Failure criterion effect on solid production prediction and selection of completion solution

Production of fines together with reservoir fluid is called solid production. It varies from a few grams or less per ton of reservoir fluid posing only minor problems, to catastrophic amount possibly leading to erosion and complete filling of the borehole. This paper assesses solid production potential in a carbonate gas reservoir located in the south of Iran. Petrophysical logs obtained from the vertical well were employed to construct mechanical earth model. Then, two failure criteria, i.e. Mohre Coulomb and Mogi-Coulomb,were used to investigate the potential of solid production of the well in the initial and depleted conditions of the reservoir. Using these two criteria, we estimated critical collapse pressure and compared them to the reservoir pressure. Solid production occurs if collapse pressure is greater than pore pressure. Results indicate that the two failure criteria show different estimations of solid production potential of the studied reservoir. Mohre Coulomb failure criterion estimated solid production in both initial and depleted conditions, where Mogi-Coulomb criterion predicted no solid production in the initial condition of reservoir. Based on Mogi-Coulomb criterion, the well may not require completion solutions like perforated liner, until at least 60% of reservoir pressure was depleted which leads to decrease in operation cost and time.

Prediction of new perforation intervals in a depleted reservoir to achieve the maximum productivity: A case study of PNN logging in a cased-well of an Iranian oil reservoir

Pulsed neutron-neutron(PNN)logging is based on emitting neutrons into the near-wellbore zone and computing the neutron count decay due to scattering and capturing.The main application of this logging tool is to determine the current oil saturation and to detect channeling in perforated and non-perforated intervals behind the casing.Correct interpretation of the results obtained from PNN logging enables engineers to predict new perforation intervals in depleted reservoirs.This study examines the application of PNN logging in a well located in one of Iranian oil reservoirs.The interpretation procedure is described step by step.The principle of the PNN logging and the specifications of the tool are discussed and the applications of PNN logging in evaluation of oil saturation,identification of water flooded zones and prediction of potential perforating zones are described.Channeling is also investigated between all layers,good and poor oil zones are characterized based on the calculated oil saturations and new perforation intervals are suggested with the aim to boost oil production from the reservoir.The results of this study show that zones 1 to 5 having low oil saturations,are interpreted as depleted oil zones.Zones 6 to 8 are interpreted as good oil zones having high potential to produce oil.Zone 9 is interpreted as a water zone.

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.