or free of any prescribed order, depending on data-driven or concept-driven uncertainty in interpretation. Flow diagnostics assessment of the sketched models enforces the link between geological interpretation and flow behaviour without using time-consuming and computationally expensive workflows. Output of RRM models includes static measures of facies architecture, flow diagnostics and model elements that can be exported to industry-standard software. A deep-water case is presented to show how assessing the impact of different scenarios at a prototyping stage allows users to make informed decisions about subsequent modelling efforts and approaches. Furthermore, RRM provides a valuable method for training or to develop geological interpretation skills, in front of an outcrop or directly on subsurface data.

Management of Uncertainty in Reservoir Modelling for an Onshore Carbonate Reservoir in the Middle East: A Case Study

As part of a reservoir modelling study for an onshore oil field in the Middle East, our study implemented a workflow with the objective to evaluate the impact of uncertainty on the long-term development scenario. The presence of several geological uncertainties characterized the field: many faults with uncertainty in juxtaposition and conductivity, lateral distribution of permeability in high permeability layers, and uncertainty on the rock typing. A deterministic geological model was available. There were also many dynamic uncertainties. The workflow started with an identification of uncertain variables, both from the static and the dynamic point of view, through an integrated team approach supported by a previous reservoir synthesis (Major Field Review). Subsequently, a screening analysis allowed identifying the relative impact of uncertain variables. After selecting the uncertainties with the largest impact on recovery, use of an experimental design methodology with a space-filling design resulted in alternative history matches. Statistical analysis of forecasts yielded probability density functions and low and high estimates of ultimate recovery. Forty-five uncertain variables, including both static and dynamic uncertainties, characterized the production profiles. Screening allowed reducing these to 11 main uncertain variables. A Wootton, Sergent, Phan-Tan-Luu (WSP) space-filling design yielded 162 simulation runs. Only five out of these corresponded to acceptable history matches. This number being statistically insignificant, a reexamination of the uncertainty ranges followed by a narrowing, allowed obtaining 45 history matches (out of 198 runs). The obtained spread in the cumulative oil production was narrow, with a slightly skewed distribution around the base case (closer to P90 than to P10). The study resulted in an estimation of final uncertainty in reserves that is smaller than the typical uncertainty found in post-mortem analysis of oil field development projects. Other reservoir studies in the company and in literature, employing a similar workflow, yielded outcomes with a similar bias. To tackle this issue, as a way forward we suggest history matching of multiple geological scenarios, either with multiple deterministic cases (min, base, max) or with an ensemble history matching loop including structural model generation, in-filling, and dynamic parameter uncertainty.

An open-source package with interactive Jupyter Notebooks to enhance the accessibility of reservoir operations simulation and optimisation

In this paper we present the interactive Reservoir Operations Notebooks and Software (iRONS) toolbox for reservoir modelling and optimisation. The toolbox is meant to serve the research and professional community in hydrology and water resource management and contribute to bridge the gaps between them. iRONS is composed of a package of Python core functions and a set of interactive Jupyter Notebooks. Core functions implement typical reservoir modelling tasks and the interactive Jupyter Notebooks illustrate, with practical examples, the key functionalities of iRONS. We describe our development philosophy, the key features of iRONS, and report some results of evaluating the effectiveness of interactive Jupyter Notebooks for training and knowledge transfer. The paper may be of interest also beyond the water resources management field, as an example of how Jupyter Notebooks and interactive visualisation help improving the documentation and sharing of open-source code and the communication of underpinning methodologies.