Modern energy asset planning requires the synthesis of many multi-disciplinary inputs to translate reservoir data into coherent simulations and their associated economic forecasts for effective decision making. Utilizing a nodal network model to integrate well forecasts with the asset's operational considerations allows for improved accuracy of the simulations, better guidance and generation of insights that allow for subsequent optimization of plans and more effective capital allocation.
A novel asset development planning workflow is presented which combines type curves with pad scheduling logic, surface flow constraints, costs, ownership, and price forecasts to model the cash flow of the asset through its full life. Flow capacities, costs, shrinkages, liquid yields, and carbon emissions are input at their representative nodes, enabling dynamic and accurate incremental evaluation. Existing base production and corporate type curve databases are leveraged to streamline the workflow, and several scenarios and sensitivities are examined.
The novel workflow is compared against a more traditional method using average infrastructure costs and high level capacity assumptions.
As a result of enabling more nuanced operational inputs, the nodal network was able to predictively simulate an asset's value within the given constraints and dynamically re-evaluate the full model after inputs were adjusted. For example, the model could automatically reroute production when a constraint was hit, then evaluate the liquid extraction and fee structure based on the new routing. Each iteration's production forecast included the latest base production, well forecasts, drilling schedule shifts, facility throughput constraints, shut-ins expected due to nearby scheduled fracs, third party offtake and fee structures, carbon emission costs, liquid extractions, and "take or pay" penalties. The outcome was a more accurate representation of incremental economics associated with development options, insight into potential optimizations, and improved quality of capital allocation decisions.
In addition, the novel workflow was less cumbersome to manage than the traditional workflow. In the conventional process, obtaining accurate network inputs first requires assumption of the flow path. Any subsequent adjustment to the drilling schedule or type curve can change the production timing and routing, requiring the analysis to be manually redone.
The traditional method of planning from the type well database alone does not allow a practical means of incorporating schedule and flow details, especially when the plan changes frequently or when considering multiple scenarios. The resulting forecast relies upon broad assumptions that may be materially incorrect in some cases, preventing optimization of planning decisions. Recent systems integrations allow sharing of data between tools which enables more powerful computational methods for improved forecasting and decision making.