How do you measure and evaluate the impact and value of your stratigraphic simulation projects?

1 Define your objectives

Before you start any stratigraphic simulation project, you need to define your objectives and expectations clearly. What are the main questions or problems you want to address with your simulation? What are the data and constraints available to support your simulation? How will you validate and calibrate your simulation results? How will you communicate and present your findings to your stakeholders? These questions will help you design your simulation workflow, select the appropriate methods and parameters, and evaluate the outcomes.

2 Choose your methods

Different types of stratigraphic simulation methods each have their own advantages and limitations. Forward models are numerical simulations which model sediment transport and deposition in response to external factors such as tectonics, climate, and sea level. Inverse models are statistical models which infer the external forcing factors from observed stratigraphic data. Hybrid models combine both forward and inverse approaches with data assimilation techniques to update the simulation results based on the data. Depending on your objectives, you may choose one or more of these methods, or combine them with other geological or geophysical models.

3 Assess your results

Once you have completed your stratigraphic simulation, assessing the quality and reliability of your results is essential. This involves comparing the simulation outputs with available data, testing the sensitivity and uncertainty of model parameters, and checking the consistency and coherence of model assumptions and interpretations. To assess your results, you can use criteria such as data fit, sensitivity analysis, uncertainty analysis, and model validation. Data fit measures the degree of agreement between simulation outputs and observed data. Sensitivity analysis evaluates how results change with respect to changes in model parameters. Uncertainty analysis estimates the confidence and variability of simulation results. Model validation verifies whether results are consistent with underlying physical principles. Quantitative measures such as correlation coefficients or error statistics, and qualitative measures such as visual inspection or pattern recognition can be used for data fit. Perturbation analysis, factorial design, or Monte Carlo simulation are methods for sensitivity analysis. Error propagation, Bayesian inference, or ensemble simulation are methods for uncertainty analysis. Cross-validation, blind test, or model comparison are methods for model validation.

4 Communicate your value

Finally, it is important to communicate the value and impact of stratigraphic simulation projects to stakeholders such as clients, managers, colleagues, or reviewers. This involves presenting and explaining objectives, methods, results, and implications in a clear, concise, and convincing manner. To do this effectively, you can use visual aids such as graphs, maps, cross-sections, animations or 3D models to illustrate your simulation results and insights. Additionally, storytelling techniques such as setting the context and creating a plot can help structure your presentation in a logical way. Moreover, using simple language such as plain words and active voice can help convey your message clearly and effectively.