Accurate estimation of oil and gas productivity in development wells is crucial for perforation decisions and planning for future development activity. The production capacity of a well can be described by the productivity index (J), which is mainly determined by the effective permeability to the mobile fluid phase. In exploration wells, J is usually estimated from Drill Stem Tests (DST), which measure the flow rate of the reservoir section isolated for production. In development wells, however, DST's are typically scarce, and hence J must be estimated from well logs.

The log-based permeability model, K-Lambda, estimates the absolute permeability (k) from mineral abundances, which in turn are derived from geochemical logs. The model associates a specific surface area (S0) with each lithology to calculate the permeability from the surface area-to-volume ratio of the rock. In general, S0 for sand and carbonate are well defined and stable. However, S0 for clay depends largely on the clay type and varies from reservoir to reservoir. Since clay has the most significant effect on permeability, correctly accounting for its surface area is key to improving the prediction accuracy.

This paper describes a workflow to improve J estimation in development wells by calibrating the clay S0 parameter with fluid mobility, which is estimated from formation pressure pretests. Since the pretest mobility is defined as the effective permeability to the mud filtrate over its viscosity, the pretest effective permeability in water-based mud (kw) must first be converted to absolute k before it can be used in the calibration process. The conversion relies on relative permeability (krw) measurements on core samples as (Equation). Once the calibrated K-Lambda permeability log is obtained, we use it to improve J estimation in development wells. The workflow consists of the following steps:

  1. Establish a relationship between k and effective permeability to water (kw) at irreducible oil saturation (Sor), using relative permeability measurements on core samples from the exploration (or development) well.

  2. Using the relationship in step 1, convert the measured pretest permeability at discrete points from kw to k (assuming water-based mud).

  3. Calibrate clay S0 in the K-Lambda model for each pay sand with the converted k at the pretest points and compute a continuous k log with the calibrated model.

  4. Compute the relative permeability logs to water (krw) and oil (kro) from known correlations. Then, calculate the continuous effective permeability to oil (k0) as k × kro.

  5. Calculate J in the development well from k0 using the well testing data in exploration wells as a reference.

This workflow is demonstrated using formation pressure tests and geochemical logs acquired by LWD tools in an offshore siliciclastic brownfield. The productivity estimation from this workflow shows excellent agreement with actual production data in our case studies.

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