New methods have been advanced to process, manipulate, and utilize real-time data from permanent downhole monitoring (PDM) systems such as fiber optic (F-O) pressure, temperature, and flow sensors. These interpretative methods are centered upon detailed computational fluid dynamics (CFD) simulations of the wellbore and surrounding near-wellbore region. In addition to facilitating the solution of geometrically complex flow dependencies, the CFD approach accounts for the highly coupled chemical physics inherent in multi-phase systems, particularly gas compressibility effects that impact wellbore hydraulics and heat transfer which, in turn, govern the flow, pressure, and thermal profiles in the wellbore. In the near-wellbore region, true reservoir permeabilities and porosities are honored and used to populate the CFD model. By running the model for different anticipated production scenarios, a series of production "type curve" analogues are generated that can be used to "predict" the time-dependent flow, pressure, and thermal profiles recorded by a PDM, thereby assisting interpretation of the data.
Introduction
Continuing improvements in quality and reliability have led to increased applications of F-O technology for downhole sensing applications in the harsh pressure and temperature environments of the oil and gas industry1. Moving beyond routine monitoring of pressure or temperature, F-O sensors increasingly are being deployed as components of "smart" or "intelligent" well completions to enable response automation, or at the very least, aid in actuation. For example, a F-O distributed temperature sensing (DTS) system might be deployed in a well with commingled flow in order to detect changes in production (e.g., gas or water breakthrough), upon which a sliding sleeve might be exercised to shut off production from an offending zone.
Despite increased installations of fiber optic instrumentation as a key component of intended response systems, automated remote administration of "smart" wells is still plagued by several difficulties. Indeed, far from being automated, manual intervention and decision for actuation of "smart" features are still required at present. These hurdles stem in large part from limitations in capabilities to process and interpret sensory data; although data may be recorded in real-time, they are still processed as-needed on an indeterministic time basis.
Traditional reservoir and well surveillance methods rely upon surface production data and infrequent production logging trips typically performed only during shut-in or workover and on a well-by-well basis. In contrast, a burgeoning array of fiber optic (F-O) systems promises to provide continuous acquisition of sensory data during both "normal" flowing operation as well as shut-in. However, conventional PLT analysis methods rely on multiple types of log data -- inferring flow phenomena from a combination of pressure, spinner, and temperature logs2–4. Consideration of all the data can be time consuming and laborious, typically introducing (and requiring) significant subjectivity in the analysis. This is especially difficult to extend to real-time F-O data, particularly if only one property is measured (e.g., temperature but not pressure or rate). Although F-O technology continues to advance rapidly, temperature sensing is still the primary and often single measurement recorded by F-O systems. An important question that arises, then, is how to interpret F-O thermal data and infer downhole flow phenomena. Inherently related is the need to predict the thermal profile under variable producing conditions.
Many efforts to solve this problem have focused on using multi-nodal models5–9. At each node (corresponding to a different depth), a description of the macro-structure of the well and the surrounding near-wellbore region is specified. Rock properties, fluid pressures, fluid temperatures, wellbore pressures, and wellbore diameters are typically specified. Pipe-flow correlations and simple energy balances may be used between nodes to assemble the flow behavior at each node into a contiguous wellbore flow and thermal profile. The input parameters are adjusted until a match with the measured thermal profile is obtained. While the approach has the advantage of simplicity and ease of calculation, there are several inherent assumptions that render these models inapplicable for modeling more complex wellbores.
