Lithology Prediction Using Convolutional Neural Network Algoritm Study Case Poseidon Field Australia Basin

Abstract Convolutional Neural Network (CNN) method is one of a machine learning algorithm that is adapted from the the way of human brain works and is used to facilitate the processing of large amounts of data. The CNN method predicts the lithology of a well with well log data, there are Gamma-Ray Logs, Density Logs, Neutron Porosity Logs, and DT Logs. The CNN model is formed by several layers, such as 2 Convolutional Layers, 2 Dense Layers, 0.5 Dropout Layers, and 256 Dense Nodes. Contrary outcomes are observed in predictions that were previously generated utilizing 10 input parameters from Poseidon2, Poseidon North, Well Proteus1, and Poseidon1 data. The lithology frequency of Claystone and Siltstone is the dominant type based on the data from Poseidon2, Poseidon North, and Proteus1 Wells. The Poseidon1 Well data mostly consists of Claystone lithological frequency. The trained model utilizes data from the Poseidon2, Poseidon North, and Proteus1 wells, which exhibit lithology distinct from that of the Poseidon1 Wells. Consequently, the model is limited to predicting lithology based solely on log types that align with the training data.. The amount of 3730 initial data from the Poseidon2, Poseidon North, and Proteus1 wells were entered and trained which later became the CNN model and then tested the 820 new data. The accuracy of the lithology prediction was done by comparing the actual facies with the predicted facies which had an accuracy of 87.9% at the Poseidon2, Poseidon North, and Proteus1 wells. The resulting RMSE value is 1.4. As a result, the CNN method can predict the lithology according to the original facies and identify the lithology of the Poseidon1 well as a target at a depth of 4620 m to 4660 m with Claystone lithology. CNN method can also predict thin layers such as Ferruginous Calcilutite and Altered Volcanic. The input section of the CNN method is required to use well report data that matches the depth data.