Diagnosing a Sudden Decline with EnerGPT

John

John

Jun 29, 2023

Diagnosing a Sudden Decline with EnerGPT

Introduction

In the rapidly evolving world of the energy industry, making timely and informed decisions can be the difference between success and failure. To help industry professionals navigate this landscape, we have developed EnerGPT - an application that combines the power of ChatGPT, OpenAI's state-of-the-art language model, with the wealth of knowledge from public resources.

This article will walk you through a real-world scenario, showcasing how EnerGPT can provide valuable insights for an operations center grappling with an unexpected situation.

Scenario

To start, we'll provide EnerGPT with a scenario from an operations center, from the screenshot above.

We're facing an unexpected drop in oil production from our multi-stage fractured horizontal wells in the Bakken Shale. Initial diagnostics show no obvious equipment failures, and the decline in production was not gradual, but rather, quite abrupt. From a reservoir and production engineering perspective, what could be the potential causes and what steps can we take to diagnose and rectify the situation?

EnerGPT replies with two major sections, possible causes and diagnostic next steps.

Potential Causes

EnerGPT came up with three likely scenarios, given the sudden dropoff in production. It also explains why these possible causes could be the case.

Based on the provided context, the potential causes of the unexpected drop in oil production from multi-stage fractured horizontal wells in the Bakken Shale could be:
Transient pressure behavior: Rapid decline in production rate is common in reservoirs producing by pressure depletion due to transient pressure behavior. It is possible that the wells have not yet passed through the transient pressure period and settled into semisteady-state conditions, leading to the abrupt decline in production.
Water breakthrough: The drop in oil production could be a result of water breakthrough in the field. Water handling capacity may be tested, and new issues related to water production such as sand production, corrosion, or H2S may arise, affecting availability and leading to a decline in oil production.
Flow problems: Flow problems, such as crossflow, underground blowouts, or perforation plugging by sand, can also cause a sudden decline in production. These flow problems need to be identified and addressed to rectify the situation.

Diagnostic Next Steps

While these steps are somewhat generic, they show the power of EnerGPT - these steps could've been generated directly in the field quickly from a cell phone, giving our crews more time to act.

To diagnose and rectify the situation, the following steps can be taken:
Collect and analyze comprehensive data: Gather data for each well over time to identify any underlying issues that may have caused the abrupt decline in production. This data can include pressure data, production rates, water and gas production rates, and any other relevant parameters.
Evaluate reservoir and fracture models: Conduct sensitivity runs with reservoir models and fracture propagation models to evaluate uncertainties in the data. This can help identify potential causes and understand the behavior of the fractured reservoir.
Wellbore processes evaluation: Evaluate within-wellbore processes and near-wellbore processes to identify any potential issues such as sand influx, perforation plugging, or gas breakout. This evaluation will help determine if any of these processes are contributing to the decline in production.
Consider remote reservoir processes: Assess the possibility of remote reservoir processes such as lateral water coning or the existence of stable sand zones in the interwell regions. Understanding these remote processes can provide insights into the decline in production and potential solutions.
Perform workovers or interventions: If any mechanical issues or flow problems are identified, consider performing workovers or interventions to rectify the situation. This may involve clearing blockages, optimizing perforations, or repairing any equipment failures.

Conclusion

Even these somewhat generic responses can bring a wealth of information to the ops center or the field, in a few seconds. Followup questions with more detail about the equipment or formation could be sent - adding data to our messages to EnerGPT is a great way to ensure it gives us helpful data back.

When something goes wrong, ask EnerGPT.