Hydraulic fracturing is used extensively to increase hydrocarbon production from oil and gas formations. Hydraulic fracture conductivity is a key parameter in optimizing the productivity of a well after the fracture treatment. The American Petroleum Institute (API) proppant permeability / fracture conductivity testing results are frequently used in industry fracturing models when selecting the proppant that provides the optimum fracture conductivity for a well's particular reservoir properties. This design methodology invariably results in a lower than expected fracture conductivity and in many cases, lower than optimum well performance. The industry has recognized that actual fracture conductivity is often a small fraction of what would be expected by using API test results. Non-Darcy flow, multiphase flow, gel damage, stress cycling, fines migration, proppant embedment, proppant flowback, and fracture cleanup are some of the parameters that result in fracture conductivities significantly lower than those measured in an API conductivity test. A new proppant was developed to improve the final fracture conductivity achievable with high-strength spherical proppants currently available in the market place. This new product is an elongated rod-shaped, high-strength particle with integrated proppant flow back control. Initial field testing of the product was conducted in moderate permeability formations where production from prior fracture treatments indicated lower than optimum fracture conductivity. Production results from these field tests confirmed that substantial increases in fracture conductivity can be achieved. The large improvement seen in fracture conductivity can be attributed to increased porosity of the proppant pack and reduced fracture conductivity losses due to non-Darcy and multiphase flow effects. Completely changing the typical geometry of proppants used in hydraulic fracturing is a viable option for improving the conductivity of hydraulic fractures to a point not currently obtainable with spherical proppants.
Production from shale gas reservoirs depends greatly on the efficiency of hydraulic fracturing treatments. The cumulated experience in the industry has led to several best practices in treatment design, which have improved productivity of these reservoirs. However, further advancement of treatment design requires a deeper understanding of the complex physics involved in both hydraulic fracturing and production, such as stress shadow, proppant placement and treatment interaction with pre-existing natural fractures. This paper sheds light on the non-linear physics involved in the production of shale gas reservoirs by improving the understanding of the complex relation between gas production, the reservoir properties, and several treatment design parameters. A fracturing-to-production simulation workflow integrating the Unconventional Fracture Model (Weng et al., 2011), with the Unconventional Production Model (Cohen et al., 2012) is presented. By applying this workflow to a realistic reservoir, we did an extensive parametric study to investigate the relation between production and treatment design parameters such as fracturing fluid viscosity, proppant size, proppant concentration, proppant injection order, treatment volume, pumping rate, pad size and hybrid treatment. The paper also evaluates the influence of unconventional reservoir properties -such as permeability, horizontal stress, horizontal stress anisotropy, horizontal stress orientation, Poisson's ratio and Young's modulus -on production. Since this paper focuses on fluid and proppant selection, our methodology was to run 28 simulations to cover the 2D parametric space of proppant size and fracturing fluid viscosity for all of these parameters. More than fourteen hundred simulations were run in this parametric study and the results provide guidelines for optimized treatment design. This paper illustrates how this unique workflow can identifies the optimum fluid and proppant selection that gives the maximum production for a given reservoir and completion. In addition, the parametric study shows how these optimums evolve as a function of reservoir and treatment parameters. The results validate several best practices in treatment design for shale. For example, combination of different sizes of proppant optimizes production by maximizing initial production and slowing down production decline. Simulations also confirm the best practice of injecting the smallest proppant first. The study explains why slickwater treatments should be injected at maximum pumping rate and preferably with 40/70 mesh sand. It also illustrates why reservoirs with high Young's modulus (such as the Barnett shale) can be stimulated effectively with slickwater. Another key finding is that the optimum fluid viscosity increases with treatment volume.
Production from shale gas reservoirs depends greatly on the efficiency of hydraulic fracturing treatments. The cumulated experience in the industry has led to several best practices in treatment design which have improved productivity in these reservoirs. However, further advances in treatment design require a deeper understanding of the complex physics involved in both hydraulic fracturing and production, such as stress shadow, proppant placement and interaction with natural fractures. This paper investigates the non-linear physics involved in the production of shale gas reservoirs by improving the understanding of the complex relation between gas production, the reservoir properties, and several treatment design parameters, with a focus on proppant and fluid selection. A fracturing-to-production simulation workflow integrating the Unconventional Fracture Model, with the Unconventional Production Model is presented. This workflow has shown qualitative consistency with real production data. In this paper we applied the workflow on a realistic reservoir with characteristics from the Marcellus play, and then studied the relation between production and treatment design parameters such as proppant size, proppant concentration, the treatment volume of the treatment, fracturing fluid viscosity, pumping rate and proppant injection sequence. Since this paper focuses on fluid and proppant selection, our methodology was to run 28 simulations to cover the 2D parametric space of proppant size and fluid viscosity for every parameter. More than four hundred simulations were run in this parametric study and the results provide guidelines for optimized treatment design. The behaviors observed confirm several best practices in treatment design for shale. For example, combination of different sizes of proppant optimizes production by maximizing initial production and slowing down production decline. Simulations also confirm the best practice of injecting the smallest proppant first. Another key finding is that the optimum fluid viscosity increases with treatment volume, and decreases when pumping rate increases.
Synthesis of mullite from a mixture of a-Al 2 O 3 + b-SiO 2 and native kaolin-based materials in the presence of nanodisperse aluminum is reported. In both cases, addition of nanodisperse Al increases the yield of mullite and stabilizes its structure.Materials based on mullite (3Al 2 O 3 × 2SiO 2 ), owing to their unique mechanical and thermophysical properties, have been gaining ever-increasing acceptance in the production of refractories and engineering ceramics. Mullite may be used as a single basic phase or in combination with oxide [1] or metallic ingredients [2]. Nanodisperse metallic powders, owing to their high reactivity, can be used both as components of a metal ceramic composites and as an active mixture component involved in the solid-phase synthesis of new phases [3]. Heating mixes containing nanodisperse aluminum in an oxidizing medium initiates sequential reactions of which metal oxidation and mullitization are central.The synthesis of mullite containing nanodisperse aluminum was carried out by heating mixes of two composition: (i) based on oxides of aluminum and silicon and (ii) based on kaolinite with aluminum oxide added. Aluminum oxide was calcined vibroground alumina (a-Al 2 O 3 ) with average particles size of 5.6 mm; silicon dioxide was a finely ground sand (from the Tuganskoe deposit) treated using hydrocyclone and electroseparator techniques. The basic component was b-SiO 2 (up to 99.10%) and the minor components were iron oxide (0.04%) and aluminum oxide (0.46%); the average size of wet-ground particles was 10 mm. The major component of kaolin mixes was kaolin from the Zhuravlinyi Log deposit; the base material was ³ 97.5%, the total of minor oxides was £ 2.41%.The mullitization reaction was activated by adding ultradisperse aluminum with an average particle size of 80 nm. Aluminum was added at a concentration of 0.2 -5 wt.%. The proportion of components in the mixtures was in conformance with the mullite stoichiometry. The aluminum concentration was corrected for subsequent oxidation to Al 2 O 3 . The specimens were molded by semi-dry pressing under a load of 50 MPa and heated in air at a rate of 1.5 K/min to 1000°C; on reaching this temperature, the heating rate was increased to 200 K/h. The holding time at maximum temperature varied from 30 min to 5 h. The synthetic mullite phase was quantitated by an x-ray reflection peak (110) (interplanar spacing 0.540 nm).The synthesis of these two types of specimens and the involvement of nanodisperse aluminum exhibit specific features. The synthesis in kaolinite-containing mixtures is more energetically favorable in comparison to the pure-oxide process. Mullitization of kaolinite in the early stage involves the formation of a "primary" mullite with the onset at about 1200°C; at temperatures above 1300°C, a "secondary" mullite is formed. The rate of formation of mullite in mixtures containing natural kaolin at 1300°C is higher than that in oxide-containing mixtures (Fig. 1); for this reason, the activating effect due to the high-disperse additive ...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.