.... These factors lead to low data rates, high error rates, and unreliable communication if using conventional MPT techniques. noise machine learning measurement while drilling drilling measurement deep learning artificial intelligence annual logging symposium pulse signal telemetry spwla 66th...

... geological subdiscipline spwla-2025-0003 electrofacies prediction lithology log analysis machine learning image log prediction membership function mudstone classification variation borehole image log formation reservoir characterization annual logging symposium spwla 66th transition...

..., Gaussian decomposition, or machine learning techniques, have been developed to partition both 1D and 2D distributions for accurate quantification of pore systems and the fluids contained within. Challenges still remain, partly caused by the continuous distributions themselves, which are derived from...

... intelligence machine learning permeability assessment resistivity measurement assessment constriction factor permeability estimate micp measurement SPWLA 66th Annual Logging Symposium, May 17-21, 2025 DOI: 10.30632/SPWLA-2025-0013 TORTUOSITY ASSESSMENT FOR RELIABLE PERMEABILITY QUANTIFICATION USING...

.... Based on measurements of these sensors, the machine learning method is then utilized to obtain fluid compositions, gas-oil ratio (GOR) and phase fractions. Furthermore, application of artificial intelligence to real-time fluid identification is presented in detail based on downhole optical data measured...

... rock variation rock texture rock class plane investigation pixel annual logging symposium spwla-2025-0017 geological subdiscipline textural feature spatial variation heterogeneity tensor well logging feature contrast machine learning rock slice square window assessment rock image...

... their diagnostic capabilities. geology geologist fluid dynamics flow in porous media well logging artificial intelligence formation testing log analysis model machine learning stabilization spwla 66 efficiency decision-making real time system spwla-2025-0018 buildup stabilization time...

.... The predictive accuracy of the model will increase through feedback iteration because the discriminator assesses the authenticity of the generated data. The results of the study show that well logs generated by the GAN have high accuracy compared to traditional machine learning methods, even in complex...

... mineral sandstone log analysis machine learning exposure reservoir characterization annual logging symposium brine interaction dindoruk well logging core analysis climate change dissolution spwla 66th engineering berea sandstone spwla-2025-0025 co -saturated brine porosity logarithmic...

... characterization annual logging symposium spwla 66th fracture aperture electrical image interpretation well production conductive fracture artificial intelligence drilling data acquisition machine learning reservoir type fracture and vug proportion SPWLA 66th Annual Logging Symposium, May 17-21...

... better reservoir management. geology resistivity invasion permeability flow in porous media effective permeability saturation geologist machine learning annual logging symposium fluid dynamics invasion depth different time inversion spwla 66th permeability curve artificial...

... characterization moduli equation permeability clastic rock phyllosilicate log analysis geological subdiscipline estimation seismic data annual logging symposium geologist rock type well logging machine learning elastic moduli total porosity petrophysics effective elastic moduli hydrocarbon...

... logging log analysis sedimentary rock salt tectonics porosity machine learning total porosity geological subdiscipline spwla-2025-0039 resolution equation structural geology artificial intelligence reservoir geomechanics amplitude permeability carbonate impedance reservoir...

... on CT scanning technology, combined with artificial thresholding and deep machine learning methods, a rock skeleton-bitumen-pore interpretation segmentation technique was established for the first time, effectively establishing two types of digital core models: pore type and fracture type in fracture...

... carbonate reservoir complex reservoir dolomite machine learning spwla 66th dissolution pore structure artificial intelligence geological subdiscipline porosity micp data permeability prediction sedimentary geology core analysis reservoir characterization annual logging symposium spwla-2025...

... ABSTRACT Estimating permeability and reservoir characterization in heterogeneous reservoirs like the Mandapeta Formation in the Krishna Godavari (KG) Basin poses significant challenges due to limited core and NMR data. This study addresses these challenges using two machine-learning approaches...

... (Torres Caceres et al., 2022). While machine learning offers alternatives, supervised approaches demand large labeled datasets, and unsupervised clustering requires extensive post-processing, introducing potential errors (Acharya and Fabian, 2024b; Acharya et al., 2025). This study proposes a Siamese...

... extreme-value (GEV) CDF-PDF. Optimized fitting parameters from GEV, including location ( μ ), scale ( σ gev ), and weighting constraints, served as inputs for machine-learning methods to estimate permeability. Ridge regression, random forests, and artificial neural networks (ANNs) were applied...

... production stage platform that automatizes model parameter selection of complex petrophysical models as targets are drilled, solving for the full non-linear system of equations as customary in modern formation evaluation practice. Additionally, our platform also deploys pretrained Machine Learning models...

...-consuming and rely heavily on the expertise of analysts, which can lead to variability in results. Recent advances in AI have enabled deep learning models to automate corrosion detection. Al-Khalidi and Abdulsadda (2024) examine efficient machine learning techniques that lower operational costs. However...