The low price of oil has had an immediate effect in the planning departments of oil companies. They were forced to shift the focus and carefully rank and select only those developments that would ensure profitability in the production of oil and gas. Hence, the field-development projects need to include and consider not only a static or dynamic subsurface characterization but also the production-systems and facilities options, to trigger profitability and establish clear breakeven thresholds. More than ever, the consideration of deep water, tight reservoirs, shale oil, remote locations, or environmentally critical plays is placed under the microscope. Increasingly difficult project economics has delayed or stopped investments that were estimated to be safe and profitable before the price debacle.
With crude-oil prices continuing to languish, margins in tight-reservoir-asset developments have continued to tighten and new drilling and completion activity, of course, is substantially reduced. Looking back 2 years, the focus in onshore-asset development has essentially shifted entirely from fast-paced growth of tight hydrocarbon reservoirs to production enhancement from existing (but still profitable) wells, as well as to maximizing productivity from the smaller number of new well completions. However, enhancing production from multizone, propped-fracture completions in tight reservoirs, for example, is not straightforward. There are questions to address, especially with respect to the understanding of the contribution of natural fractures and induced unpropped (IU) fractures.
One of the many challenges we face today in the petroleum industry is the management of data and information. In some instances, we are overwhelmed by the amount and diversity of formats, and, in other cases, we are blinded from the right information to understand a process (What has happened?), to predict the immediate future (What could happen?), or to make proper decisions (What should we do?). The answer to these questions is data analytics supporting appropriate engineering and management judgment and the modeling of actual energy scenarios.
Why is it that many of our completion quality decisions are focused on cost and not deliverability? I can think of two primary reasons: (a) We lack the metrics to support our decisions, and (b) we do not have consistent practices (e.g., laboratory and design work, deployment processes) across our wells to allow us to compare our results. I suspect that each of you can think of others. Both of these areas offer improvement opportunities.