Key Insights, Industry Challenges & Practical Solutions
Hosting the Onshore Wellsite Automation Conference 2024 brought together a diverse group of operators, technology providers, and industry leaders, all focused on the evolving role of automation in upstream operations. Discussions highlighted both strategic imperatives and technical advancements, reflecting a fast-moving landscape where AI-driven solutions, data standardization, and low-latency communications are reshaping wellsite management. The event was defined not only by high-calibre presentations but by deep, practical discussions during Q&A sessions—where real-world challenges, implementation hurdles, and breakthrough solutions were debated in detail. While automation is unlocking new efficiencies, companies must navigate regulatory shifts, workforce adaptation, and the integration of scalable AI-driven automation solutions.
Example Macro Takeaways (please watch the video or read the post-conference report for a more comprehensive analysis)
AI & Machine Learning in Wellsite Automation
o Operators are moving toward exception-based surveillance, where AI-powered diagnostics
detect inefficiencies and optimize production in real-time.
o Predictive analytics in ESP failure prevention, gas lift optimization, and liquid loading
mitigation are significantly reducing downtime.
Regulatory Uncertainty & ESG-Driven Compliance
o The shifting U.S. regulatory landscape is forcing operators to balance automation investments
with evolving environmental standards.
Data Standardization vs. User Customization
o While unified historian systems and standardized platforms improve efficiency, they often reduce
user-specific customization.
o Companies are struggling to balance enterprise-wide data visibility with field-specific
operational flexibility.
Communication Infrastructure & Edge Processing
o Hybrid communication systems—including LoRaWAN, LTE, and satellite connectivity—are key to ensuring secure and reliable data transmissionin remote areas.
o The role of edge computing is expanding, allowing real-time data processing without dependency on cloud-based infrastructure.
AI-driven Downtime Reduction is Event-Specific: For liquid loading mitigation, downtime dropped from 15 days per event to just 2–3 days, showcasing the highly targeted benefits of AI automation.
Scalability of AI for Mid-Sized Operators: Larger companies have 20+ engineers building in-house AI models, whereas smaller operators must rely on vendor solutions and cloud-based integration.
Cybersecurity in Wellsite Automation: The increased reliance on digital platforms raises concerns over data integrity and cybersecurity, making secure communication protocols and encrypted AI processing more critical than ever.
Onshore vs. Offshore Operational Cultures Differ: Offshore teams develop deep, well-specific knowledge, while onshore operations rely on broader, data-driven automation strategies.
This conference demonstrated that onshore wellsite automation is entering a new era, where AI, data integration, and scalable communications are shaping the next decade of oil and gas operations. While challenges remain—particularly in scaling AI for mid-sized operators, balancing regulatory demands, and adapting the workforce—the industry is well-positioned to leverage automation for increased efficiency, cost savings, and sustainability. The rich technical discussions and in-depth Q&A sessions reflected the industry's growing commitment to a digital transformation that is both scalable and pragmatic. Below you find a selection of highlights and takeaways from the individual presentations. The comprehensive conference report package, featuring full event video recordings, all available presentations, and a detailed summary report, is now available for purchase on this website.
Selected 2025 Presentation & Discussion Highlights
Denise Sherrod, Chief of Automation, Occidental Petroleum
- This talk provided a practical, hard-hitting look at the realities of scaling automation in upstream operations. While AI, IoT, and predictive analytics promise efficiency gains, success ultimately hinges on how well organizations integrate automation into real workflows.
- Oxy has tackled this by prioritizing operational adoption, refining exception-based management to eliminate alert fatigue, and developing mobile-first field solutions for a new generation of workers.
- Key wins include their gas lift optimization solution, which has scaled to 1,400+ wells after proving its value in a controlled pilot. However, challenges remain in ensuring data leads to action and securing organization-wide buy-in for automation at scale.
Amit Kumar, Data Science Technical Project Manager, Unconventional Business Line ExxonMobil
- Amit Kumar’s presentation highlighted ExxonMobil’s success in leveraging AI/ML to reduce downtime and optimize wellsite automation. By integrating high-frequency data, machine learning models, and physics-based simulations, the company has enhanced diagnostic capabilities, saving over $50M annually.
- Real-world applications, such as ESP failure prediction and virtual flow meters, have significantly improved response times. While AI-driven insights are revolutionizing field operations, challenges remain, including refining models for dynamic well conditions and integrating AI-driven virtual assistants.
- Overall, this session provided a compelling look at the power of AI/ML in optimizing production while setting the stage for the next wave of digital transformation.
Iris Wang, Team Lead for Geoscience & Data Analytics, Ecopetrol Permian
- Iris from EquiPetrol Permian delivered an insightful presentation on how AI and machine learning are driving efficiency in wellsite development, particularly in well spacing optimization, production forecasting, and reservoir management.
- By integrating a vast proprietary dataset—30,000 horizontal wells, 2,500 reservoir sales, and high-resolution geologic models—they have developed AI-driven workflows that maximize production while preventing overcapitalization and resource depletion. Their NPV Max model identifies the onset of diminishing returns, ensuring well density aligns with optimal economic performance. While AI enables smarter, faster decision-making, the industry still faces challenges in standardizing subsurface datasets and improving AI transparency. This presentation underscored AI’s transformative role in upstream oil and gas operations.
Ben Randell, Product Line Manager, Chevron
- Ben Randell from Chevron delivered a compelling perspective on leading successful wellsite automation initiatives. He emphasized that leadership must shift from a command-and-control model to one centered on learning, empathy, and empowerment.
- Industry 4.0 brings data-driven decision-making at unprecedented speeds, but its success depends on leaders who foster long-term thinking and upskill their teams for both technical and cultural changes. The biggest unresolved challenge is bridging the gap between automation adoption and workforce adaptation, ensuring employees see technology as an enabler rather than a threat.
- Overall, this presentation provided a forward-thinking roadmap for navigating digital transformation in oil and gas.
David Jones, Senior Exploration Systems Advisor, Murphy Oil
Sandro Esposito, VP Sales & Marketing, SignalFire Wireless Telemetry
- David Jones from Murphy Oil showcased how offshore automation strategies from the Gulf of Mexico can be successfully applied to onshore unconventional assets.
- By consolidating disparate historian and visualization systems, Murphy Oil streamlined data access, cutting inefficiencies and freeing up 20% of engineers’ time. Standardized naming conventions and a single intuitive interface improved decision-making and minimized IT burdens. While automation has advanced, challenges remain in fully migrating from legacy offshore platforms and further optimizing automation for onshore fields. Overall, the presentation was an insightful look at how cross-industry learnings can accelerate efficiency gains in unconventional oil and gas operations.
Sandro Esposito, VP Sales & Marketing, SignalFire Wireless Telemetry
Sandro Esposito, VP Sales & Marketing, SignalFire Wireless Telemetry
- Sandro Esposito from SignalFire Telemetry presented a compelling vision for the future of wellsite automation, emphasizing the power of wireless IoT integration.
- By combining low-power sensors with LTE-M/NB-IoT and MQTT/SparkPlugB protocols, operators can achieve real-time monitoring with minimal infrastructure.
- Case studies demonstrated how secure, cloud-connected wireless systems reduce downtime, optimize production, and eliminate legacy IT burdens. However, challenges remain in ensuring compatibility across different network environments and extending battery life. With advancements in wireless charging and satellite-based connectivity on the horizon, this presentation underscored the growing role of intelligent wireless solutions in streamlining upstream oil and gas operations
Alan Bryant, Senior Automation Engineer, Oxy
- Alan Bryant from Oxy demonstrated how the rigorous measurement standards in CCUS can enhance well site automation by improving data integrity, calibration, and real-time monitoring.
- Key takeaways included the necessity of precision in CO₂ flow measurement—where even minor variations impact sequestration compliance—paralleling the challenges of automation data accuracy in oilfield operations.
- The presentation emphasized the role of standardized methodologies, such as API MPMS 6XB for CO₂ metering, as a model for improving automation reliability. Unresolved challenges remain in establishing universal data verification protocols for automation.
- Overall, this session provided a compelling framework for cross-industry data standardization and operational excellence.
Rahul Roy, Digital Oilfields Technical Lead IPCOS, Inc.
- Rahul Roy from IPCOS delivered a standout presentation on how Digital Oilfields technology is reshaping wellsite automation. Key innovations included automated well test calibration, ML-driven back allocation, and real-time asset surveillance, reducing manual work while improving accuracy.
- Data-backed case studies showed impressive results: compressor optimization alone led to over $4.5M in fuel savings and 115k tons of CO₂ reductions annually. The challenge now is scaling these innovations across diverse operating environments.
- Overall, this presentation provided a compelling, technically rich vision of how automation and analytics are unlocking new efficiencies and cost savings in oil and gas operations.
Dave Lafferty, President, Scientific Technical Services
- We heard a compelling overview of Generative AI’s potential in oil and gas, emphasizing its role in predictive analytics, automation, and intelligent decision-making.
- David highlighted the importance of private Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) for improving AI accuracy while reducing retraining costs.
- Key concerns—such as data bias, misinformation, and intellectual property risks—were explored alongside strategies for mitigating them. While AI’s impact is undeniable, industry-wide adoption hinges on responsible implementation and robust validation frameworks.
- Overall, this presentation provided a practical roadmap for leveraging AI’s power while safeguarding operational integrity.
Example Cross Operator Panel Questions
More Highlights From The Interactive Discussions
- Q "What kind of downtime reductions have you observed after implementing AI tools? Can you share specific numbers comparing the original downtime versus post-implementation?"
- A Downtime reduction varies depending on the type of event. For liquid loading, AI-driven interventions have reduced downtime from 15 days per event to around 2–3 days. For other downtime types, detection algorithms have accelerated response times from several hours to a few days. The impact is event-dependent, and AI does not eliminate downtime in all cases, but it significantly enhances response efficiency.
- Question (From a Digital Transformation Lead at a Mid-Sized E&P Company):
- "Given your limited team size, how did you navigate the challenges of implementing a unified data analytics workflow across your operations?"
- The initiative gained traction when a key champion of the idea, who had executive connections, relocated to Houston and helped push the proposal internally. The team leveraged the companys existing technology review process to secure funding by demonstrating headcount efficiencies and improved data accessibility. The key takeaway: securing executive buy-in early and aligning the proposal with corporate efficiency goals made the initiative successful.
More Highlights From The Interactive Discussions
More Highlights From The Interactive Discussions
More Highlights From The Interactive Discussions
- Question (From a Data Science Manager at an Upstream Operator):
- "How do you classify downtime events in your system, and have you implemented structured classification methods?"
Ans: The company refines downtime classifications every year to improve analytics. Two years ago, a single downtime category could have had ten different root causes, making analysis difficult. Now, classifications are more structured, but updates are made prospectively rather than retroactively. The approach ensures better analytics moving forward while avoiding data inconsistencies from historical reclassification. - Question (From a Digital Strategy Director at an Independent Shale Producer):
- "Given the rapid development of AI-driven automation, are the technologies scalable for small and mid-sized operators?"
- Ans: Larger companies have 20+ engineers developing proprietary solutions, which isn’t feasible for smaller operators. The best approach for smaller firms is to leverage vendor solutions rather than build AI models internally. The key challenge is securing management buy-in to transition data to the cloud, breaking away from legacy workflows where critical data is stored on individual hard drives rather than in centralized systems.
- Question (From an Automation Engineer at a Mid-Cap Operator with Offshore and Onshore Assets):
- "What are the cultural differences between offshore and onshore teams when it comes to automation and operational efficiency?"
- Ans: A major cultural difference exists. Offshore engineers typically work on the same 15 wells daily and develop deep familiarity with individual wells. In contrast, onshore engineers may manage hundreds of wellsremotely, making it harder to develop well-specific knowledge. Offshore operations also involve closer coordination between engineers and field operators, while onshore automation is more data-driven and exception-based.
Day 2 Roundtable Discussions
More Highlights From The Interactive Discussions
More Highlights From The Interactive Discussions
- The discussion focused on the viability of Starlink’s low-Earth orbit (LEO) satellite connectivity for wellsite automation in remote U.S. onshore oil and gas operations. Participants explored how Starlink compares to existing communication solutions, its technical strengths and limitations, and which automation applications would benefit most from satellite-based connectivity.
- While Starlink presents a promising alternative to fiber, LTE, and LoRaWAN, key considerations include cost, reliability, cybersecurity risks, and integration with existing SCADA and automation infrastructure. The group also discussed practical deployment challenges and the future role of Starlink in enabling AI-driven remote operations.
- While Starlink presents a strong alternative for remote wellsite automation, several unanswered questions and potential limitations remain:
- Will Starlink’s network congestion increase as more users adopt it?
- What role will private LEO satellite networks play in future automation strategies?
- How can operators integrate Starlink with existing edge computing solutions to optimize bandwidth usage?
- What regulatory and compliance issues (FCC, data security) could impact widespread adoption?