Post 5: The Critical Role of Production Data Verification in Production Optimization and Analytics

Introduction

The oil and gas industry is increasingly using advanced analytics and artificial intelligence to optimize production processes and enhance decision-making in the digital era. The foundation of these sophisticated tools is the accuracy and reliability of production data. To ensure that the data used for analytics is clean, consistent, and trustworthy, production data verification is crucial. Proper data verification is necessary for even the most advanced AI and analytical models to prevent misleading results, which can result in costly mistakes and inefficiency. This post delves into the critical importance of production data verification and its impact on production optimization and analytics. It highlights how robust data practices can drive operational excellence and unlock new levels of performance.

The Importance of Production Data Verification

There is an old saying: garbage in, garbage out. Every engineer/scientist (now data scientists) spent lots of time in cleaning data before even trying to analyze something.

For oil and gas industry the source of high measured production data is transmitters, meters, analyzers and lab results (bottomhole PVT sampling, routine surface sampling). This data serves as an input to various engineering and analytical software to perform critical functions such as reservoir simulations, production forecasting, and optimization of field operations. However, the accuracy and reliability of these functions are heavily dependent on the quality of the input data. Therefore, production data verification becomes an essential process to ensure that the data used in these analyses is clean, consistent, and accurate.

According to studies, data scientists spend between 60% and 80% of their time cleaning the data, rather than spending on analysis itself. This statistic should not be very off for petroleum, reservoir and process engineers as well.

Main reason for this is usually enormous amount of data (data accumulated for years) is cleaned in one go. This might be adequate for reservoir studies or for future development planning, but production optimization requires very quick decisions. Due to time constraint, these decisions are mostly taken without taking into consideration all the insights from high frequency production data. Why? Because the process of cleaning and verifying such high-frequency data in real-time can be extremely challenging and time-consuming without the right tools and systems in place. Engineers and decision-makers often must rely on simplified data sets (e.g. only rates from MPFM, daily averages, etc.) or historical data (e.g. months old well tests), which may not reflect the current operational conditions accurately. This can lead to suboptimal decisions that do not fully exploit the potential of available production data.

Impact on Production Optimization

As mentioned in the previous section, the production optimization process is highly dynamic, requiring quick and timely decisions. Imagine an onshore production asset with 500 wells spread out over a large area and three trains of processing units. The wells are equipped with wellhead and flowline sensors, some even have bottomhole pressure and temperature gauges. The well testing facilities consist of different models of MPFMs and test separators. The processing trains have several stages of separation, oil conditioning units, gas processing units, and oil and gas export systems, all equipped with sensors (pressure, temperature, metering, analyzers, etc.). Some of the wells are produced using artificial lift systems, also equipped with sensors. There is also injection of gas and water to maintain reservoir pressure. Each sensor generates real-time data every 10 seconds, which is stored in production data archives. Now, imagine a group of engineers who need to analyze all this data and make decisions accordingly. By the time a decision needs to be made, the engineers are often still arguing about the correctness of the data.

This delay in decision-making can lead to missed opportunities for optimizing production, addressing operational issues promptly, and enhancing overall efficiency. The sheer volume and complexity of the data make it difficult to ensure that all the information is accurate and reliable in a timely manner. This is where robust near real time data verification and cleaning processes become valuable.

Moreover, usually what engineers need for their decision making are parameters which are not directly measured, but rather calculated. For example, flow rates at standard condition from MPFM are not directly measured, but calculated based on sensors data and fixed PVT tables. Meters installed in test separators usually assumes constant composition or density.

Real-time production data verification and computation

The methods and techniques used for data cleaning and preparation are well-established and involve several key steps:?

• Identify Anomalies: Detect missing values, outliers, and duplicates in the dataset.
• Correct Errors: Identify and rectify any inaccuracies or errors present in the data.
• Check for Inconsistencies: Ensure data consistency across various sources and within the dataset itself.
• Create New Variables: Derive new variables or features from existing data to enhance analysis.

These methods are supported by domain knowledge to ensure relevance and accuracy. However, the challenge lies in applying these data verification processes to massive datasets, which can be time-consuming and resource intensive. On top of that there are gaps in production data due to various reasons which requires additional time spent on filling the gaps.

To address these challenges, implementing real-time data verification and computation systems is essential. These systems leverage advanced technologies and automation to streamline the data cleaning process, ensuring that high-frequency production data is accurate and reliable as soon as it is generated. By doing so, companies can significantly reduce the time and effort required for data preparation, allowing engineers and data scientists to focus more on analysis and decision-making.

Real-time data verification involves continuously monitoring data streams, automatically identifying and correcting errors, and validating data against predefined rules and patterns. This proactive approach helps in maintaining data quality, reducing the risk of erroneous decisions based on faulty data.

By adopting real-time production data verification and computation, companies can achieve several benefits:

• Improved Decision-Making: Access to clean, accurate data in real-time allows for more informed and timely decisions, optimizing production processes and resource allocation.
• Increased Efficiency: Automation reduces the manual effort involved in data cleaning, speeding up the entire process and enabling quicker responses to operational changes.
• Enhanced Data Integrity: Continuous monitoring and validation ensure that data remains consistent and reliable, reducing discrepancies and errors.
• Operational Excellence: Leveraging real-time data allows for proactive management of production systems, identifying and addressing issues promptly to minimize downtime and maximize productivity.

In conclusion, real-time production data verification and computation are crucial for modern production optimization. By implementing these advanced systems, companies can ensure data integrity, improve operational efficiency, and make better-informed decisions that drive overall business success.