The instant global trend towards developing tight reservoir is great; however, development can be very challenging due to stress and geomechanical properties effect in horizontal well placement and hydraulic fracturing design. Many parameters are known to be important to determine the suitable layer for locating horizontal well such as petrophysical and geomechanical properties. In the present study, permeability sensitivity to stress is also considered in the best layer selection for well placement. The permeability sensitivity to the stress of the layers was investigated using measurements of 27 core sample at different confining stress values. 1-D mechanical earth model (MEM) was built and converted to a 3-D full-field geomechanical model to reach perfect layer choice. The analysis of results has diagnosed the maximum horizontal stress direction of NE-SW as determined using both Fullbore Formation Micro Imager FMI and sonic scanner anisotropy analysis. The effect of porosity and permeability compaction as a result of stress changes while reservoir depletion is including on the reservoir simulation model. The choice of best layer and optimum design criteria for hydraulic fracturing is done in the current study using a compaction simulation model with the results of available measurements of geomechanical properties. The results of the simulation model show that the formation sensitivity to stress is an important factor for detecting a suitable layer for horizontal wells placement. The results of MEM indicate that horizontal stress difference (Δσ) and unconfined compressive strength (UCS) are the most important factors among geomechanical parameters affected the layer selection. From simulation results, it was found that 225 to 275 m fracture half-length gives a higher increment in oil production. The optimum number of fracture stages is noticed to be 8 to 10 stages after which the increment in production will reduce.
The unconventional techniques called “the quick look techniques”, have been developed to present well log data calculations, so that they may be scanned easily to identify the zones that warrant a more detailed analysis, these techniques have been generated by service companies at the well site which are among the useful, they provide the elements of information needed for making decisions quickly when time is of essence. The techniques used in this paper are:
- Apparent resistivity Rwa
- Rxo /Rt
The above two methods had been used to evaluate Nasiriyah oil field formations (well-NS-3) to discover the hydrocarbon bearing formations. A compu
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In this work, a deep computational study has been conducted to assign several qualities for the graph . Furthermore, determine the amount of the dihedral subgroups in the Held simple group He through utilizing the attributes of gamma.
The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).
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