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In this paper, taking Block G in Canada as an example, combined with the data of the working area, the Pearson–MIC comprehensive evaluation method was adopted to optimize the key parameters of productivity. Based on the analytic hierarchy process, the weight of each parameter was calculated, the grade of evaluation index of the “sweet spot” was divided, the standard of the sweet spot was established, and the distribution of the superimposed sweet spot was finally depicted. The results show that lateral length, number of stages, volume of fluid, and amount of proppant are the key engineering parameters of horizontal well, and lateral length is an independent key engineering parameter. The cumulative gas production in the first two years was normalized on the lateral length to eliminate the engineering influence, and the total organic carbon (TOC) was finally determined as the key geological parameter, whereas porosity and water saturation were the secondary key parameters. The area of Type I sweet spots accounts for 24.2% in the Series Upper and 23.1% in the Series Lower. This study proposed a new sweet spot prediction idea based on the influence of geological factors on productivity, and its results also laid a foundation for the subsequent placement of horizontal wells in Block G.

Interest has spread to potential unconventional shale reservoirs in the last decades, and they have become an increasingly important source of hydrocarbon. Importantly, pore structure of shale has considerable effects on the storage, seepage and output of the fluids in shale reservoirs so that reliable fractal characteristics are essential. To better understand the evolution characteristics of pore structure for a shale gas condensate reservoir and their influence on liquid hydrocarbon occurrences and reservoir physical properties, we conducted high-pressure mercury intrusion tests (HPMIs), field emission scanning electron microscopies (FESEM), total organic carbon (TOC), Rock-Eval pyrolysis and saturation measurements on samples from the Duvernay formation. Furthermore, the fractal theory is applied to calculate the fractal dimension of the capillary pressure curves, and three fractal dimensions D1, D2 and D3 are obtained. The relationships among the characteristics of the Duvernay shale (TOC, organic matter maturity, fluid saturation), the pore structure parameters (permeability, porosity, median pore size), and the fractal dimensions were investigated. The results show that the fractal dimension D1 ranges from 2.44 to 2.85, D2 ranges from 2.09 to 2.15 and D3 ranges from 2.35 to 2.48. D2 and D3 have a good positive correlation. The pore system studied mainly consists of organic pores and microfractures, with the percentage of micropores being 50.38%. TOC has a positive relationship with porosity and D3 due to the development of organic pores. D3 has a positive correlation with gas saturation. With increased D3, median pore size shows a decreasing trend and an increase in permeability and porosity, demonstrating that D3 has a large effect on pore size distribution and the heterogeneity of pore size. In general, D3 has a better correlation with petrophysical and petrochemical parameters. Fractal theory can be applied to better understand the pore evolution, pore size distribution and fluid storage capacity of shale reservoirs.

Combined with micro-seismic exploration results, an unsteady production model for multi-branched fractures in bounded tight fractured reservoirs has been established in this paper, and by means of Laplace transform, point source integration, Stehfest numerical inversion, and semi-analytical solutions to the production model have been obtained. The accuracy of model simulation has been verified through the fitting analysis of the theoretical model and field production data. The influences of fracture branch number, inter-porosity flow coefficient, storage coefficient, branched fracture density, and drainage area on production have been analyzed. According to the calculation results, the larger the fracture branch number, the higher the decline rate in the early stage; in addition, the dimensionless production curves in the late stage coincided with each other; basically, inter-porosity flow coefficient affects the productivity of only the inter-porosity flow interval; the greater the inter-porosity flow coefficient, the higher the production; storage coefficient mainly affects the early productivity of volume-fractured wells; the greater the storage coefficient, the higher the early production, but the productivity decline rate is similar at different storage coefficients; fracture density mainly affects the early productivity of volume-fractured wells; the larger the fracture density, the smaller the productivity, but with the reduction of fracture density, the influence of fracture density on productivity is less obvious; a larger reservoir drainage radius leads to later entering of energy depletion stage. Much attention shall be paid to the optimization of productivity influence factors, and they shall be taken into account as comprehensively as possible in the actual production process.