Inverse Stratigraphic Modeling Using Hybrid Algorithms

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Solution Overview

Problem

Accurate estimation and tuning of input parameters for forward stratigraphic models are time-consuming and inconsistent across geological engineers, limiting the effectiveness of hydrocarbon reservoir simulations.

Innovation Solution

A hybrid linear and nonlinear algorithm is used for inverse stratigraphic modeling, employing a genetic algorithm and particle swarm optimization to optimize hydrodynamic input parameters and grain sizes, reducing the manual effort and improving accuracy by calibrating models with multiscale prior observation data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual estimation and tuning of input parameters is performed by geological engineers, then the model can be calibrated, but the process is very time consuming and results are often inconsistent

Engineering Contradiction:

Improvecalibration accuracyVSAvoidtime for estimation and tuning

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs self-calibration by automatically adjusting input parameters based on observed data and misfit values, eliminating the need for manual estimation and tuning by geological engineers. The automated algorithm iteratively optimizes parameters to minimize differences between simulated and observed topography, achieving consistent and time-efficient calibration.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical process of geological engineer estimation with an automated computational algorithm. The system uses mathematical optimization to automatically tune input parameters, substituting human expertise with a systematic computational approach that is both faster and more consistent.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual estimation and tuning of input parameters is performed by geological engineers, then the model can be calibrated, but the results are often inconsistent between different geological engineers

Engineering Contradiction:

Improvecalibration consistencyVSAvoidoperator dependency

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system eliminates operator dependency by performing self-calibration through automated algorithms. The same input data and model always produce identical calibrated parameters, ensuring consistency regardless of which geological engineer operates the system. The automated optimization process follows a deterministic path to the optimal solution.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system incorporates feedback mechanisms where the calculated misfit value between simulated and observed topography continuously guides the parameter optimization. This closed-loop feedback ensures that all calibration efforts converge on the same optimal parameter set, eliminating the inconsistency that arises from different geological engineers' subjective judgments.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If a forward stratigraphic model is used to simulate hydrocarbon reservoirs, then geological predictions can be made, but accurate estimation of many input parameters is required which is technically challenging

Engineering Contradiction:

Improveinput parameter estimation accuracyVSAvoidmodel complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

Instead of using complex forward models to predict outcomes and then trying to invert the results, the patent uses a simplified inverse approach where the goal is to adjust input parameters to match observed data. The algorithm works backward from observed topography to determine the optimal input parameters, simplifying the overall process compared to traditional forward modeling approaches.

Inventive Principle:

Principle #13The other way round (Inversion)

Solution Approach 2:

The patent segments the complex calibration process into manageable components by optimizing different input parameters separately. The system divides the parameter space into distinct categories (hydrodynamic parameters, sediment parameters, etc.) and optimizes each group independently, making the overall complex estimation process more manageable and computationally efficient.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11506804B2Inverse stratigraphic modeling using a hybrid linear and nonlinear algorithm

Publication Date: 2022.11.22 SAUDI ARABIAN OIL CO

AI summary

In a first step, a defined scope value is selected for each of a plurality of hydrodynamic input parameters. A simulated topographical result is generated using the selected scope values and a forward model. A detailed seismic interpretation is generated to represent specific seismic features or observed topography. A calculated a misfit value representing a distance between the simulated topographical result and a detailed seismic interpretation is minimized. An estimated optimized sand ratio and optimized hydrodynamic input parameters are generated. In a second step, a genetic algorithm is used to determine a proportion of each grain size in the estimated optimized sand ratio. A misfit value is used that is calculated from thickness and porosity data extracted from well data and a simulation result generated by the forward model to generate optimized components of different grain sizes. Optimized hydrodynamic input parameters and optimized components of different grain sizes are generated.