UCS Neural Network Model for Real Time Sand Prediction

Abstract

Exploration and production activities have moved into more challenging deep-water and subsea environments. Many of the clastic reservoirs in these environments are characterized by thick overburden, HP-HT and largely unconsolidated formations with challenging sand management issues. For effective overall field/reservoir management, it is crucial to know if and when sand would fail and be ultimately produced. Field-life sanding potential evaluation and analysis, which seeks to evaluate the sanding potential of reservoir formations during the appraisal stage and all through the development to the abandonment stage, is therefore necessary so that important reservoir/field management decisions regarding sand control deployment can be made. Recent work has identified Unconfined Compressive Strength (UCS) as a key parameter required for the evaluation and analysis of sanding potential of any reservoir formation. There is therefore the need to be able to predict this important sanding potential parameter accurately and in real time to reduce the level of uncertainties usually associated with sanding potential evaluation and analysis. In this work, neural network coded in C++ was trained with log-derived petrophysical, geomechanical and textural data to develop a stand-alone model for predicting UCS. Real-time functionality of this model is guaranteed by real time data gathering via logging while drilling (LWD) and other measurement while drilling (MWD) tools. The choice of neural network over and above other methods and techniques which have been widely used in the industry was informed by its ability to better resolve the widely known complex relationship between petrophysical, textural and geomechanical strength parameters.

Evaluation of Interactions Between Oilfield Chemicals and Reservoir Rocks

“…However, the negative effects on the reservoir geomechanical properties are not always fully considered. Therefore, it is imperative to evaluate the geomechanical effects of these oilfield chemicals on the properties of the reservoir rock to be able to develop accurate sand failure prediction models (Oluyemi et al 2010). Chemical-rock interaction occurs via adsorption, which is the binding of chemical to the rock (substrate) surface through Van der Waal forces.…”

Geomechanical effects of oilfield chemicals on sand failure in reservoir rocks

“…However, the potential deleterious geomechanical effects of these chemicals on the reservoir formation rock are often not considered by the current industry approach to geomechanical evaluation and prediction of sand failure of reservoir formations that have experienced substantial use of these chemicals. Geomechanical evaluation of the effects of these oilfield chemicals on the properties of the reservoir rock is required in the development of effective sand failure models (Oluyemi et al, 2010). The prediction and prevention of sand production in reservoir rocks has constantly given the oil and gas production engineers concern for decades.…”

Conceptual Physicochemical Models for Scale Inhibitor-Formation Rock Interaction

“…Evaluation of geomechanical properties of reservoir formation is a key requirement in sand production related field optimization [Oluyemi et al 2010]. In reservoir formations, which have had substantial contact with scale inhibitors and other related oilfield chemicals via field chemical injection programme, geomechanical evaluation of the rock is often done without any consideration for the likely effects of these chemicals on the formation strength [Oluyemi 2007].…”