Predicted facies, sedimentary structures and potential resources of Jurassic petroleum complex in S-E Western Siberia (based on well logging data)

This paper is devoted to the current problem in petroleum geology and geophysics- prediction of facies sediments for further evaluation of productive layers. Applying the acoustic method and the characterizing sedimentary structure for each coastal-marine-delta type was determined. The summary of sedimentary structure characteristics and reservoir properties (porosity and permeability) of typical facies were described. Logging models SP, EL and GR (configuration, curve range) in interpreting geophysical data for each litho-facies were identified. According to geophysical characteristics these sediments can be classified as coastal-marine-delta. Prediction models for potential Jurassic oil-gas bearing complexes (horizon J[1]{1}) in one S-E Western Siberian deposit were conducted. Comparing forecasting to actual testing data of layer J[1]{1} showed that the prediction is about 85%

Solid-phase arsenic speciation in aquifer sediments: A micro-X-ray absorption spectroscopy approach for quantifying trace-level speciation

Arsenic (As) is a geogenic contaminant affecting groundwater in geologically diverse systems globally. Arsenic release from aquifer sediments to groundwater is favored when biogeochemical conditions, especially oxidation-reduction (redox) potential, in aquifers fluctuate. The specific objective of this research is to identify the solid-phase sources and geochemical mechanisms of release of As in aquifers of the Des Moines Lobe glacial advance. The overarching concept is that conditions present at the aquifer-aquitard interfaces promote a suite of geochemical reactions leading to mineral alteration and release of As to groundwater. A microprobe X-ray absorption spectroscopy (μXAS) approach is developed and applied to rotosonic drill core samples to identify the solid-phase speciation of As in aquifer, aquitard, and aquifer-aquitard interface sediments. This approach addresses the low solid-phase As concentrations, as well as the fine-scale physical and chemical heterogeneity of the sediments. The spectroscopy data are analyzed using novel cosine-distance and correlation-distance hierarchical clustering for Fe 1s and As 1s μXAS datasets. The solid-phase Fe and As speciation is then interpreted using sediment and well-water chemical data to propose solid-phase As reservoirs and release mechanisms. The results confirm that in two of the three locations studied, the glacial sediment forming the aquitard is the source of As to the aquifer sediments. The results are consistent with three different As release mechanisms: (1) desorption from Fe (oxyhydr)oxides, (2) reductive dissolution of Fe (oxyhydr)oxides, and (3) oxidative dissolution of Fe sulfides. The findings confirm that glacial sediments at the interface between aquifer and aquitard are geochemically active zones for As. The diversity of As release mechanisms is consistent with the geographic heterogeneity observed in the distribution of elevated-As wells

Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, anexample from a North American Shale Gas Reservoir

Estimation of reservoir parameters has always been a challenge for shale gas reservoirs. This study has concentrated on neural network technique and multiple regression analysis to predict reservoir properties including porosity, permeability, fluid saturation and total organic carbon content from conventional wireline log data for a large North American shale gas reservoir. More than 262 core analysis data from 3 wells were used as "target" and "response" for neural network and multiple regression analysis. Common log data available in three wells including GR, SP, RHOB, NPHI, DT and deep resistivity were used as "input" and "predictor".This study shows that reservoir parameters could be better estimated using the neural network technique than through multiple regression. The neural network method had a correlation coefficient greater than 80% for most of the parameters. Although providing a set of algorithms, multiple regression analysis was less successful for predicting reservoir parameters