How to reduce uncertainty before excavations and interventions in mature oilfields

In mature oilfields, many operational decisions are made with incomplete information.

That is not necessarily because field teams lack experience. In many cases, the problem is simpler: the field has changed over years or decades, but the drawings, models, reports, and records did not change at the same pace.

A line was modified. A segment was taken out of service. A wellhead was abandoned. A casing string remained buried. A previous intervention was never documented well. A drawing still exists, but it no longer represents the physical reality of the site with enough accuracy.

That gap between the physical reality of the field and the information available to the team can become a real source of risk before excavation, maintenance, construction, equipment mobilization, or operational intervention.

At StrataIntel, we start from a simple idea: better field decisions require better field data.

The problem is not always visible on the surface

In oilfield and industrial operations, part of the risk may be buried.

Before excavating or intervening in an area, the team may only have partial information about what exists below grade: old lines, buried pipelines, metallic remnants, casing, abandoned wellheads, or infrastructure that does not appear clearly in the available drawings.

When that information is unclear, uncertainty increases. And when uncertainty increases, so do the operational risks:

• execution delays
• scope changes in the field
• damage to existing infrastructure
• accidents during excavation
• extra cost from rework
• unnecessary mobilization of people or equipment
• decisions made with incomplete information

The problem is not only technical. It is operational.

A poorly planned excavation does not only affect a drawing. It can affect safety, schedule, budget, contractors, production, and coordination across teams.

The gap between the model and reality

In construction, engineering, and operations, it is common to work with drawings, models, reports, surveys, or digital systems that attempt to represent reality.

But field reality always moves faster than the model.

That is one of the clearest lessons from working with processes like BIM and operational management systems: a model is only useful if it stays connected to real, current information.

Otherwise, teams end up spending enormous effort trying to force reality to fit the model, or updating the model too late for it to actually improve the decision.

That same problem appears in mature oilfield operations. It is not enough to have a drawing. It is not enough to have an old report. It is not enough to rely on a surface visual inspection.

Before acting in the field, teams need to reduce the distance between what they believe exists and what actually exists.

What reducing uncertainty in the field really means

Reducing uncertainty does not mean eliminating all risk. It means giving the team better information before a decision is made.

Before excavation, the team needs to know whether there are signals of buried metallic infrastructure. Before mobilizing equipment, the team needs a clearer understanding of the site. Before intervening on a line, the surrounding conditions need to be validated. Before planning a construction job, visible and hidden constraints need to be identified. Before sending personnel into the field, the work area needs to be converted into useful information.

That is where field intelligence becomes valuable.

Field intelligence is not simply about collecting drone imagery. It is about acquiring site data, processing it, interpreting it, and turning it into deliverables that help operations, maintenance, engineering, or project teams make better decisions.

Drones and sensors as the acquisition layer

The drone is not the final product. The drone is an acquisition platform.

The real opportunity comes from combining drones, sensors, data processing, and technical interpretation to produce information that is useful before field work begins.

Depending on the case, different technologies can contribute value:

• Aerial magnetometry. Helps detect ferromagnetic anomalies associated with buried metallic infrastructure such as lines, casing remnants, abandoned wellheads, pipelines, or undocumented metallic elements.
• LiDAR. Generates high-precision terrain, elevation, surface, structure, and site-condition models.
• Photogrammetry. Converts aerial imagery into orthomosaics, 3D models, and measurable maps for planning, tracking, and documentation.
• Gas or methane detection. Can help identify possible leaks or zones of interest related to emissions.
• Thermal imagery. Adds another observation layer for identifying temperature differences, anomalous conditions, or areas of interest in certain assets.

Each technology answers a different question.

The point is not to deploy sensors for their own sake. The point is to define which decision needs to be made and which data can reduce uncertainty before that decision is taken.

From field data to a decision map

Capturing data is not enough.

An operational team does not only need attractive imagery or heavy files. It needs deliverables it can actually use.

For example:

• anomaly maps
• prioritized risk zones
• orthomosaics of the area
• terrain models
• GIS layers
• technical reports
• coordinates for points of interest
• visual evidence from the site
• recommendations for field review or validation

The value lies in transforming the physical reality of the site into organized information.

A good survey should not end as a folder full of files no one reviews. It should help answer practical questions:

• Where are there signs of buried infrastructure?
• Which zones require greater care before excavation?
• Which areas should be reviewed before mobilizing equipment?
• What information should a contractor share with operations or engineering?
• Which risks were not visible on the surface?

Why this matters for contractors, EPCs, and maintenance teams

For contractors and service companies, reducing uncertainty before execution can become a competitive advantage.

A company that arrives in the field with better information can plan better, coordinate better, and reduce surprises during execution.

For EPC, maintenance, integrity, or construction teams, field intelligence can support pre-job planning, site review, constraint identification, and cross-discipline coordination.

In mature fields, this capability can be especially useful because the asset history is often not fully documented.

The older the field, the more likely it is that a gap exists between the drawing and reality. And that gap can cost time, money, and safety.

StrataIntel and field intelligence

At StrataIntel, we are working precisely on that gap between the physical reality of the field and the information teams need before they act.

Our approach combines drone-based data acquisition, specialized sensors, and technical processing to support decisions before excavation, intervention, maintenance, construction, or equipment mobilization.

This is not just about drones. It is about turning field data into operational intelligence.

Because in real operations, the most important moment to reduce risk often happens before the team reaches the site.

Conclusion

Mature oilfields present a complex reality: aging infrastructure, incomplete information, accumulated changes, and risks that are not always visible on the surface.

In that context, the question is not only how to execute better. The question is how to arrive at execution with better information.

Reducing uncertainty before acting in the field can improve planning, protect personnel, avoid damage, reduce delays, and support better operational decisions.

At StrataIntel, we believe the future of field intelligence lies in closing the loop between physical reality, technical data, and operational decision-making.

Better field decisions require better field data.