Summary The application of horizontal-well drilling and multistage fracturing has become a norm in the industry to develop unconventional resources from ultratight formations. A complex fracture network generated in the presence of stress isotropy and pre-existing natural fractures immensely extends reservoir contact and improves hydrocarbon production. A semianalytical method is presented in this paper to simulate the production from such a complex fracture network. This method combines an analytical reservoir solution with a numerical solution on discretized fracture panels. The mathematics is briefly introduced. Numerous case studies are presented, from a simple planar fracture to a real-field example from the Barnett shale. Production behavior and the key flow regimes are discussed. With its simplicity, yet capturing the physics of the transient-production performance, this approach provides an accessible tool for people from multiple disciplines in unconventional-resource development to rapidly evaluate treated-well productivity and stimulation effectiveness.

HIGHLIGHTS • Stereolithographic 3D printing was introduced to fabricate polymeric lattices. • Electroless plating was employed to make the lattices conductive and mechanically robust. • Hydrogen bubbles were served as template for engineering the hierarchically porous graphene. • The supercapacitor device holds great promise in the rational coupling of energy and power density. ABSTRACT 3D printing-based supercapacitors have been extensively explored, yet the rigid rheological requirement for corresponding ink preparation significantly limits the manufacturing of true 3D architecture in achieving superior energy storage. We proposed the stereolithographic technique to fabricate the metallic composite lattices with octet-truss arrangement by using electroless plating and engineering the 3D hierarchically porous graphene onto the scaffolds to build the hierarchically cellular lattices in quasi-solid supercapacitor application. The supercapacitor device that is composed of composite lattices span several pore size orders from nm to mm holds promising behavior on the areal capacitance (57.75 mF cm −2), rate capability (70% retention, 2-40 mA cm −2), and long lifespan (96% after 5000 cycles), as well as superior energy density of 0.008 mWh cm −2 , which are comparable to the state-of-the-art carbon-based supercapacitor. By synergistically combining this facile stereolithographic 3D printing technology with the hierarchically porous graphene architecture, we provide a novel route of manufacturing energy storage device as well as new insight into building other high-performance functional electronics.

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A highly sensitive portable piezoresistive sensor with a fast response time in an extended linear working range is urgently needed to meet the rapid development of artificial intelligence, interactive human−machine interfaces, and ubiquitous flexible electronics. However, it is a challenge to rationally couple these figures of merit (sensitivity, response time, and working range) together as they typically show functionally correlative behavior in the sensor. Here, we aim at introducing the hierarchical pores across several size orders from micro-to larger scale into the intrinsically flexible graphene-based electrode materials that overcome this limitation of the sensor. We achieved a flexible sensor with a prominent sensitivity of 11.9 kPa −1 in the linear range of 3 Pa to ∼21 kPa and a rapid response time of 20 ms to positively monitor the pulse rate, voice recognition, and true force value for biomedical and interactive human−machine interface application assisted by an analog-digital converter. More interesting is the carbon-nanotube-doped graphene that also served as the electrode in the waterproof supercapacitor to actively drive the sensor as a whole flexible system. We believe our findings not only offer a general strategy for the graphene-based platform in flexible electronics but also possess other intriguing potential in functional application such as the heat dissipation component in electron devices or seawater filtration in environment application.

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Forecasting of production in unconventional prospects has gained a lot of attention in the recent years. The key challenges in unconventional reservoirs have been the requirement to put online a) a large number of wells in a short period of time, b) well productivity significantly driven by completion characteristics and that c) the physics of fluid flow in these prospects still remain uncertain. In this paper, machine learning algorithms are used to forecast production for existing and new wells in unconventional assets using inputs like geological maps, production history, pressure data and operational constraints. One of the most popular Machine Learning methods – Artificial Neural Network (ANN) is employed for this purpose. ANN can learn from large volume of data points without assuming a predetermined model and can adapt to newer data as and when it becomes available. The workflow involves using these data sets to train and optimize the ANN model which, subsequently, is used to predict the well production performance of both existing wells using their own history and new wells by using the history of nearby wells which were drilled in analogous geological locations. The proposed technique requires users to do less data conditioning and model building and focus more on analyzing what-if scenarios and determining the well performance.

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