Data-Driven Decisions
In traditional oilfield operations, decisions were often based on experience, intuition, and incomplete data. In a digital oilfield, decisions are supported by real-time data, historical trends, and analytical models - leading to faster responses, fewer mistakes, and better outcomes.
The Data-to-Decision Pipeline
Collect
Sensors, SCADA, and manual inputs generate raw data - pressures, temperatures, flow rates, equipment statuses, and maintenance logs.
Validate & Clean
Remove bad readings, fill gaps, handle outliers, and apply engineering unit conversions. This step is critical - bad data leads to bad decisions.
Analyse
Apply descriptive analytics (what happened?), diagnostic analytics (why?), predictive analytics (what will happen?), and prescriptive analytics (what should we do?).
Visualise
Present findings on dashboards, reports, or alerts in a format that is easy to understand and act on. Context matters - a number without context is meaningless.
Decide & Act
Engineers and managers make informed decisions based on the analysis: adjust choke settings, schedule workovers, reallocate injection, or approve capital expenditure.
Types of Analytics in Oil & Gas
Descriptive Analytics
"What happened?"
Summarises historical data using charts, tables, and KPIs. Most dashboards fall into this category.
Example: A monthly production report showing average daily oil rate, total gas production, and water cut by well.
Diagnostic Analytics
"Why did it happen?"
Investigates root causes by correlating variables. Drill-down analysis and cross-plots are common techniques.
Example: Correlating ESP failure dates with sand production events to identify sand as the root cause.
Predictive Analytics
"What will happen?"
Uses ML models and statistical methods to forecast future outcomes - production decline, equipment failure, water breakthrough.
Example: A decline curve model forecasts that Well-F02 will reach its economic limit in 14 months at current rates.
Prescriptive Analytics
"What should we do?"
Recommends optimal actions. Combines predictive models with optimisation algorithms to suggest the best course of action.
Example: An optimiser recommends increasing gas lift on 5 wells and reducing it on 3 to maximise total field oil production.
Use Case: Waterflood Optimisation
A reservoir team uses diagnostic analytics to identify that 3 out of 8 water injectors are contributing to premature water breakthrough in nearby producers. By cross-plotting injection rates against producer water-cut responses and using tracer data, they determine that Injector-W04 is channelling water through a high-permeability streak. The prescriptive model recommends reducing W04 injection by 40% and redistributing that volume to W06 and W08. After implementation, the affected producers see a 12% reduction in water cut and a combined increase of 600 bbl/d of oil over the next 3 months.