Abstract
A sloping depositional surface, known as clinoform, is commonly associated with prograding strata deep into water and this surface can be imaged with seismic (J.L.Rich, 1951). During a sea level drop carbonate sediment factories tend to shut down and results in periods of non-deposition. These clinoform surfaces can be cemented, resulting in a partially sealing effect. Therefore characterizing clinoforms is crucial to better understand the reservoir dynamics and hydraulic communication throughout the field.
In the Karachaganak field, Wireline Formation Pressure acquisition along the sub-horizontal well section has played a major role in the identification of semi-transmissible flow restrictions in the field. In particular, plots of depth corrected pressure measurements against distance from wellhead showed clear discontinuities confirming the existence of pressure baffles. Once these clinoforms are identified, their properties have to be calibrated against the measured pressure data, process that historically has been implemented by manual trial-and-error approach which is time-consuming and often frustrating.
This paper proposes a methodology based on assisted history matching solutions to constrain the properties and sealing degree of clinoform's regions to both Wireline Formation and Static Bottom-Hole Pressure data. The proposed approach allows engineers to integrate geological and reservoir engineering workflows into a single model driven by state-of-the-art history matching optimization techniques. This makes possible to systematically sensitize the properties of the clinoforms assuring consistency between static and dynamic models.
As a result, observed pressure has been matched, and hence the characterization of the clinoforms properties was considerably improved in a short time compared with the timeframe required by the traditional manual approach. The presented workflow is a valuable tool to set methods and gain experience using assisted history matching techniques, and furthermore, it contributes to a change in history matching philosophy by semi-automating laborious tasks achieving faster and more physically coherent solutions.
