... on the reservoir parameters like horizontal permeability, vertical permeability, skin and boundary distances, which need to be refined to achieve a pressure history match. To do that we introduce an inversion approach, where simultaneous inversion of all these reservoir parameters take place in an iterative manner...

... engineering, and are efficient in calculation, we still choose to use machine learning techniques (Supported Vector Machine (SVM) plus kernel method) to identify reservoir model of pressure transient data. We further used the selected reservoir models to derive the initial values of reservoir parameters...

... analysis is proven to be ineffective due to the long waiting time before the results are available. Full scale DST on the other hands offers higher operational complexity and cost for reservoir parameters determination. As an alternative, the wireline formation tester dual packer module was deployed...

... methods. Perm-based guidelines for CBM well completions are given, which include permeability bands and benchmarks. Industry success has led to two benchmarks for evaluating ahead of full-field development (1) commercial success from reservoir parameters, and (2) effectiveness of well completions...

.... The first part of the paper discusses the basis of parameter transformations and gives suitable transform pairs for common reservoir parameters. We analyzed the homogeneous probability distribution of transformed parameters using Monte Carlo analysis and verified that the transform pairs proposed generate...

... statistical study was conducted to determine the effects of various reservoir and operational parameters on the performance of waterfloods in these reservoirs. In this study, a database of 120 operational and reservoir parameters for 177 waterfloods in Alberta and Saskatchewan was developed and analyzed...

... between seismic attributes and reservoir parameters are not always clear, statistical relationships should be preferred than deterministic approaches. This technique performs a statistical relationship between well data (porosity) and seismic data (attributes) then a calibration process and a residual...

... & gas information drillstem/well testing neural network interpretation expert system application machine learning reservoir parameter artificial intelligence type curve selection equation southern california ershaghi juniardi drillstem testing model 7 reservoir united states...

... and evaluate different projects quantitatively. Normalized performance histories for various projects in the same field were similar, indicating that histories of previous projects can serve as a preliminary predictive tool for future projects. The normalized curves were correlated with reservoir parameters...

... the reservoir parameters are otherwise difficult or impossible to obtain. Well tests on naturally fractured reservoirs are not very easy to analyze. However, in the rare case when two or three clear semilog straight lines are present, analysis is straightforward. More often, because of the wellbore storage...