Introduction

Introduction

Introducing Petrofinde AI ELM

Petrofinde is introducing a new analytical technique in collaboration with IBM Watson that uses Artificial Intelligence and ELM (Extreme Learning Machines) to analyse the global repository of geological data coupled with live data streams of information including Satellite feeds from Copernicus and DIAL data. The outcome is a revolutionary prediction tool with extreme precision.

Petrofinde’s Extreme Learning Machine (ELM) is a single-hidden layer feed-forward neural network (SLFN). Training SLFN consists of randomly generating the hidden layer weights, followed by solving a linear system of equations by least-squares for the estimation of the output layer weights. The learning strategy is very fast and gives a significantly higher prediction accuracy. Theoretically and practically, this algorithm can produce excellent generalisation performance and can learn thousands of times faster than conventional popular AI learning algorithms for feed-forward neural networks.

PetroFinde Introduction

Petrofinde is developing a kernel-based ELM (KELM), where the hidden layer feature mapping is determined by the kernel matrix. In this version, only the kernel function and its parameters are needed; the number of hidden nodes is not required. With the use of kernel function, KELM is expected to achieve better generalisation performance than basic ELM. Furthermore, as randomness does not occur in KELM, the chance of result variations could be reduced.

PetroFinde Introduction

Petrofinde in collaboration with ESA (European Space Agency) and Nvidia, will be dealing with the live treatment of satellite images. This can stack up to several tera-bytes a day, to transfer, process and analyse. The Copernicus project and Sparkin data, analyse land movement from satellite images, which could have an impact on mining, civil engineering and in the oil and gas industry. Thanks to ELM deep learning, It will evolve quickly in the domain of geoscientist assistance. The same goes for production analysis, and text mining.

Blind Studies – Trial Sample

Kernel-based extreme learning machine is used to predict missing sonic (DT) logs when only common logs (e.g., natural gamma ray—GR, bulk density—DEN, or deep resistivity—REID) are available. By using KELM, Petrofinde can create and train a supervised network model based on experimental data and then confirm and validate the model by blind-testing the results. The optimal model is at last applied to wells containing the predictor data but with lack of DT log. Petrofinde can use this work flow in GJH survey from Erdos Basin and the KELM-estimated DT logs are then integrated in the seismic inversion to identify the sandstone reservoir.