Building a Custom AI Petro Agent: A Case Study in Rapid Prototyping for Petrophysical Analysis

In the complex field of oil and gas exploration and production, petrophysical analysis is an essential process that converts raw well log data into valuable reservoir insights. However, these complex workflows can be a significant bottleneck, demanding deep domain expertise and meticulous, time-consuming effort. What if geoscientists and petrophysicists could be augmented with an intelligent assistant, ready to provide expert guidance and streamline these evaluations?

This is the very challenge we recently addressed. This post details our experience in rapidly developing a Proof of Concept (POC) for an "AI Petro Agent"—a bespoke AI chatbot designed to serve as a specialized assistant for petrophysical analysis.

The Core Challenge: Enhancing Consistency and Efficiency in Petrophysical Workflows

The fundamental challenge for many geology and geophysics (G&G) teams is not the absence of data, but the significant time and specialized knowledge required to interpret it consistently and efficiently. A standard shaly sand petrophysical evaluation, for example, is a multi-stage process that includes: selecting appropriate logging tools, applying the correct petrophysical models (like the Archie equation or more advanced shaly sand equations), accurately calculating shale volume (Vshale), and determining key reservoir properties like porosity and water saturation.

Our objective was to design an AI assistant capable of acting as an expert resource to empower users by:

• Providing on-demand, context-aware recommendations for logging programs.

• Guiding them through the selection of appropriate workflows and parameters.

• Performing essential calculations based on user inputs.

• Delivering insights through an intuitive and interactive interface.

The Solution: A Specialized AI Assistant for Petrophysical Analysis

The AI Petro Agent POC was conceptualized and built as a focused, intelligent tool. The core of this assistant was trained on a curated knowledge base grounded in the principles of shaly sand analysis. This specialization is essential; unlike general-purpose AI tools such as ChatGPT or Co-Pilot, the Petro Agent delivers contextually accurate and dependable guidance within its specific domain.

Key capabilities demonstrated in the POC included:

• Focused Expertise: Specializing in the evaluation of shaly sand formations using data from open-hole well logs.

• Intelligent Workflow Guidance: Advising on the selection of equations and parameters based on specific reservoir characteristics.

• Interactive Calculations: Performing petrophysical calculations like VCL, PHIT, and SW in real-time based on data points provided by the user.

• Essential Data Visualization (Conceptual): Generating critical log plots and crossplots to help users visualize well log data and interpret results more effectively.

The Power of Rapid Prototyping: Our Experience with Jotform AI Agent Builder

To validate this concept quickly and demonstrate tangible value, we chose to implement the initial POC using the Jotform AI Agent Builder. During the initial phases of an AI project, rapid prototyping is crucial. No-code and low-code platforms, such as the Jotform AI builder, play an essential role in this stage, facilitating the transition from a strategic concept to a functional, interactive agent within days.

This agile approach allowed us to:

• Rapidly structure and test a domain-specific knowledge base.

• Deploy an intuitive chatbot interface without the overhead of extensive front-end development.

• Deliver a working prototype to the client for immediate, hands-on testing and critical feedback.

Using the Jotform AI Agent builder for the POC phase was instrumental. It enabled us to showcase the real-world value of a custom AI chatbot for oil and gas workflows, proving the concept's viability without the significant upfront investment typically associated with a full-scale production build.

Key Learnings and the Strategic Path to a Commercial Solution

The proof of concept (POC) was exceptionally successful in demonstrating the value of a specialized AI assistant. The client quickly acknowledged its potential to streamline processes, enhance analytical consistency, and facilitate the onboarding of junior team members more effectively. Nonetheless, this phase also highlighted the inherent limitations of using a no-code builder to develop a robust, scalable commercial product.

The strategic next step is to leverage these invaluable learnings to build the commercial version of the AI Petro Agent on a more powerful and flexible enterprise-grade platform, such as Google Cloud's Vertex AI. This evolution will unlock:

• Deeper integration with industry-standard software and data formats, including direct LAS file processing.

• Advanced context management and sophisticated learning from user interactions.

• Greater scalability to support a larger user base and more complex petrophysical models.

• Enterprise-level security, data governance, and reliability.

Conclusion

The journey from a complex business problem to an effective, AI-driven solution does not have to be a long, high-risk endeavor. By embracing an agile methodology and leveraging rapid prototyping tools, we can quickly validate concepts, demonstrate tangible value, and build a powerful business case for investing in more advanced, scalable AI solutions. The AI Petro Agent project is a clear testament to the power of this strategic, phased approach.

Are you looking to implement a custom AI assistant to automate your petrophysical evaluation, geomechanical analysis, or other specialized oil and gas workflows? Contact us to explore how we can help you build an intelligent solution tailored to your unique operational needs.