There’s a lot of talk these days about “integration”—in new product launches, company visions or even petroleum science degrees. Terms such as “integrated operations” (IO) refer to a new way of working that is now an integral part of the oil and gas lexicon.

Yet when we talk about “integrated petroleum technology,” what do we actually mean?

For me, integration means uniting technologies to support the decision-making process across the reservoir life cycle and from exploration to production and beyond. It means putting data, knowledge and analysis in the hands of decision makers spanning exploration and prospect evaluation right through to simulation and day-to-day production operations. IT, and in particular software, is the “technology bridge” that enables this integration to happen. And the best means of delivering truly integrated operations is through the reservoir model.

Reservoir modeling today stands at the epicenter of the upstream oil and gas workflow, not only in the mapping, understanding and predicting of future oil and gas reservoir behavior over the asset life cycle but also in day-to-day production. Reservoir models form the glue that brings different elements and players in reservoir and production management together. They provide a broad ecosystem where real-time data are continually fed back into the model for analysis by geophysicists, geologists, reservoir engineers, drillers, asset managers, production and reservoir engineers, auditors, and senior managers, all of whom are responsible for crucial decisions relating to the development of the field.

Yet to ensure that this vision becomes a reality, it’s equally important to appreciate the huge challenges reservoir modeling faces. First, there’s the growth in complex geologies and tectonics. In frontier regions, reservoir models must accurately represent complex structures such as thrust faults and salt domes while at the same time honoring stratigraphic relationships and fault geometries. How operators and reservoir models account for these complexities in the geology and physical interactions has a huge impact on production decisions further down the line.

Second, there’s the issue of scale and level of detail. We know from geological analogues that reservoir porosity and permeability can vary dramatically over a range of just a few centimeters. These small-scale heterogeneities that are beyond our ability to image can be detected in part by a careful analysis of daily production data.

Unfortunately, centimeter-scale representations of the reservoir are not currently computationally feasible within a standard field development timeframe. Therefore, successful reservoir models need to provide a realistic depiction at an appropriate scale of all the geometries and properties that impact fluid flow and volumes. It’s through a realistic representation of this data that operators can understand the risks associated with their models and how these risks impact field development decisions.

Third, a tight and integrated workflow that enables all members of the asset team to share data and work together toward common objectives—namely the maximum productivity of the reservoir—is crucial. To meet this and other challenges, the development and deployment of a scalable, flexible and collaborative computing solution (whether via internal or external clusters) becomes inevitable.

Today reservoir modeling is meeting the challenges of geological complexity, detail and integration. Whether through generating a range of plausible scenarios or model realizations or testing the sensitivity of production to uncertainties in key parameters, geologists and production engineers are using reservoir models to assess the range of production outcomes and make sure facilities are in place to meet those needs.

Ultimately, through standardized risk assessment processes, operators will be able to balance risk across their full portfolio of assets and use reservoir modeling as the technology bridge that brings together all available data from the field, whether subsurface, metering or production data. Operators also need to be able to update the models in near-real time to explore and impact production scenarios and decisions.

Through this approach and a focus on connectivity, flexibility and scalability, reservoir modeling can be the technology bridge that unifies the reservoir life cycle, reduces exposure to risk and delivers increased investment returns.

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