Near Wellbore Predictive Methodology, 18-R8150

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Principal Investigators
Dr. Christopher J. Freitas
Dr. Nicholas Mueschke

Inclusive Dates:  04/01/10 – Current

Background - Current deepwater oil wells cost more than $100 million to plan, drill and complete (a completion is all the design, hardware and components required to create a producing well). These costs are incurred prior to any oil being produced and revenues generated. Thus, once the well is on-line, the oil producer is strongly motivated to operate the well in a manner that recoups the well investment costs in as short a time period as possible. While high oil producing rates are desirable for maximizing the return on these expensive deepwater wells (such as in the Gulf of Mexico), this also significantly elevates the risk of well failures caused by ingestion of produced sand into the well. Mobile sand results from structural failure of the reservoir as a consequence of high fluid velocity and large drawdown pressures in the near wellbore region. So, as the value of hydrocarbon reserves and the costs of remediation of failed wells increases, a renewed emphasis is being placed on proper well completion design, operation and performance prediction by the oil and gas industry. There is, unfortunately, an industry-recognized technology gap within the oil and gas industry for advanced predictive methodologies for the design, analysis, and assessment of near wellbore performance. When designing or analyzing specific completions, a completions engineer will typically use a variety of independent, simplified semi-analytical tools to predict the characteristics of the well. These analytical tools tend to be incorporated into spreadsheets. Unfortunately, these simplified analytical models are insufficient to predict the multi-scale (spatial and temporal) inflow characteristics of the well and reservoir structural performance in the near wellbore region. Using these tools for design and analysis generally leads to overly simplistic analyses of what is a complex nonlinear problem, and often results in poor business decisions caused by the inadequate prediction of outcomes and uncertainty ranges.

Approach - Because of this technology gap, a distinct opportunity  exists to develop a predictive methodology based on mathematical models and solution methods for the near wellbore region that integrates into a single approach the disparate length and time scales of this problem for use by completion/production engineers. This project attempts to address this need through the formulation of a predictive methodology based on sets of mathematical models and solution methods that allow for (1) investigating and analyzing through computational simulation the near wellbore region and its characteristics as a function of time and space and (2) designing and assessing well performance and predicting future operational states. The model forms and methods and the associated resulting software shall be applicable to a range of sand completion types. These models and methods will allow for developing a functional design tool specific to each client needs (paid for by the client) and capable of aiding completion/production engineers during completion design, evaluation and operation. The predictive methodology will ultimately allow for completion/production engineers to answer key questions such as: (1) what are the appropriate near wellbore operational limits (drawdown and depletion rates) given current conditions for the completion and reservoir? (2) how is the completion performing and are we realizing the full inflow potential, and hence value from the well? (3) what are appropriate well intervention options and expected performance ranges given the state of the well?

Accomplishments - The overall architecture for wellbore completions and production has been developed and all the key elements required in completions evaluation and selection, completions appraisal, completions definition, and completions execution have been defined. This architecture provides the outline or process diagrams for the overall predictive methodology that is currently under development.

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