What are the main sources of uncertainty in reservoir heterogeneity?

2 Scale and resolution

Another source of uncertainty in reservoir heterogeneity is the scale and resolution of the analysis and modeling. Reservoir heterogeneity can occur at different scales, from millimeters to kilometers, and each scale can have a different impact on the fluid flow and recovery. For example, small-scale heterogeneities, such as fractures, vugs, and laminations, can create preferential pathways or barriers for the fluid flow, while large-scale heterogeneities, such as faults, channels, and facies, can create compartments or connectivity within the reservoir. However, it is impossible to capture all the scales of heterogeneity in a single model, due to computational and data constraints. Therefore, some degree of simplification and aggregation is inevitable, which can introduce uncertainty in the reservoir heterogeneity representation and prediction.

3 Geostatistical methods

A third source of uncertainty in reservoir heterogeneity is the geostatistical methods used to interpolate and extrapolate the data and generate multiple realizations of the reservoir model. Geostatistical methods are based on statistical concepts, such as variograms, kriging, simulation, and uncertainty quantification, that aim to capture the spatial correlation and variability of the reservoir properties. However, geostatistical methods also involve assumptions, parameters, and choices that can affect the results and introduce uncertainty in the reservoir heterogeneity estimation and simulation. For example, the choice of the variogram model, the kriging algorithm, the simulation technique, and the number of realizations can influence the spatial distribution and range of the reservoir properties.

4 Fluid flow models

A fourth source of uncertainty in reservoir heterogeneity is the fluid flow models used to simulate the reservoir performance and optimize the drilling engineering decisions. Fluid flow models are based on physical and mathematical equations, such as mass conservation, Darcy's law, and multiphase flow, that aim to describe the fluid movement and interaction within the reservoir. However, fluid flow models also involve simplifications, approximations, and parameters that can affect the predictions and introduce uncertainty in the reservoir heterogeneity response and behavior. For example, the choice of the grid size, the numerical scheme, the relative permeability curves, and the fluid properties can influence the fluid flow rates, pressures, saturations, and recovery factors.

5 Sensitivity and uncertainty analysis

A fifth source of uncertainty in reservoir heterogeneity is the sensitivity and uncertainty analysis performed to assess the impact of the reservoir heterogeneity on the drilling engineering objectives and outcomes. Sensitivity and uncertainty analysis are based on methods, such as design of experiments, response surface modeling, Monte Carlo simulation, and probabilistic risk assessment, that aim to evaluate the effect of the reservoir heterogeneity uncertainty on the drilling engineering performance and risk. However, sensitivity and uncertainty analysis also involve challenges, such as data integration, model selection, parameter estimation, and result interpretation, that can affect the conclusions and introduce uncertainty in the reservoir heterogeneity evaluation and optimization.

6 Human factors

A sixth and final source of uncertainty in reservoir heterogeneity is the human factors involved in the reservoir heterogeneity characterization and modeling. Human factors are the psychological, social, and organizational aspects that influence the human behavior and performance in the reservoir heterogeneity analysis and decision-making. However, human factors also introduce uncertainty in the reservoir heterogeneity, as they can lead to biases, errors, conflicts, and miscommunication. For example, the human factors can affect the data acquisition, processing, and interpretation, the model building, validation, and updating, the scenario analysis, ranking, and selection, and the communication, collaboration, and learning.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?