A New Approach for Predrilling the Unconfined Rock Compressive Strength Prediction

Abstract

A reasonable knowledge of rock's physical and mechanical properties could save the cost of drilling and production of a reservoir to a large extent by selection of proper operating parameters. In addition, a master development plan (MDP) for each oilfield may contain many enhanced oil recovery procedures that take advantage of rock mechanical data and principles. Thus, an integrated rock mechanical study can be considered an investment in field development. The unconfined compressive strength (UCS) of rocks is the important rock mechanical parameter and plays a crucial role when drilling an oil or gas well. A drilling operation is an interaction between the rock and the bit and the rock will fail when the resultant stress is greater than the rock strength. UCS is actually the stress level at which rock is broken down when it is under a uniaxial stress. It can be used for bit selection, real-time wellbore stability analysis, estimation an optimized time for pulling up the bit, design of enhanced oil recovery (EOR) procedures, and reservoir subsidence studies. Rock strength can be estimated along a drilled wellbore using different approaches, including laboratory tests, core–log relationships, and penetration model approaches. Although this rock strength profile can be used for future investigation of formations around the wellbore, they are actually dead information. Dead rock strength data may not be useful for designing a well in a blind location (infill drilling). Rock strength should be predicted prior to drilling operations. These sort of data are helpful in proposing a drilling program for a new well. In this research, new equations for estimation of rock strength in Ahwaz oilfield are formulated based on statistical analysis. Then, they are utilized for estimation of the rock strength profile of 36 wells in a Middle Eastern oilfield. An artificial neural network is then utilized for prediction of UCS in any predefined well trajectory. Cross-validation tests showed that the results of the network were compatible with reality. This approach has proven to be useful for estimation of any designed well trajectory prior to drilling.