Pseudo Density Log Generation Using Artificial Neural Network

Abstract

This paper presents the results of using artificial neural networks (ANN) in an inverse mapping problem for earthquake accelerograms generation. This study comprises of two parts: 1-D site response analysis; performed for Dubai Emirate at UAE, where eight earthquakes records are selected and spectral matching are performed to match Dubai response spectrum using SeismoMatch software. Site classification of Dubai soil is being considered for two classes C and D based on shear wave velocity of soil profiles. Amplifications factors are estimated to quantify Dubai soil effect. Dubai's design response spectra are developed for site classes C & D according to International Buildings Code (IBC-2012). In the second part, ANN is employed to solve inverse mapping problem to generate time history earthquake record. Thirty earthquakes records and their design response spectrum with 5% damping are used to train two cascade forward backward neural networks (ANN1, ANN2). ANN1 is trained to map the design response spectrum to time history and ANN2 is trained to map time history records to the design response spectrum. Generalized time history earthquake records are generated using ANN1 for Dubai's site classes C and D, and ANN2 is used to evaluate the performance of ANN1.

Generation of Synthetic Density Log Data Using Deep Learning Algorithm at the Golden Field in Alberta, Canada

“…In these cases, a solution is to acquire additional well log data by new drilling or by rerunning the well logging to obtain the required log type for an already drilled well. However, drilling a new well or stopping production to rerun logging causes a huge additional cost, and some log types are not measurable owing to casing [1][2][3].…”

“…Besides, the quality control of training data considerably affected the trained prediction model. Long et al built synthetic density log with a concept of pairwise well prediction [3]. Even though eight wells were available in the field, they chose only one ideal well to train a neural network.…”

“…Even though eight wells were available in the field, they chose only one ideal well to train a neural network. However, the two aforementioned studies used a simple neural network model with a single hidden layer [3,4].…”

“…If the goal is to construct a reliable porosity model and a target field lacks sonic log, a prediction model for sonic log should be trained with the available log data. However, previous prediction models tried to make a prediction model without considering which logs are needed for the prediction and why [2,3,6]. Therefore, a trained model required numerous well logs for the input layer and hundreds of wells were used for training [5,7].…”

“…Therefore, the density log is assigned to the output layer in a neural network. Long et al [3] created a prediction model of synthetic density log but it required more than 30 data for the input layer. In the target field, this trained model cannot be applied because the log data for the input layer are not available for most of the wells.…”