We’re moving away from the idea that small decisions don’t have big impacts. There’s an overwhelming amount of information coming at you from all directions; however, there just isn’t much time to process that information. Drilling is getting faster and more complicated, and we’re now wondering if our insights arrive fast enough to make a difference, or do they show up too late?
The problem is that centralized intelligence is always going to be slower than your operations
Unconventional drilling has dramatically changed the way that oil and gas wells are designed and drilled. Horizontal laterals are significantly longer, and designs are changing well-to-well, and geology is varying at scales that even models cannot capture.
Historically, we’ve been able to handle these complexities using teams in a central office to review large amounts of data that was remote from the field. It was effective until the speed of operation outpaced the speed of analysis.
Chevron views this lag between operations and analysis as a structural issue (not technical). We have plenty of data, but because we rely on centralizing the analysis of that data, it takes long periods of time for us to gain insight into our drilling activities. This creates an opportunity for additional uncertainty, cost increases, and lost optimization opportunities.
Edge-enabled analytics addresses the problem
Chevron’s edge-based analytics approach brings computing capability closer to operations so that data can be analyzed near its point of origin rather than shipping it somewhere else for later review. Engineers will receive performance insight related to their drilling activities in close to real-time during drilling.
Combining sub-surface data, drilling parameters, and historical well performance in close proximity to each other enables systems utilizing edge-enabled analytics to rapidly detect patterns of behavior that would otherwise take longer to develop in traditional workflows. These systems enable adjustments to “weight-on-bit”, “rate-of-penetration”, and/or “well-trajectory” to be made with consideration to live conditions rather than assumptions regarding prior wells.
The distinction is slight, but important: Analytics no longer operate retrospectively; instead, they begin operating operationally.
Real-time insight leads to better outcomes
Many drilling-related decisions are binary; i.e., continue/drill, pause/stop, adjust/modify, etc. Systems utilizing edge-enabled analytics provide those decisions with relevant context based upon actual conditions existing down-hole. Early corrective actions can prevent later complications and issues, e.g., non-productive time, stabilized well-bore integrity, and optimized drilling rates throughout various types of formations.
Chevron’s implementation is indicative of a growing trend, namely that drilling performance improves most when learning occurs at the same pace as execution. Rather than relying on generalized models developed days/weeks ago, edge analytics allows wells to effectively teach operators how best to drill them. Over time, the learning cycle becomes self-reinforcing. Each subsequent well provides input that enhances performance relative to the next well; not via protracted analysis cycles, but via ongoing learning processes integrated directly into daily operations.
Benefits expand as portfolio sizes increase
As portfolio size expands, the benefits of applying edge analytics will also expand. With consistent data feeds and standard models applied across multiple rigs, Chevron will be able to analyze patterns of performance, identify repetitive efficiencies, and recognize potential issues sooner than before. Additionally, insights derived from one location will become instantly applicable to another.
Scalability is critical given the expanding diversity and complexity associated with increased drilling program scope. Edge systems reduce dependence on centralized chokepoints, thereby providing operational personnel greater flexibility to work independently while still adhering to analytical standards. Ultimately, Chevron’s application of edge analytics represents a fundamental change in how drilling performance is improved. By positioning intelligent functionality closer to the drill bit, Chevron has narrowed the gap between available data and timely decision-making, resulting in real-time insight serving as a source of competitive differentiation.
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