Geomorphic indices and relative tectonic uplift in the Guerrero sector of the Mexican forearc

Tectonically active areas,such as forearc regions,commonly show contrasting relief,differential tectonic uplift,variations in erosion rates,in river incision,and in channel gradient produced by ongoing tectonic deformation.Thus,information on the tectonic activity of a defined area could be derived via landscape analysis.This study uses topography and geomorphic indices to extract signals of ongoing tectonic deformation along the Mexican subduction forearc within the Guerrero sector.For this purpose,we use field data,topographical data,knickpoints,the ratio of volume to area（Rva）.the stream-length gradient index（St）,and the normalized channel steepness index（k_（sn））.The results of the applied landscape analysis reveal considerable variations in relief,topography and geomorphic indices values along the Guerrero sector of the Mexican subduction zone.We argue that the reported differences are indicative of tectonic deformation and of variations in relative tectonic uplift along the studied forearc.A significant drop from central and eastern parts of the study area towards the west in values of R_（VA）（from ~500 to^300）,St（from ~500 to ca.400）,maximum St（from ~1500-2500 to ~ 1000） and k_（sn）（from ~150 to ~100） denotes a decrease in relative tectonic uplift in the same direction.We suggest that applied geomorphic indices values and forearc topography are independent of climate and lithology.Actual mechanisms responsible for the observed variations and inferred changes in relative forearc tectonic uplift call for further studies that explain the physical processes that control the forearc along strike uplift variations and that determine the rates of uplift.The proposed methodology and results obtained through this study could prove useful to scientists who study the geomorphology of forearc regions and active subduction zones.

Fracture permeability estimation utilizing conventional well logs and flow zone indicator

Characteristics of the natural open fractures on the oil and gas reservoirs is crucial in drilling and production planning. Direct methods of fractures studies such as core analysis and image log interpretation are usually not performed in all drilled wells in a field. Therefore, in absence of these data, the indirect methods can play an important role. In this study, an integrated algorithm is introduced to identify the fractures and estimate its permeability employing conventional well logs. First, open fractures were identified and their properties including density, aperture, porosity and permeability were estimated using FMI log. Subsequently, the fracture index log (FR_Index) was estimated utilizing conventional logs including density, micro-resistivity, sonic (compressional, shear and stoneley slownesses), and caliper logs. After that, the fracture index permeability was estimated by improving the FZI permeability equation. The coherence coefficient between two estimated fracture permeability logs is 0.66. A good correlation is observed on the high permeability zones, but the lower correlation on the low permeability zones. It is notified that, in the high fracture permeability zones, the conventional logs are heavily impacted by fracture permeability. However, due to lower vertical resolution of conventional logs compared with the image logs, the conventional logs are less influenced by less dense fracture zones. However, this algorithm can be used with acceptable accuracy in all uncored and image log wells.

A comparative study of 3D FZI and electrofacies modeling using seismic attribute analysis and neural network technique:A case study of Cheshmeh-Khosh Oil field in Iran

Electrofacies are used to determine reservoir rock properties,especially permeability,to simulate fluid flow in porous media.These are determined based on classification of similar logs among different groups of logging data.Data classification is accomplished by different statistical analysis such as principal component analysis,cluster analysis and differential analysis.The aim of this study is to predict 3D FZI(flow zone index)and Electrofacies(EFACT)volumes from a large volume of 3D seismic data.This study is divided into two parts.In the first part of the study,in order to make the EFACT model,nuclear magnetic resonance(NMR)log parameters were employed for developing an Electrofacies diagram based on pore size distribution and porosity variations.Then,a graph-based clustering method,known as multi resolution graph-based clustering(MRGC),was employed to classify and obtain the optimum number of Electrofacies.Seismic attribute analysis was then applied to model each relaxation group in order to build the initial 3D model which was used to reach the final model by applying Probabilistic Neural Network(PNN).In the second part of the study,the FZI 3D model was created by multi attributes technique.Then,this model was improved by three different artificial intelligence systems including PNN,multilayer feed-forward network(MLFN)and radial basis function network(RBFN).Finally,models of FZI and EFACT were compared.Results obtained from this study revealed that the two models are in good agreement and PNN method is successful in modeling FZI and EFACT from 3D seismic data for which no Stoneley data or NMR log data are available.Moreover,they may be used to detect hydrocarbon-bearing zones and locate the exact place for producing wells for the future development plans.In addition,the result provides a geologically realistic spatial FZI and reservoir facies distribution which helps to understand the subsurface reservoirs heterogeneities in the study area.