A review on intelligent recognition with logging data: tasks, current status and challenges
X Zhu, H Zhang, Q Ren, L Zhang, G Huang… - Surveys in …, 2024 - Springer
Geophysical logging series are valuable geological data that record the physical and
chemical information of borehole walls and in-situ formations, and are widely used by …
chemical information of borehole walls and in-situ formations, and are widely used by …
Enhanced group method of data handling (GMDH) for permeability prediction based on the modified Levenberg Marquardt technique from well log data
AK Mulashani, C Shen, BM Nkurlu, CN Mkono… - Energy, 2022 - Elsevier
Permeability is the key variable for reservoir characterization used for estimating the flow
patterns and volume of hydrocarbons. Modern computer advancement has highlighted the …
patterns and volume of hydrocarbons. Modern computer advancement has highlighted the …
Oil logging reservoir recognition based on TCN and SA-BiLSTM deep learning method
W Yang, K **a, S Fan - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Abstract The use of Deep Learning methods to mine useful and critical information from
massive and complex logging datasets is of great importance for oil logging reservoir …
massive and complex logging datasets is of great importance for oil logging reservoir …
Application of machine learning models for real-time prediction of the formation lithology and tops from the drilling parameters
Lithology changes significantly affect the drilling program and the total cost of drilling an oil
well, therefore, it is very important to detect the lithology variation and formation tops while …
well, therefore, it is very important to detect the lithology variation and formation tops while …
Real-time determination of rheological properties of spud drilling fluids using a hybrid artificial intelligence technique
The rheological properties of the drilling fluid play a key role in controlling the drilling
operation. Knowledge of drilling fluid rheological properties is very crucial for drilling …
operation. Knowledge of drilling fluid rheological properties is very crucial for drilling …
Rock strength prediction in real-time while drilling employing random forest and functional network techniques
The rock unconfined compressive strength (UCS) is one of the key parameters for
geomechanical and reservoir modeling in the petroleum industry. Obtaining the UCS by …
geomechanical and reservoir modeling in the petroleum industry. Obtaining the UCS by …
Evaluation of different machine learning frameworks to predict CNL-FDC-PEF logs via hyperparameters optimization and feature selection
Although being expensive and time-consuming, petroleum industry still is highly reliant on
well logging for data acquisition. However, with advancements in data science and AI …
well logging for data acquisition. However, with advancements in data science and AI …
Real-time prediction of rheological properties of invert emulsion mud using adaptive neuro-fuzzy inference system
Tracking the rheological properties of the drilling fluid is a key factor for the success of the
drilling operation. The main objective of this paper is to relate the most frequent mud …
drilling operation. The main objective of this paper is to relate the most frequent mud …
NMR log response prediction from conventional petrophysical logs with XGBoost-PSO framework
A combined approach that exploits both the eXtreme Gradient Boosting (XGBoost) method
and the Particle Swarm Optimization (PSO) method was used here to predict the nuclear …
and the Particle Swarm Optimization (PSO) method was used here to predict the nuclear …
Rate of penetration prediction while drilling vertical complex lithology using an ensemble learning model
The rate of penetration (ROP) accounts for a substantial portion of the overall drilling cost.
The drilling optimization process, which mostly involves the adjustment of the mechanical …
The drilling optimization process, which mostly involves the adjustment of the mechanical …