Carbonate rocks exhibit a large variation in their petrophysical properties within short distances and depths. In particular, the porosity and permeability relationship in these reservoirs is very challenging and in many cases, porosity values from logs appear constant along the reservoir interval yet display dramatic fluctuation in the associated permeability. Therefore, it is difficult to characterize the reservoir zones and to estimate a reasonable and realistic net pay value to imply in well testing pressure transient analysis (PTA) and for production logging tools (PLT) interpretation.
The paper integrates different data sets from pilot holes and associated horizontal drains which include: close spacing core data analysis, wireline data, well testing and PLT to obtain a sound conclusion about the reservoir's productivity and better PTA modelling. The paper also proposes an integrated methodology to evaluate various aspects of the reservoir's expected performance, and ultimately, to estimate a discrete PLT-driven permeability in highly deviated wells and long horizontal reaches.
In addition, the estimation of discrete dynamic and independent permeability from horizontal PLTs is important because it increases the data intensity population into the reservoir modelling and delivers a better control on reservoir performance.
The average horizontal PLT permeability is calculated using the PLT flow regimes and the adapted horizontal well testing equations. In the proposed approach, the flow units (FU) are associated to the average PLT permeability to estimate reasonable single zone permeability.
The proposed static (core, logs) and dynamic data (WFT, PTA, PLT) bridging evaluates the single-well completion in horizontal drains as it describes the different flow unit's production partitions and not the routinely used "average" profile across the opened interval onto production. Ina addition, the Flow facies enables the construction of the conceptual geological model to introduce for the dynamic reservoir simulation. The methodology suggests a better well performance evaluation that leads to an enhanced field completion strategy for optimizing hydrocarbon recovery through smart completions planning.