What are the best data management practices for well testing and analysis?

2 Data Organization and Storage

The second step in data management is to organize and store the data in a systematic and secure way, so that it can be easily accessed, retrieved, and shared when needed. Data organization and storage involves creating a clear and logical structure for the data, such as using folders, subfolders, labels, and metadata, and choosing a suitable format and platform for the data, such as spreadsheets, databases, or cloud services. Data organization and storage can help you save time and effort in finding and using the data, and prevent data loss or damage due to human or technical errors.

3 Data Analysis and Visualization

The third step in data management is to analyze and visualize the data to extract meaningful insights and trends from the well test results. Data analysis and visualization involves using various tools and techniques, such as graphs, charts, tables, maps, and dashboards, to display and explore the data in a clear and concise way, and applying various methods and models, such as regression, interpolation, extrapolation, and simulation, to understand and predict the behavior and performance of the well. Data analysis and visualization can help you identify and quantify the key parameters and factors that affect the well test results, such as reservoir pressure, flow rate, skin factor, and productivity index.

4 Data Interpretation and Evaluation

The fourth step in data management is to interpret and evaluate the data to draw conclusions and recommendations from the well test results. Data interpretation and evaluation involves using various concepts and principles, such as material balance, inflow performance relationship, type curve analysis, and pressure transient analysis, to estimate and validate the reservoir and fluid properties, such as porosity, permeability, saturation, viscosity, and compressibility, and to assess and compare the production potential and economic viability of the well. Data interpretation and evaluation can help you optimize your well design and operation, and support your decision making and planning.

5 Data Documentation and Reporting

The fifth step in data management is to document and report the data to communicate and disseminate the well test results and findings to the relevant stakeholders, such as clients, partners, regulators, and peers. Data documentation and reporting involves creating a comprehensive and coherent report that summarizes and explains the data quality control, data organization and storage, data analysis and visualization, and data interpretation and evaluation processes and outcomes, and highlighting the main conclusions and recommendations, and the limitations and uncertainties of the data. Data documentation and reporting can help you demonstrate your professionalism and expertise, and provide valuable feedback and learning for future well testing and analysis projects.

6 Data Review and Update

The sixth and final step in data management is to review and update the data to ensure that it remains relevant and accurate over time, and to incorporate new or additional data that may become available or required. Data review and update involves revisiting and revising the data quality control, data organization and storage, data analysis and visualization, data interpretation and evaluation, and data documentation and reporting steps, and applying new or improved methods, models, or standards, if necessary. Data review and update can help you maintain and enhance the quality and reliability of your data, and adapt to the changing conditions and requirements of the well and the reservoir.

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?