... conditions were constructed to train a reliable surrogate model. This study successfully developed the 3D F-IHNF deep learning model to effectively track dynamic responses and complex flow fronts arising from cyclic injection and production in UHS. The architecture's integration of Convolutional LSTM, 3D...

... in deep learning for equipment failure investigation analysis in drilling tools. The first component of our approach focuses on leveraging NLP for automated incident classification from a mixture of structured and unstructured text data within the oil and gas industry. With vast volumes of data generated...

... mudrock mudstone pore network model permeability prediction deep learning artificial intelligence cnn-bilstm-attention model reservoir information geology flow in porous media prediction attention mechanism pore radius Introduction Porous media are materials composed of a solid...

... the transformative potential of customized LLMs, capable of generating reports in just 10 minutes—compared to 4 hours manually—thereby revolutionizing labor-intensive processes and significantly improving work efficiency in the well logging industry. deep learning geologist well logging natural language...

... outcomes in detailed existed-well assessment tasks. agent geology geologist rock type artificial intelligence arxiv natural language application accuracy mechanism large language model manuscript deep learning machine learning interpretation agent framework curve reconstruction...

..., and service quality across geographically distributed drilling sites. deep learning artificial intelligence agent machine learning drilling operation vector database edge device procedure agent system automation enhanced observability & security suite sre team natural language database...

... Abstract Geological carbon storage (GCS) is crucial for reducing greenhouse gases and mitigating global warming. Deep saline aquifers are regarded as optimal sites for implementing GCS. This paper proposes an LSTM-based deep learning model that can rapid forecast the temporal evolution...

... and more stable error fluctuation range. deep learning geology neural network reservoir characterization artificial intelligence prediction machine learning algorithm prediction accuracy depth prediction pso-bp prediction result geologist optimization problem evolutionary algorithm...

... Abstract In subsurface flow simulation, data-driven deep learning surrogate models have emerged as a promising alternative to traditional simulation methods. However, a major challenge is the large amount of high-fidelity simulation data required for training, and purely data-driven flow...

... geologist production forecasting production control multi-factor lstm oil well production oil production forecasting accuracy production data production monitoring well production production fluctuation information deep learning artificial intelligence machine learning lstm operation...

... sections of electrical image data that can predict high-quality images using deep learning algorithms. While traditional image inpainting algorithms fail to extract meaningful features from pad data, deep learning networks using the encoder-decoder architecture can automatically learn representations...

... architecture containing a model trained with synthetic seismic data to improve SNR of field seismic data; hence, interpretation. artificial intelligence architecture deep learning seismic data geology machine learning geologist reservoir characterization interpretation data conditioning...

... with generative deep learning algorithms has been developed. This innovative method leverages the power of multi-source experimental data and generative deep learning algorithms to enable the rapid and efficient reconstruction of three-dimensional (3D) digital cores. By integrating sophisticated components...

... and horizontal well trajectory design. sedimentary rock geology coalbed methane artificial intelligence deep learning rock type reservoir characterization geologist complex reservoir coal seam machine learning organic-rich rock coal seam gas cnn-based seismic impedance inversion enhanced...

... drillstem/well testing production logging deep learning production monitoring mudrock sedimentary rock prediction feature transfer machine learning artificial intelligence dataset reservoir geomechanics geological subdiscipline proppant fracture design parameter real data hydraulic...

... Abstract Faults play a crucial role in the exploration and development of oil and gas resources. In recent years, deep learning algorithms for fault interpretation have shown signs of progress and are still in a rapid development stage. Since most of the research at this stage uses 3D synthetic...

... to the prediction of real-time rate of penetration. The results are expected to provide guidance for the further study on the increase of drilling speed and reduction of well costs. geologist neural network deep learning drilling operation geology drilling fluids and materials drilling fluid management...

... network machine learning deep learning artificial intelligence multivariate time sery accuracy multiplex visibility graph pipeline system operation condition information visibility graph liquified natural gas (lng) time sery attention mechanism recognition algorithm classification task kl...

... sections does not need to be structured and images do not need to be labeled. Thus, we maximize the utilization of existing data without the need for these time-consuming tasks that are usually needed for most formulations of machine learning problems. sedimentary rock deep learning geological...

... management system international petroleum technology conference asset management agreement assessment artificial intelligence deep learning dataset gas facility machine learning application ai capability damage classifier automation solution Overview This paper features the development...