An advanced workflow to compress the uncertainties of stochastic distribution of Bahariya reservoir properties using 3D static modeling:An example from Heba Oil Fields,Western Desert,Egypt

The main objective of this paper is to construct a static model that compress the uncertainties of the stochastic distribution of the reservoir properties of the Bahariya Formation in Heba field,at the northeastern portion of the Western Desert.This model has been constructed through the integration of the interpretations of the eighteen 2D seismic sections and the analysis of well logs data for four wells(HEBA 300X,E.BAH-E-1X,E.BAH-D-1X,and HEBA 10X)drilled in the study area.This set of data was implemented in a harmonic workflow.Structural framework was the first step created on the basis of the seismic and well log interpretations.Model zonation was mainly managed by the marine flooding events took place during the Cenomanian period.The trapping faults position uncertainty has been compressed through the tying of the seismic profiles with the identified fault cuts in the well data.Effective porosity spectrum was broke up into three reservoir qualities.The results showed heterogeneous facies qualities for oil production in specific five zones in the topmost part of the Bahariya Formation.The effective porosity model was generated stochastically considering the normal distribution for each reservoir quality.Water saturation was distributed by two methods;1)Sequential Gaussian Simulation that was co-simulated by porosity model.2)Log-based saturation height function for each reservoir quality.This methodology provided as accurate as possible estimates for the volume calculation by quantifying the sensitivity of the important parameters such as oil contact.Additionally,the model was prepared to be used as a front end for dynamic simulation.

3D static reservoir modelling of Abu Madi paleo-valley in Baltim Field,Offshore Nile Delta Basin,Egypt

The 3D static reservoir modelling provides a good understanding for the hydrocarbon reservoir distri-bution and properties.This study aimed to construct a static model to study the gas-reservoir potentiality of the Messinian Abu Madi Formation in Baltim Field,offshore Nile Delta Basin.This model was constructed based on the interpretation of the thirty-seven 2D seismic sections besides the analysis of well logs data for five drilled wells(Baltim East-2,Baltim East-3,Baltim East-5,Baltim North-1 and Baltim North-2).The seismic interpretation exposed that the Abu Madi incised valley in Baltim Field is oriented NNW-SSE with width ranges from 5 to 8 km and approximately 32 km in length.The reservoir modelling revealed a relatively homogeneous reservoir facies qualities for gas production in two different zones within the Abu Madi sandstones.The trapping mechanism for these intervals is represented by pinching out in the two sides of the Abu Madi paleo-valley and bounded by two faults in the northern and southern parts of the study area.

Prediction of fold-of-increase in productivity index post limited entry fracturing using artificial neural network

Unified Fracture Design(UFD)bridges the gap between practices and theory in the hydraulic fracturing industry.It represents a technique to design hydraulic fracturing treatment with a particular amount of proppant.This design could provide the maximum fold-of-increase(FOI)in productivity-index(PI)after hydraulic fracturing treatment.The UFD optimization tool is very effective,but it has assumptions like any other model.One assumption of UFD optimization technique;is a single-layer assumption.This assumption does not align with the limited entry fracturing design concept.In limited entry fracturing,the frictional pressure is employed to offset the stress differences between multi-layers reservoirs to attain fluid injection through these layers,intended to deliver an optimal fracture conductivity in all layers.The drawback of this assumption is the underestimation of the actual value of FOI in PI.This paper aims to recast the original unified fracture design approach to extend the optimal UFD to a multilayer reservoir to predict the FOI in PI after limited entry fracturing treatment.The recasting tool for this problem to find the optimum solution is Artificial Neural Networks(ANN).The architected ANN model is based on actual historical data of limited entry fracturing treatments.A statistical comparison between the proposed ANN model and classical UFD technique demonstrates that ANN model solution has a more reliable estimation of FOI in PI with the actual historical data.