Novi Labs | Reservoir Engineers Survey 2025

We’re launching the second edition of our Reservoir Engineer Survey to track how AI adoption in oil and gas is evolving. Your insights are essential in shaping our understanding of industry trends, challenges, and opportunities. AI has driven significant shifts in our industry over the past year, and documenting this transformation wouldn't be possible without experts like you.

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* 1. In which states are you currently operating?

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* 2. Which areas does your exploration and production company primarily focus on?

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* 3. What is the current daily production level of your E&P company in barrels per day (BBL/d)?

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* 4. How many years of experience do you have as a reservoir engineer?

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* 5. Are you familiar with Novi Labs and its AI tools for reservoir engineering?

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* 6. Are you encountering challenges in upstream public data quality and accessibility?

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* 7. Can you please name a few data providers you are using?

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* 8. How would you rate the reliability of their data on a scale of 1 (not reliable) to 5 (very reliable)?

1 5

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* 9. Who in your organization is primarily responsible for ensuring data quality?

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* 10. How would you rate the quality of the data you use for AI-driven workflows? (On a scale of 1 to 5, where 1 = Very poor and 5 = Excellent)

1 5

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* 11. What actions does your organization take to improve data quality for AI adoption? (Select all that apply)

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* 12. How do you allocate your time in your reservoir engineering work?

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* 13. Have you felt pressure to incorporate AI into your current workflows based on your peers' experiences?

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* 14. How do you perceive the future of reservoir engineering being influenced by AI?

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* 15. Do you believe AI has influenced the way you approach well forecasting and development, as well as the skill set required for your work

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* 16. In your opinion, can data-driven approaches be trusted as the first source of truth, or are they better suited as a second opinion in decision-making?

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* 17. How confident are you in the accuracy and reliability of AI-driven tools compared to traditional methods? (On a scale of 1 to 5, where 1 = Not confident and 5 = Very confident)

1 5

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* 18. Are you concerned about missing out on the competitive advantages that AI-driven workflows may offer?

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* 19. Are you currently using oil and gas analytics or software solutions in your reservoir engineering work?

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* 20. Have you used ML-driven well forecasting approaches in your reservoir engineering work?

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* 21. How confident are you in your ability to create accurate production forecasts using traditional methods like type curves?

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* 22. Has your company dedicated resources to build an in-house analytics team?

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* 23. Have you had experience building an in-house ML-driven forecasting tool, and if so, was it a success?

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* 24. Have you observed improvements in accuracy and efficiency when using AI for well forecasting and development plans?

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* 25. What areas of reservoir engineering have you used AI tools for? (Select all that apply)

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* 26. What challenges have you faced when integrating AI tools into your workflows? (Select all that apply)

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* 27. Do you believe that leveraging non-public proprietary data (sourced from top L48 operators and mineral owners) to build forecasting models has the potential to significantly impact the reservoir engineering field?

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* 28. Do you believe that traditional type curves in reservoir engineering are prone to bias and slow in providing results?

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* 29. Have you or your organization conducted a head-to-head comparison between data-driven approaches (e.g., machine learning, AI) and traditional type curves for reservoir analysis?

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* 30. What is the primary obstacle holding most companies back from using off-the-shelf oil & gas forecasting solutions?

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* 31. To what extent do you possess expertise in Data Science?

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* 32. What skills or training would help you use AI more effectively in your role? (Select all that apply)

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* 33. How do you envision AI adoption impacting your personal career and professional development in reservoir engineering?

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* 34. Do you believe that AI-driven solutions will become standard in reservoir engineering within the next decade?

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* 35. What role do you see AI playing in addressing challenges in complex reservoir scenarios?

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* 36. Are you open to collaborating with colleagues who have experience in data-driven approaches?

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* 37. If Novi Labs could develop one new feature or capability for AI tools, what would it be? (Select all that apply)

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* 38. What would most convince your organization to adopt AI tools? (Select all that apply)

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* 39. Would you find value in a Novi Labs-hosted forum or networking group for AI in reservoir engineering?