Saudi Aramco a global leader in the oil and gas industry, faced the intricate challenge of optimizing reservoir modeling to better understand and exploit their assets. With traditional methods providing a foundation, there was a pressing need to embrace more sophisticated, data-driven approaches. Additionally the traditional methods were becoming less sufficient due to increasing complexities in extraction and exploration. Saudi Aramco partnered with Sigmoidal to harness the power of Artificial Intelligence, seeking to enhance computation speed, accuracy, and ultimately, to interpret, visualize and predict oil and gas reservoirs.
What was the business objective?
Partnering with Sigmoidal, Saudi Aramco embarked on an innovative journey to integrate AI-driven methodologies, aiming to transcend conventional limitations and significantly enhance their operational efficiency.
- Optimization of Reservoir Analysis: To leverage AI for a significant enhancement in the speed and accuracy of reservoir simulation and modeling.
- Integration of Multidisciplinary Data: To amalgamate varied data sets from geology, drilling, reservoir engineering, and production engineering, creating a comprehensive and interconnected model.
- Minimization of Exploration Risks: To use data-driven insights to reduce uncertainties and risks associated with oil and gas exploration and extraction.
- Forecasting and Optimization: To employ machine learning techniques to accurately predict reservoir production and identify optimal drilling locations.
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How did we accomplish it?
Phase 1: Data Aggregation and Preprocessing
- We began by gathering extensive historical and real-time data from Aramco’s diverse reservoirs, including production data, injection data, well properties, and known reservoir characteristics.
- The data underwent rigorous preprocessing to ensure it was clean, structured, and ready for analysis, setting a solid foundation for accurate machine learning models.
Phase 2: Model Development and Training
- With the data prepared, we initiated the development of machine learning models, leveraging techniques designed specifically for reservoir modeling.
- These models were meticulously trained using past production and injection data to accurately forecast reservoir performance and identify optimal drilling locations.
Phase 3: Simulation Enhancement with AI
- We enhanced traditional reservoir simulation methods by integrating AI-driven proxy models. These models were able to quickly process and simulate the physics of fluid flow in porous media, vastly improving computation speed and accuracy.
- Rather than relying solely on physical governing equations, the AI models built relationships between geological information, fluid dynamics, and field constraints, leading to a more holistic understanding of the reservoir.
Phase 4: Predictive Analysis and Optimization
- The AI system was fine-tuned to provide predictive analytics, offering valuable foresight into future trends in reservoir behavior and production outcomes.
- Optimization algorithms were applied to analyze the forecast data, suggesting the most efficient new drilling locations, thus maximizing potential oil recovery.
Phase 5: Validation and Iterative Refinement
- The machine learning models and AI simulations underwent a stringent validation process to verify their predictive accuracy and operational reliability.
- An iterative refinement approach was employed, allowing the models to learn and adapt from each simulation, continuously improving their predictive power.
Phase 6: Integration and Deployment
- The final AI-powered reservoir modeling solution was seamlessly integrated into Aramco’s existing workflows, ensuring that the transition to the new system was smooth and that it complemented existing processes.
- Comprehensive training was provided to Aramco’s team to ensure they could fully leverage the new AI tools for enhanced decision-making.
Through these meticulous phases, Sigmoidal delivered a robust AI-powered solution for reservoir modeling that enabled Aramco to significantly boost the efficiency and accuracy of their oil recovery operations.
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The Results
The AI-driven initiative spearheaded by Sigmoidal catalyzed a transformative upgrade to Aramco's reservoir management capabilities. Implementation of the tool has shown a solid improvement in reservoir modeling and simulation performance.
- The data-driven reservoir modeling approach integrated by Sigmoidal bridges measures geo-data and geological models and explores various uncertainties to minimize exploration risks.
- Additionally, a significant leap in computational efficiency was observed, with the AI models and simulations accelerating the pace and precision of reservoir analysis. This leap was quantifiable in operational tempo and a tangible 20% reduced time in the decision-making process.
Beyond the numbers, the refined modeling tools didn't just simulate; they illuminated, identifying the most promising drilling locations with a previously unattainable precision - guiding Saudi Aramco to more confident and informed operational strategies.
