Mechanisms of fracture complexity and topology of fracture systems induced by hydraulic fracturing

Stimulated reservoir volume (SRV) is a prime factor controlling well performance in unconventional shale plays. In general, SRV describes the topology of induced fractures by hydraulic fracturing. Natural fractures (NFs), such as joints and faults, are ubiquitous in oil and gas reservoirs, where their tectonics, diagenesis, and hydrocarbon-generation history make the rock prone to fracturing. Being a pre-existing weak interface, NFs are preferred failure paths during hydraulic fracturing and becoming conductive under shear slip. Therefore, the interaction of hydraulic fractures (HFs) and NFs is fundamental to fracture growth in a formation. However, field observations of induced fracture systems show the necessity of modeling fracture complexity for improving completion design and interpreting drained reservoir volume (DRV). Thus, this work explains the mechanisms of HF-NF interaction and provides a physics-based method to infer SRV. First, fracture complexity results from fracture-tip processes involving stress perturbation by HF and failure of the pre-existing weak interface. Such so-called HF-NF interactions enable permeability enhancement around the HF and the development of SRV within unconventional shale reservoirs. This work proposes a two-dimensional (2D) analytical workflow to delineate the potential slip zone (PSZ) induced by an HF. An explicit description of failure modes in the near-tip region explains the complexity involved in HF-NF interaction. The results show varying influences of HF-NF relative angle, stress state, net pressure, frictional coefficient, and HF length to the NF slip. An NF at a 30±5° relative angle to an HF is analytically proved to have the highest potential for reactivation, which dominantly depends on the frictional coefficient of the interface. The spatial extension of the PSZ normal to the HF converges as the fracture propagates away and exhibits asymmetry depending on the relative angle. The proposed concept of PSZ can be used to measure and compare the intensity of HF-NF interactions at various geological settings. Second, the intensity of HF-NF interaction has been found to vary by formation and shale play. The problem of HF-NF interaction is multivariant and nonlinear, requiring conditional screening among three failure modes. By considering realistic subsurface conditions, a machine-learning (ML) model (random forest [RF] regression) is built to replicate the physics-based model and statistically investigate parametric influences on NF slip. The ML model finds the statistical significance of predicting features to be in the order of relative angle between HF and NF, fracture gradient (FG), frictional coefficient of the NF, overpressure index, stress differential, formation depth, and net pressure. The ML result is compared with sensitivity analysis and provides a new perspective on HF-NF interaction using statistical measures. The importance of formation depth on HF-NF interaction is stressed in both the physics-based and data-driven models, thus providing insight for field development of stacked resource plays. Finally, previous fracturing models either reduce model flexibility in simulating complex HF-NF interaction or require great computation cost for discrete fracture growth. This work presents a finite discrete-element model, which is a hybrid model adopting numerical setups from both continuum and discontinuous approaches, to investigate multifracture propagation in fractured reservoirs. The numerical model captures the fracture complexity, including branched, stranded, and kinked fractures, as well as the offset crossing of NFs. The results show biased fracture growth in the fractured reservoir, which is different from the numerical results of multifracture propagation in homogeneous rocks.. This work also emphasizes the control of fluid partition at the wellbore and among the intersecting fractures. Fluid partition at the wellbore is found to be a major challenge to the completion design of tight cluster spacing, which has been shown to improve production in recent years.Petroleum and Geosystems Engineerin

Numerical investigation of lost circulation and fracture resistance enhancement mechanism

Drilling in complex geological settings often possesses significant risk for unplanned events that potentially intensify the economic problem of cost-demanding operations. Lost circulation, a major challenge in well construction operations, refers to the loss of drilling fluid into formation during drilling operations. Over years of research effort and field practices, wellbore strengthening techniques have been successfully applied in the field to mitigate lost circulation and have proved effective in extending the drilling mud weight margin to access undrillable formations. In fact, wellbore strengthening contributes additional resistance to fractures so that an equivalent circulating density higher than the conventionally estimated fracture gradient can be exerted on the wellbore. Therefore, wellbore strengthening techniques artificially elevate the upper limit of the mud weight window. Wellbore strengthening techniques have seen profound advancement in the last 20 years. Several proposed wellbore strengthening models have contributed considerable knowledge for the drilling community to mitigate lost circulation. However, in each of these models, wellbore strengthening is uniquely explained as a different concept, with supporting mathematical models, experimental validation, and field best practices. Due to simplifications of the mathematical models, the limited scale of experiments, and insufficient validation of field observations, investigating the fundamental mechanisms of wellbore strengthening has been an active and controversial topic within the industry. Nevertheless, lost circulation is undoubtedly induced by tensile failure or reopening of natural fractures when excessive wellbore pressure appears. In this thesis, a fully coupled hydraulic fracturing model is developed using Abaqus Standard. By implementing this numerical model, an extensive parametric study on lost circulation is performed to investigate mechanical behaviors of the wellbore and the induced fracture under various rock properties and bottomhole conditions. Based on the fracture analysis, a novel approach to simulate the fracture sealing effect of wellbore strengthening is developed, along with a workflow quantifying fracture gradient extension for drilling operations. A case study on fracture sealing is performed to investigate the role of sealing permeability and sealing length. The results described in this thesis indicate the feasibility of hoop stress enhancement, detail the mechanism of fracture resistance enhancement, and provide insights for lost circulation mitigation and wellbore strengthening treatment.Petroleum and Geosystems Engineerin

MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation

We introduce MVControl, a novel neural network architecture that enhances existing pre-trained multi-view 2D diffusion models by incorporating additional input conditions, e.g. edge maps. Our approach enables the generation of controllable multi-view images and view-consistent 3D content. To achieve controllable multi-view image generation, we leverage MVDream as our base model, and train a new neural network module as additional plugin for end-to-end task-specific condition learning. To precisely control the shapes and views of generated images, we innovatively propose a new conditioning mechanism that predicts an embedding encapsulating the input spatial and view conditions, which is then injected to the network globally. Once MVControl is trained, score-distillation (SDS) loss based optimization can be performed to generate 3D content, in which process we propose to use a hybrid diffusion prior. The hybrid prior relies on a pre-trained Stable-Diffusion network and our trained MVControl for additional guidance. Extensive experiments demonstrate that our method achieves robust generalization and enables the controllable generation of high-quality 3D content. Code available at https://github.com/WU-CVGL/MVControl/.Comment: Project page: https://lizhiqi49.github.io/MVControl

BALF: Simple and Efficient Blur Aware Local Feature Detector

Local feature detection is a key ingredient of many image processing and computer vision applications, such as visual odometry and localization. Most existing algorithms focus on feature detection from a sharp image. They would thus have degraded performance once the image is blurred, which could happen easily under low-lighting conditions. To address this issue, we propose a simple yet both efficient and effective keypoint detection method that is able to accurately localize the salient keypoints in a blurred image. Our method takes advantages of a novel multi-layer perceptron (MLP) based architecture that significantly improve the detection repeatability for a blurred image. The network is also light-weight and able to run in real-time, which enables its deployment for time-constrained applications. Extensive experimental results demonstrate that our detector is able to improve the detection repeatability with blurred images, while keeping comparable performance as existing state-of-the-art detectors for sharp images

USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields

Neural Radiance Fields (NeRF) has received much attention recently due to its impressive capability to represent 3D scene and synthesize novel view images. Existing works usually assume that the input images are captured by a global shutter camera. Thus, rolling shutter (RS) images cannot be trivially applied to an off-the-shelf NeRF algorithm for novel view synthesis. Rolling shutter effect would also affect the accuracy of the camera pose estimation (e.g. via COLMAP), which further prevents the success of NeRF algorithm with RS images. In this paper, we propose Unrolling Shutter Bundle Adjusted Neural Radiance Fields (USB-NeRF). USB-NeRF is able to correct rolling shutter distortions and recover accurate camera motion trajectory simultaneously under the framework of NeRF, by modeling the physical image formation process of a RS camera. Experimental results demonstrate that USB-NeRF achieves better performance compared to prior works, in terms of RS effect removal, novel view image synthesis as well as camera motion estimation. Furthermore, our algorithm can also be used to recover high-fidelity high frame-rate global shutter video from a sequence of RS images

An Augmented Discrete-Time Approach for Human-Robot Collaboration

Human-robot collaboration (HRC) is a key feature to distinguish the new generation of robots from conventional robots. Relevant HRC topics have been extensively investigated recently in academic institutes and companies to improve human and robot interactive performance. Generally, human motor control regulates human motion adaptively to the external environment with safety, compliance, stability, and efficiency. Inspired by this, we propose an augmented approach to make a robot understand human motion behaviors based on human kinematics and human postural impedance adaptation. Human kinematics is identified by geometry kinematics approach to map human arm configuration as well as stiffness index controlled by hand gesture to anthropomorphic arm. While human arm postural stiffness is estimated and calibrated within robot empirical stability region, human motion is captured by employing a geometry vector approach based on Kinect. A biomimetic controller in discrete-time is employed to make Baxter robot arm imitate human arm behaviors based on Baxter robot dynamics. An object moving task is implemented to validate the performance of proposed methods based on Baxter robot simulator. Results show that the proposed approach to HRC is intuitive, stable, efficient, and compliant, which may have various applications in human-robot collaboration scenarios

T\u3cem\u3ecf\u3c/em\u3e21 Marks Visceral Adipose Mesenchymal Progenitors and Functions as a Rate-Limiting Factor During Visceral Adipose Tissue Development

Distinct locations of different white adipose depots suggest anatomy-specific developmental regulation, a relatively understudied concept. Here, we report a population of Tcf21 lineage cells (Tcf21 LCs) present exclusively in visceral adipose tissue (VAT) that dynamically contributes to VAT development and expansion. During development, the Tcf21 lineage gives rise to adipocytes. In adult mice, Tcf21 LCs transform into a fibrotic or quiescent state. Multiomics analyses show consistent gene expression and chromatin accessibility changes in Tcf21 LC, based on which we constructed a gene-regulatory network governing Tcf21 LC activities. Furthermore, single-cell RNA sequencing (scRNA-seq) identifies the heterogeneity of Tcf21 LCs. Loss of Tcf21 promotes the adipogenesis and developmental progress of Tcf21 LCs, leading to improved metabolic health in the context of diet-induced obesity. Mechanistic studies show that the inhibitory effect of Tcf21 on adipogenesis is at least partially mediated via Dlk1 expression accentuation

Progenitor Cell Isolation From Mouse Epididymal Adipose Tissue and Sequencing Library Construction

Here, we present a protocol to isolate progenitor cells from mouse epididymal visceral adipose tissue and construct bulk RNA and assay for transposase-accessible chromatin with sequencing (ATAC-seq) libraries. We describe steps for adipose tissue collection, cell isolation, and cell staining and sorting. We then detail procedures for both ATAC-seq and RNA sequencing library construction. This protocol can also be applied to other tissues and cell types directly or with minor modifications. For complete details on the use and execution of this protocol, please refer to Liu et al. (2023).1 *1 Liu, Q., Li, C., Deng, B., Gao, P., Wang, L., Li, Y., ... & Fu, X. (2023). Tcf21 marks visceral adipose mesenchymal progenitors and functions as a rate-limiting factor during visceral adipose tissue development. Cell reports, 42(3) 112166. https://doi.org/10.1016/j.celrep.2023.11216

Photoinduced Charge Transfer and Trapping on Single Gold Metal Nanoparticles on TiO2

We present a study of the effect of gold nanoparticles (Au NPs) on TiO2 on charge generation and trapping during illumination with photons of energy larger than the substrate band gap. We used a novel characterization technique, photoassisted Kelvin probe force microscopy, to study the process at the single Au NP level. We found that the photoinduced electron transfer from TiO2 to the Au NP increases logarithmically with light intensity due to the combined contribution of electron-hole pair generation in the space charge region in the TiO2-air interface and in the metal-semiconductor junction. Our measurements on single particles provide direct evidence for electron trapping that hinders electron-hole recombination, a key factor in the enhancement of photo(electro)catalytic activity.This work was supported by the Office of Basic Energy Sciences (BES) of the U.S. Department of Energy (DOE) under contract DE-AC02-05CH11231 through the Structure and Dynamics of Materials Interfaces Program (FWP KC31SM) and the Molecular Foundry. M.L. acknowledges funds from Comunidad de Madrid (P2018/EMT-4308), a Fulbright grant PRX16/00564, and the MCIU-AEI-FEDERUE (RTI2018-096937-B-C22 and MAT2014-59772-C2-1-P). J.C. acknowledges financial support from Ministerio de Ciencia e Innovación (MICINN) and the European Union through the project PID2019-104272RB-C52. Also, Y.H. acknowledges financial support from MCIU through MAT2014-59772-C2-2- P and L.M. from EC through ERC-2013-SYG-610256. V.A.P.O. and M.B. acknowledge the financial support from EC through ERC CoG HyMAP 648319, MINECO PID2019- 106315RB-I00 and ENE2017-89170-R, ″Comunidad de Madrid″ and European Structural Funds (FotoArt-CM project S2018/NMT-4367) and Fundación Ramón Areces (Art-Leaf project). M.B. also thanks the Juan de la Cierva Incorporación contract (IJC2019042430-I). X.Z. was supported by the NSF-BSF 359 grant number 1906014. The authors thank Prof. Eran Edri, María Ujué González Sagardoy, and Judit Meseguer- Oliver for fruitful discussions and Asylum customer support for help with modifications of the AFM

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan