Artificial Neural Network Model for Predicting Wellbore Instability

Abstract

Drilling activities have progressed to deep and ultra deep seas in recent times and with it comes more challenges. Due to the difficulty of directly obtaining important parameters like in-situ stress and fracture gradient, simple models have been evolved. This study is a novel attempt to make up for the gap inherent in such models namely that they neglect chemical and thermal effects, settling for only effective stress and a time-dependent analysis. The study applied the Neural Network (NN) technology to predict geomechanical parameters. Neural Network (NN) as a branch of Artificial intelligence (AI) possesses the ability of training available parameters to replace data that cannot be immediately or easily acquired. Data of a well drilled in the Niger Delta Region of Nigeria was used as the case study. A training set of input data was used to train the network and a validation set ensured a completely independent measure of network accuracy. A Neural Network model was developed in Neuroph Studio, Java neural network platform and the Netbeans IDE. The model has the advantage of being easy to use, open source, cross-platform and generally designed to save the cost associated with wellbore instability.

Artificial neural network models to predict lost circulation in natural and induced fractures

“…This is done to ensure the model's robustness at generalizing to new data. Training data are used in training, verification data are Arehart [18] Drill bit diagnosis Used ANNs to find the state of wear of drill bit during drilling Dashevskiy et al [19] Real-time drilling dynamic Modeling the dynamic behavior of drilling system Bilgesu et al [20] Drill bit selection Used ANNs to select the "best" bit based on some inputs Ozbayoglu et al [21] Bed height for horizontal wells Used ANNs to predict bed heights in horizontal or highly inclined wellbores Vassallo et al [22] Bit bounce detection Used ANNs to detect bit bounce that can be used as a proactive approach to prevent bit whirl and stick-slip Fruhwirth et al [23],Wang and Salehi [24] Drilling hydraulics optimization and prediction Used ANNs to optimize and predict drilling hydraulics with a practical example Moran et al [25],Al-AbdulJabbar et al [26] Rate of penetration (ROP) prediction Used ANNs to predict ROP so that the drill time can be estimated better Gidh et al [27] Bit wear prediction Used ANNs to predict/ manage bit wear to improve ROP Lind & Kabirova [28] Drilling troubles prediction Used ANNs to forecast problems during the drilling process Okpo et al [29] Wellbore instability Wellbore stability prediction Ahmadi et al [30] Prediction of mud weight at wellbore conditions…”

Development of an intelligent decision support system to control the process of well drilling under complicated conditions

“…This approach is used in paper [2], which is based on [3,4]. In study [5], it was proposed to use the models constructed based on artificial neural networks as one of the methods of the neurodynamic theory. However, this theory is at an initial stage of development and the existing results are of the local character.…”

“…, …, n; j=1, 2, …, m; will be considered as fuzzy sets, assigned on universal set U і and Y, determined by ratios (2)¸ (5).…”

Risk Factors Evaluation for Monitoring of Well Drilling

“…Another common complication -the instability of the walls of the well with the help of a neural network was predicted by Okpo and others [47]. The program developed by the authors was used to predict the geomechanical parameters of the formation.…”