Implementation and application of fracture diagnostic tools : fiber optic sensing and diagnostic fracture injection test (DFIT)

Shale reservoirs have drawn much attention in recent years in the oil and gas industry. Hydraulic fracturing is a key technology to extract the trapped hydrocarbon in the shale reservoirs. The complex hydraulic and natural fracture networks enable large contact area between fracture and low-permeability reservoir to enhance the production. The characterization of complex fracture geometry and evaluation of fracture properties are crucial to the fracturing operation design and fractured reservoir simulation. The main approach to a better understanding of fracture and shale reservoir matrix is fracture diagnosis. There are mainly five fracture diagnostic technologies: Distributed Temperature Sensing (DTS), Distributed Acoustic Sensing (DAS), Diagnostic Fracture Injection Test (DFIT), microseismic, and tracer flow-back test. In this study, we mainly focus on the data interpretation model of DTS and DFIT. The current interpretation of DTS data is mostly limited to the qualitative analysis. To enable the quantitative interpretation of DTS data, an in-house comprehensive model is developed to evaluate the fracture properties and geometry. Our model couples fracture, wellbore, and reservoir domain together to capture the full physical process during the production stage. The effects of reservoir parameters, fracture parameters, and fracture geometries on temperature profiling along the wellbore are analyzed with our model. Our forward model could be potentially used to characterize fracture parameters or fracture geometry with history matching. DFIT is consisted of before closure analysis and after closure analysis. The leak-off coefficient, injection efficiency, reservoir matrix permeability, and initial pore pressure can be obtained from DFIT data analysis. In this study, several models for DFIT data interpretation were integrated. A Marcellus shale gas DFIT data is successfully analyzed with our workflow.Petroleum and Geosystems Engineerin

Machine Learning Based Real-Time Quantification of Production from Individual Clusters in Shale Wells

Over the last two decades, there has been advances in downhole monitoring in oil and gas wells with the use of Fiber-Optic sensing technology such as the Distributed Temperature Sensing (DTS). Unlike a conventional production log that provides only snapshots of the well performance, DTS provides continuous temperature measurements along the entire wellbore. Whether by fluid extraction or injection, oil and gas production changes reservoir conditions, and continuous monitoring of downhole conditions is highly desirable. This research study presents a tool for real-time quantification of production from individual perforation clusters in a multi-stage shale well using Artificial Intelligence and Machine Learning. The technique presented provides continuous production log on demand thereby providing opportunities for the optimization of completions design and hydraulic fracture treatments of future planned wells. A Fiber-Optic sensing enabled horizontal well MIP-3H in the Marcellus Shale has been selected for this work. MIP-3H is a 28-stage horizontal well drilled in July 2015, as part of a Department of Energy (DOE)-sponsored project - Marcellus Shale Energy & Environment Laboratory (MSEEL). A one-day conventional production logging operation has been performed on MIP-3H using a flow scanner while the installed Fiber-Optic DTS unit has collected temperature measurements every three hours along the well since completion. An ensemble of machine learning models has been developed using as input the DTS measurements taken during the production logging operation, details of mechanical logs, completions design and hydraulic fracture treatments data of the well to develop the real-time shale gas production monitoring tool

Distributed Fiber Optic Sensing for in-well hydraulic fracture monitoring

This study presents the results from in-well hydraulic fracture monitoring within a horizontal well in an unconventional reservoir utilizing Distributed Fiber Optic Sensing (DFOS). An in-house-developed Brillouin-based Distributed Strain Sensing (DSS) interrogator was deployed to obtain strain measurements, complemented by a commercial Raman-based Distributed Temperature Sensing (DTS) interrogator for temperature measurements and a commercial Rayleigh-based Low-Frequency Distributed Acoustic Sensing (LF-DAS) interrogator for strain-rate measurements. Examined over a ten-day period, the spatio-temporal distribution of temperature-compensated strain obtained from DSS and DTS revealed distinct signatures of the multi-stage hydraulic fracturing process. These signatures were analyzed with respect to fracture width growth and closure, residual strain effects, and fracture conductivity near the wellbore. Fracture widths within the fracture zone were estimated for individual stages. The findings were assessed with LF-DAS measurements for further evaluation. This work integrates DFOS-measured strain, temperature, and strain-rate data for monitoring in-well hydraulic fracturing, with the goal of supporting future studies in interpreting DFOS measurements for improved understanding of hydraulic fracturing in unconventional reservoirs

Application of High-Resolution Fiber Optic Data to Enhance Completion Design

MS Dissertation Defense By Christian Pacheco Title: Application of High-Resolution Fiber Optic Data to Enhance Completion Design Major: Petroleum and Natural Gas Engineering Date: Thursday, April 18, 2024 Time: 4:00 PM Place: 141 Engineering Science Building Abstract Well stimulation is a technique that has been used in the industry for decades, and with the ability to drill wells horizontally the practice has become more valuable and effective than ever before. Its use is consistently being optimized and completions design plays a crucial role in the recovery of hydrocarbons. Several different downhole tools and measurements have been used to optimize these designs and with the continued advancement of technology available, more data has become available to better understand the events occurring beneath the surface. Logging tools provide a vast amount of information about a well and the formations it encounters. Properties and characteristics of the formations can be determined, and several parameters can be analyzed for efficient design. Using the image logging tools, acoustic or electrical, fractures can be identified based on observation of dips in the well log. These dips can represent a fracture and the depth, angle, and azimuth can be key to determining fracture properties and creating an optimal completions design. Fiber optics are also one of the tools being explored to understand and optimize the completion designs. This fiber optic data is recording events that transpire during a frack job with measurements that include temperature and acoustic data. Using well log data along with the fiber optic data can lead to a better understanding of the events occurring downhole. The Marcellus Shale Energy and Environmental Laboratory (MSEEL) research group provided an analysis using this fiber optic and well log data over two projects, MSEEL 1 & MSEEL 2. MSEEL 1 is already completed, and the conclusions drawn from MSEEL 1 were used and applied to MSEEL 2 which is the current project. The goal was to increase productivity while lowering costs by analyzing the vast amounts of data available from well logs and fiber optic data. This data will be analyzed, and completion and production data will be used to illustrate how this material can lead to an optimal completions design. Research Advisor: Dr. Ebrahim Fathi Committee Chair: Dr. Ebrahim Fathi Committee Members: Dr. Ebrahim Fathi , Dr. Kashy Aminian, Professor Samuel Amer

Interpretation of Downhole Temperature Measurements for Multistage Fracture Stimulation in Horizontal Wells

The ideal outcomes of multistage hydraulic fracturing in horizontal wells are to create a controlled fracture distribution along the horizontal well with maximum contact with the reservoir which can provide the sufficient production after stimulation. Downhole temperature sensing is one of the valuable tools to monitor hydraulic fracture treatment process and diagnose fracture performance during production. Today, there are still many challenges in quantitative interpretations of distributed downhole temperature measurements for flow profiling. These challenges come from the following aspects: the uncertainties of the parameters ranging from the reservoir properties, well completion, to fracture geometry; the need of a fast and robust forward model to simulate temperature behavior from injection, shut-in and production accurately; the need of an inversion methodology that can converge fast, reduce the uncertainties and lead to a practically meaningful solution. In this study, an integrated multiphase black-oil thermal and flow model is presented. This model is developed to simulate the transient temperature and flow behavior during injection, shut-in, and production for multistage hydraulic fractured horizontal wells. The model consists of a reservoir model and a wellbore model, which are coupled interactively through boundary conditions to each other. It is assumed that the oil and water components are immiscible, and the gas component is only soluble in oil. Comparing with the compositional model, this model has an improved computational efficiency while still maintains the maximum robustness. This study gives guidance on when and how to apply this black-oil thermal model to fulfill its full advantages. This study also proposed a new temperature interpretation methodology which incorporates the black-oil thermal model as the forward model for temperature simulation and the inversion model for inverting the flow rate profile along the wellbore by matching the simulated temperature with the measured temperature. The sensitivity study is first performed to determine the impact of parameters on temperature behavior such as fracture half-length, fracture permeability, matrix permeability, and matrix porosity. The inversion model uses the initial analysis on temperature gradient to identify the initial guess of fluid distribution which leads to a faster convergence as well as a sensible solution. The Levenberg-Marquart algorithm is adopted to update the inversion parameters during each iteration. A synthetic example with multiple fractures is presented to test the interpretation procedure’s accuracy and speed. The interpretation methodology is further applied to two different filed cases. One is a single-phase gas producing horizontal well with multiple hydraulic fractures; the other one is a two-phase water-oil producing horizontal well with multiple hydraulic fractures. This study illustrates how to adjust the methodologies and perform the analysis for each particular case and explains how to reduce the uncertainties and increase the interpretation efficiency. The results reveal that this temperature interpretation methodology is efficient and effective to translate temperature measurements to flow profile quantitatively with reasonable assumptions

INVESTIGATION OF GAS DYNAMICS IN WATER AND OIL-BASED MUDS USING DAS, DTS, AND DSS MEASUREMENTS

Reliable prediction of gas migration velocity, void fraction, and length of gas-affected region in water and oil-based muds is essential for effective planning, control, and optimization of drilling operations. However, there is a gap in our understanding of gas behavior and dynamics in water and oil-based muds. This is a consequence of the use of experimental systems that are not representative of field-scale conditions. This study seeks to bridge the gap via the well-scale deployment of distributed fiber-optic sensors for real-time monitoring of gas behavior and dynamics in water and oil-based mud. The aforementioned parameters were estimated in real-time using optical fiber-based distributed acoustic sensor (DAS), distributed temperature sensor (DTS), and distributed strain sensor (DSS). This is the first well-scale study conducted to investigate gas dynamics in oil-based muds using a variety of distributed fiber-optic sensors - DAS, DTS, and DSS. The gas migration velocity, void fraction, and length of the gas-affected region were estimated across the wellbore for a series of multiphase flow experiments carried out with gas injection (nitrogen and helium) in water and oil-based mud at various operating conditions. The results obtained using each of DAS, DTS, and DSS show good agreement with downhole gauges-based estimates and observations from surface gauges, validating the reliability of the distributed fiber-optic sensors for monitoring gas behavior and dynamics in water and oil-based muds

Advanced downhole geophysical monitoring of subsurface changes with fibre-optic sensors.

Field experiments and modelling show that the use of free-surface multiples can significantly improve the coverage of the vertical seismic profiling with downhole distributed acoustic sensors. Field studies also show that these sensors can record natural noise and human activity, and indicate imperfections of borehole cementation. The signal recorded by these sensors shows excellent correlation with reservoir pressure and temperature, which can be used for monitoring of CO2 injection into geological formations

Machine Learning Assisted Framework for Advanced Subsurface Fracture Mapping and Well Interference Quantification

The oil and gas industry has historically spent significant amount of capital to acquire large volumes of analog and digital data often left unused due to lack of digital awareness. It has instead relied on individual expertise and numerical modelling for reservoir development, characterization, and simulation, which is extremely time consuming and expensive and inevitably invites significant human bias and error into the equation. One of the major questions that has significant impact in unconventional reservoir development (e.g., completion design, production, and well spacing optimization), CO2 sequestration in geological formations (e.g., well and reservoir integrity), and engineered geothermal systems (e.g., maximizing the fluid flow and capacity of the wells) is to be able to quantify and map the subsurface natural fracture systems. This needs to be done both locally, i.e., near the wellbore and generally in the scale of the wellpad, or region. In this study, the conventional near wellbore natural fracture mapping techniques is first discussed and integrated with more advanced technologies such as application of fiber optics, specifically Distributed Acoustic Sensing (DAS) and Distributed Strain Sensing (DSS), to upscale the fracture mapping in the region. Next, a physics-based automated machine learning (AutoML) workflow is developed that incorporates the advanced data acquisition system that collects high-resolution drilling acceleration data to infer the near well bore natural fracture intensities. The new AutoML workflow aims to minimize human bias and accelerate the near wellbore natural fracture mapping in real time. The new AutoML workflow shows great promise by reducing the fracture mapping time and cost by 10-fold and producing more accurate, robust, reproducible, and measurable results. Finally, to completely remove human intervention and consequently accelerate the process of fracture mapping while drilling, the application of computer vision and deep learning techniques in new workflows to automate the process of identifying natural fractures and other lithological features using borehole image logs were integrated. Different structures and workflows have been tested and two specific workflows are designed for this purpose. In the first workflow, the fracture footprints on actual acoustic image logs (i.e., full, or partial sigmoidal signatures with a range of amplitude and vertical and horizontal displacement) is detected and classified in different categories with varying success. The second workflow implements the actual amplitude values recorded by the borehole image log and the binary representation of the produced images to detect and quantify the major fractures and beddings. The first workflow is more detailed and capable of identifying different classes of fractures albeit computationally more expensive. The second workflow is faster in detecting the major fractures and beddings. In conclusion, regional subsurface natural fracture mapping technique using an integration of conventional logging, microseismic, and fiber optic data is presented. A new AutoML workflow designed and tested in a Marcellus Shale gas reservoir was used to predict near wellbore fracture intensities using high frequency drilling acceleration data. Two integrated workflows were designed and validated using 3 wells in Marcellus Shale to extract natural fractures from acoustic image logs and amplitude recordings obtained during logging while drilling. The new workflows have: i) minimized human bias in different aspects of fracture mapping from image log analysis to machine learning model selection and hyper parameter optimization; ii) generated and quantified more accurate fracture predictions using different score matrices; iii) decreased the time and cost of the fracture interpretation by tenfold, and iv) presented more robust and reproducible results

Geomechanically Optimized Perforation Strategies for Enhanced Stimulation Efficiency in Unconventional Reservoirs

Hydraulic fracturing has become a critical technology for hydrocarbon extraction from unconventional reservoirs, yet persistent challenges in stimulation effectiveness limit recovery factors and economic viability. This review synthesizes peer-reviewed literature to address gaps in understanding the interplay between in-situ stress mechanics, perforation design, and cluster efficiency. The analysis reveals that stress anisotropy—defined as the difference between maximum (σH) and minimum (σh) horizontal stresses—fundamentally governs fracture geometry and propagation patterns. High anisotropy environments, such as the Haynesville Shale, promote planar fractures, while lower anisotropy in the Eagle Ford Shale leads to complex networks. Perforation alignment with σH is shown to reduce initiation pressures by 20–40% and minimize near-wellbore tortuosity, with field studies demonstrating 22% lower breakdown pressures and 15% higher 30-day production in stress-aligned wells. Near-wellbore stress effects, including stress cage phenomena and perforation tunnel geometry, further influence fracture initiation mechanics. Cluster efficiency remains a key challenge, with 30–40% of clusters contributing <5% of production due to stress shadowing and reservoir heterogeneity. Mitigation strategies, such as limited entry perforating and engineered spacing, have improved uniformity, as evidenced by Permian Basin case studies. This review underscores the necessity of integrating geomechanical principles with real-time diagnostics to optimize completion designs and enhance recovery in unconventional plays

Estimation of Temperature Profiles using Low-Frequency Distributed Acoustic Sensing from In-Well Measurements

Distributed fiber-optic sensing for in-well measurements is primarily used for monitoring purposes. Distributed acoustic sensing (DAS) is used to record acoustic disturbances and is sensitive to changes in strain, pressure, and temperature. Distributed temperature sensing (DTS) is used to measure temperature along the fiber. Here, we compare temperature changes measured by DAS and DTS in wells over different time periods. We affirm the linear dependency between DAS’s phase change and temperature, with the derived strain rate being proportional to the time derivative of the temperature response. Given that low-frequency (LF) DAS is sensitive to strain, pressure, and temperature effects, one must choose quiet periods in the well or condition the data to only analyze the effect of temperature on the fiber. We show that LF-DAS data can be used to track temperature changes over several weeks. We then propose a method, using liquid column movements, to invert LF-DAS data for absolute temperature profiles. The temperature profile in a well can be measured using DTS. However, DTS data are not always available, and conventional Raman scattering DTS is not used in subsea wells with long lead-in lengths. Hence, it would be desirable to acquire the temperature response from LF-DAS data to use as a multipurpose tool for in-well monitoring. Here, we show that when purely investigating the response to an initial displacement of the fluid column (i.e., from rest), LF-DAS can be used along with reference sensors, such as the wellhead and downhole temperature gauge data to estimate the depth variations in temperature in production and injection wells.acceptedVersio