How a Western Anadarko Basin Operator Unlocked 15% Cash Flow Uplift in 90 Days

Executive Summary

Company: Leading upstream operator, 1,800+ well portfolio in the Western Anadarko Basin

Challenge: Despite $10M+ in SCADA investment, operators still spent mornings building spreadsheets. No system answered the fundamental question: what should we be working on right now?

Approach: Three-phase WellOPS rollout—data integration, cash flow delta forecasting and scoring, dynamic routing and real-time prioritization.

Result: 15% gross cash flow improvement in 90 days, with faster production recovery and dramatically improved team accountability.

The Situation

The operator manages 1,800+ wells across the Western Anadarko Basin—a geologically complex landscape with stacked reservoirs including the Granite Wash, Marmaton, and Mississippian formations. Each interval exhibits distinct characteristics: variable pressure gradients, differing gas-oil ratios, unique production decline patterns. Managing this complexity demands sophisticated operational approaches.

Over the previous five years, the company had invested more than $10 million in SCADA systems, field automation, and pump-by-exception frameworks. These investments were substantial and intentional. Equipment was modern, telemetry was in place, and alerts were being generated. On paper, the infrastructure for operational excellence existed.

Yet when leadership conducted an honest assessment of cash flow impact, the results were sobering. The massive technology investment had not translated into meaningful financial improvement. Production uptime was adequate. Well counts were stable. But cash flow—the ultimate measure of operational success—had plateaued.

What was the disconnect? The systems were collecting data. The problem was that this data wasn't driving decisions in the field.

The Challenge

The root cause became clear through cross-functional investigation: the operator had built a world-class data collection infrastructure without building a data-to-action pipeline.

Alerts generated by SCADA systems were neither aggregated nor distributed in a way that operators could act on. SCADA data lived in its own world. Production forecasts (from Aries) lived in another. Work order systems (CMMS) tracked maintenance requests but not economic priority. GIS had route optimization potential but no operational context to power intelligent routing. Each system was informationally rich but operationally isolated.

The practical impact played out every morning at 5:47 AM. A lease operator would arrive, turn on a legal pad, and spend the next 45 minutes building a mental model of the day. Which wells should be checked? Which ones were producing? Where were the tank problems? What did maintenance need? The operator had tribal knowledge and gut feel—both valuable—but was working without economic context.

By the time the route was sketched out, critical information had already aged. Changes that happened overnight (a SCADA reading, a tank level update, a shift in asset economics due to commodity price movement) were missed. The route was built on yesterday's data, not today's reality.

More fundamentally: no system ranked work by economic impact. A well losing $2,000 per day in foregone production received the same scheduling weight as a routine tank check. Efforts were spread too thin across a "cover everything" mentality that masked underperforming assets and created maintenance burdens that didn't move the needle economically.

This wasn't a data problem. It was an intelligence problem. The company had the raw material for brilliant decisions. They lacked the decision engine.

The Approach

The operator adopted a three-phase, 90-day implementation of WellOPS designed to minimize disruption while building capability progressively.

Phase 1: Data Integration and Unified Foundation (Weeks 1-4)

The first phase established a single source of truth. Real-time SCADA telemetry (pressures, rates, alarms), Aries production forecasts, work order statuses, tank levels, and equipment metadata were extracted and loaded into a unified cloud-based data warehouse. A dimensional schema was employed—preserving time-series fidelity, enabling cross-functional traceability, and supporting contextual tagging across assets, routes, and tasks.

This wasn't a rip-and-replace exercise. Existing systems remained untouched. WorkSync read from them; they didn't read from WorkSync. The integration was deliberately one-directional and non-invasive. SCADA continued to operate as it always had. Aries forecasts ran on schedule. The only change was that these disparate streams now flowed into a unified model where they could be correlated and contextualized.

Phase 2: Mobile Application and Real-Time Visibility (Weeks 5-8)

Once the data foundation was established, a lightweight mobile application was deployed for field use. Operators accessed daily priorities, asset history, and task details directly from their phones—eliminating the morning spreadsheet exercise entirely.

For the first time, an operator could arrive at a well and see:

• Its historical production rate and current SCADA trending
• Whether its performance was above or below forecast (and by how much in economic terms)
• Risk indicators (time since last visit, alarm history, component failure probabilities)
• Tank status and liquid management priorities
• Work order history and open maintenance items

Supervisors and engineers gained real-time feedback loops from the field. Instead of waiting for end-of-day reports or morning standups, they could see what crews were doing and adjust priorities in real time.

Phase 3: Dynamic Routing and Economic Prioritization (Weeks 9-12)

The final phase unlocked the full power of the system: continuous economic prioritization and route optimization.

A digital twin of field operations was deployed to mirror asset performance and calculate daily cash flow deltas. For each well, the system extrapolated the last 48 hours of SCADA data using regression analysis. This projection was compared against the Aries forecast. Any gap between expected and actual performance was multiplied by current market pricing to compute a dollarized cash flow loss per well.

This cash flow delta became the foundation of prioritization. But it was only one lens. Wells were also scored on:

• Risk exposure (time since visit, alarm history, regulatory constraints, component failure probability)
• Liquid management urgency (tank levels, hauling schedules, likelihood of reaching critical thresholds)
• Feasibility and confidence (accessibility, crew expertise required, model confidence)

Each factor was weighted and combined into a 0-100 priority score that recalculated every night.

The prioritization engine fed into a route optimization algorithm that used GIS drive-time matrices, private road maps, and historical travel deviation data to generate economically optimized routes. Every morning at 6:00 AM, operators received mobile-ready route maps that:

• Visited high-cash-flow-delta wells first
• Clustered work geographically to minimize non-productive time (NPT)
• Accounted for tank urgency thresholds
• Respected crew shift constraints and load limits

The routes weren't advisory—they were operationalized. Operators executed them. Exceptions were rare. Schedule adherence achieved <5% variance between planned and executed work by end of day.

For the first time, a field operator knew not just what to do, but why. Economic context was transparent. The work had purpose grounded in financial reality, not management decree.

The Results

The measurable impact arrived quickly and compounded over time:

Cash Flow Improvement: +15%

The 15% uplift breaks down into two components: faster recovery of deferred production and higher productivity per operator hour. Wells that had slipped below forecast were identified and addressed more quickly. Deferred production—output that never happens because corrective action came too late—dropped substantially. Additionally, because operators were no longer wasting time building schedules or driving to low-priority locations, they recovered 15-20% more productive hours per day.

Inventory Reduction: -70%

Tank management improved dramatically. The Liquid Management Index flagged wells approaching critical tank thresholds before overflow became a risk. Hauling schedules aligned with production forecasts rather than arbitrary schedules. The result was a 70% reduction in average oil and water inventory across the portfolio while paradoxically reducing tank gauging (routine tank checks) by 60%. Better forecasting meant fewer surprises and less over-servicing.

Miles Reduction: -25%

Route optimization eliminated unnecessary driving. Wells that didn't need to be visited on a given day weren't visited. Geographically distributed wells were clustered into economically coherent routes. The reduction wasn't just about fuel cost savings—it freed operator hours for higher-value work and reduced safety risk exposure (fewer miles driven equals fewer incidents).

Faster Issue Resolution: 50% MTTR Improvement

Engineering productivity improved measurably. Economic well failures (wells underperforming forecast by a significant margin) were identified and routed to repair teams faster. Mean time to resolution for these failures dropped 50%.

Beyond the Metrics: Culture and Accountability

The quantified improvements were significant. But the qualitative shift was equally important.

Field operators reported a marked change in how they approached their work. Previously, the morning was spent in reactive mode—building a route based on memory and proximity. Now it was purposeful. Operators understood which wells mattered most and why. Economic context was transparent. A well flagged as high-priority wasn't just "management says so"—it had a $3,000/day cash flow impact that the operator could see in the app.

This transparency created accountability. Crews saw the outcomes of their work reflected in the system. High performers were visible. Performance gaps became apparent without blame—they were simply data. Team members took greater ownership of their roles and began proactively flagging anomalies and suggesting optimizations, knowing their insights would be valued and visible to the broader team.

Morale improved—not because the work became easier, but because the work had clarity and purpose.

What's Next

The operator is now planning Phase 2 of the WellOPS deployment, extending optimization into equipment-level performance. Artificial lift systems will be analyzed for tuning opportunities. Compressor performance will be modeled to identify efficiency gains. Chemical optimization (corrosion inhibitors, demulsifiers, etc.) will be prioritized by ROI impact.

The closed-loop nature of WellOPS means that each night's model retraining incorporates learnings from the previous day's execution. Prediction accuracy improves. Route optimization gets smarter. The compounding effect is real: today's 15% cash flow uplift is the baseline for next quarter's improvements.

Leadership is also exploring expansion into adjacent domains—pump optimization, facility bottleneck analysis, and predictive maintenance integration—to extend the economic prioritization framework across the entire asset base.

Key Takeaways

• Economic prioritization fundamentally changes field execution. Shifting from barrels of oil equivalent to cash flow delta as the core prioritization metric surfaces opportunities that aggregate volume metrics obscure.

• Data integration without system replacement is achievable. WorkSync read from SCADA, Aries, CMMS, and GIS without requiring rip-and-replace deployments or disrupting existing operations.

• 6:00 AM clarity drives accountability and morale. When field teams have a shared, data-driven view of priorities backed by economic context, they operate with purpose and ownership rather than reactive firefighting.

• Modular architecture accelerates iteration. The ability to adjust prioritization logic and route optimization algorithms in real time—without system destabilization—shortened feedback loops and enabled rapid improvement.

• Closed-loop validation compounds improvement. By comparing nightly forecasts against actual execution, the system learns and improves every day. Forecast accuracy increases. Route optimization gets better. The compounding effect is dramatic over 90+ days.

See What 15% More Cash Flow Looks Like for Your Operation

The Western Anadarko Basin operator's transformation wasn't driven by drilling new wells or implementing breakthrough geology. It was driven by reimagining how data flows into decision-making. If your operation has invested in SCADA and production accounting systems but hasn't connected those investments to daily field execution, the same opportunity exists in your portfolio.

Let's talk about what this could mean for your cash flow. Schedule a conversation with our team.