How can you manage incomplete or inconsistent reservoir data?

1 Assess data quality

The first step is to assess the quality of your reservoir data, which includes checking its accuracy, reliability, completeness, and consistency. You can use various methods and criteria to evaluate data quality, such as error analysis, data validation, quality control, and data reconciliation. You should also identify the sources and types of uncertainty and error that affect your data, such as measurement errors, sampling errors, model errors, and human errors. By assessing data quality, you can determine the level of confidence and uncertainty in your data and prioritize the areas that need improvement.

2 Handle missing data

The second step is to handle missing data, which can result from technical failures, operational constraints, or insufficient data collection. Missing data can create gaps and biases in your reservoir analysis and reduce the validity and reliability of your results. You can handle missing data in different ways, depending on the amount and pattern of missingness, the type and importance of the data, and the objective and method of the analysis. Some common techniques are deleting, imputing, or interpolating missing data, or using methods that can handle incomplete data, such as Bayesian inference, machine learning, or geostatistics.

3 Resolve data conflicts

The third step is to resolve data conflicts, which can occur when different sources or types of data provide contradictory or inconsistent information about the reservoir. Data conflicts can result from measurement errors, model errors, or interpretation errors, and they can create confusion and uncertainty in your reservoir analysis and decision making. You can resolve data conflicts in different ways, depending on the nature and cause of the conflict, the quality and reliability of the data, and the impact and implication of the conflict. Some common techniques are reconciling, weighting, or integrating data, or using methods that can handle conflicting data, such as data assimilation, uncertainty analysis, or multi-criteria decision analysis.

4 Optimize data usage

The fourth step is to optimize data usage, which means using your reservoir data in the most effective and efficient way to achieve your goals and objectives. You can optimize data usage in different ways, depending on the availability and quality of the data, the complexity and uncertainty of the reservoir, and the scope and scale of the analysis. Some common techniques are selecting, filtering, or transforming data, or using methods that can enhance data usage, such as data visualization, data mining, or data analytics.

5 Update data regularly

The fifth step is to update data regularly, which means incorporating new or revised data into your reservoir analysis and management as they become available or necessary. Updating data regularly can help you capture the changes and dynamics of the reservoir, reduce the uncertainty and error in your predictions and forecasts, and improve your reservoir performance and optimization. You can update data regularly in different ways, depending on the frequency and volume of the data, the type and impact of the data, and the method and workflow of the analysis. Some common techniques are appending, replacing, or merging data, or using methods that can facilitate data updating, such as data management, data integration, or data assimilation.

6 Learn from data feedback

The sixth step is to learn from data feedback, which means using the results and outcomes of your reservoir analysis and management to evaluate and improve your data quality and usage. Learning from data feedback can help you identify and correct the errors and biases in your data, assess and reduce the uncertainty and risk in your analysis and decisions, and enhance your reservoir understanding and knowledge. You can learn from data feedback in different ways, depending on the source and type of the feedback, the quality and relevance of the feedback, and the purpose and method of the learning. Some common techniques are reviewing, comparing, or testing data, or using methods that can support data learning, such as data auditing, data verification, or data evaluation.

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?