Automated Well-test Validation
Classifying well tests as valid or invalid based on a list on criteria to improve accuracy
About business
Petroleum Development Oman (PDO), is a leading exploration and production company in the Sultanate of Oman, delivers the majority of the country’s crude oil production and natural gas supply.
Challenge
PDO was facing a major challenge in the well testing process. The current process of manually validating well tests was time-consuming and prone to errors. The client needed a more efficient and accurate solution to automate the validation process of checking for well testing.
To ensure the quality of oil exported to international markets meets international standards, many tests must be conducted across the value chain. These tests leverage remote sensing devices to measure gross volume (M3); water-cut; pressure among others.
Unfortunately, from time-to-time sensors fail and/or require re-calibration. Therefore, for a short period of time their results may be incorrect (invalid). Therefore, a team the HQ were tasks with classifying whether a test should be considered reliable (valid) or unreliable (invalid). As you can imagine, this is a labour-intensive and error-prone approach.
Solution
PhazeRo developed an Automated Well-Test Validation solution that uses advanced data analytics and machine learning algorithms to automate the validation process of checking for well testing. The solution integrates with the client's existing software systems to extract relevant data and applies machine learning models to detect anomalies and inconsistencies in the data. The solution has significantly reduced the time and resources required for well test validation, resulting in more accurate and reliable data.
We work with our clients to identify the data sources that are most relevant to their specific business problem. This may include internal data sources such as transactional databases, as well as external data sources such as social media, web analytics, and public data sources.
Once we have collected the data, we perform a thorough cleaning and preprocessing step to ensure that the data is accurate, consistent, and ready for analysis. This may involve removing duplicate entries, filling in missing values, and transforming the data into a format that is suitable for analysis.
Before we begin any formal analysis, we perform exploratory data analysis (EDA) to gain insights into the data and identify any patterns or relationships that may exist. This may involve visualizations such as histograms, scatterplots, and heatmaps.
Based on the insights gained from EDA, we develop models that can be used to make predictions or classify data. We use a variety of modeling techniques, including regression, decision trees, and neural networks. We evaluate the performance of our models using metrics such as accuracy, precision, and recall.
Finally, we use a variety of visualization techniques to communicate the results of our analysis to our clients. This may include interactive dashboards, charts, and graphs. We work closely with our clients to ensure that the visualizations are clear, intuitive, and actionable.
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Outcomes
Reduce the time and resources by 50%
The Automated Well-Test Validation solution significantly streamlined the well test validation process, enabling the client to complete the same amount of work in half the time and with fewer resources. This reduction in time and resources allows the client to allocate their resources more efficiently, freeing up time and personnel to work on their business-critical tasks.
Improve the accuracy and reliability of well test data
The solution's ability to automate the validation process reduces the likelihood of human error, improving the accuracy and reliability of well test data. Additionally, the solution provides a more thorough validation process, identifying potential issues that may have been missed in a manual process, ensuring that the data is of the highest quality.
Increase operational efficiency and cost savings
The reduction in time and resources required to complete the validation process leads to increased operational efficiency and cost savings for the client. The client can save on operational costs, as fewer resources are required to complete the task. Additionally, potentially leading to increased revenue or cost saving.
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Business
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Enterprise
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Enterprise
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