Novel methods for active reservoir monitoring and flow rate allocation of intelligent wells

The value added by intelligent wells (I-wells) derives from real-time, reservoir and production performance monitoring together with zonal, downhole flow control. Unfortunately, downhole sensors that can directly measure the zonal flow rates and phase cuts required for optimal control of the well’s producing zones are not normally installed. Instead, the zonal, Multi-phase Flow Rates (MPFRs) are calculated from indirect measurements (e.g. from zonal pressures, temperatures and the total well flow rate), an approach known as soft-sensing. To-date all published techniques for zonal flow rate allocation in multi-zone I-wells are “passive” in that they calculate the required parameters to estimate MPFRs for a fixed given configuration of the completion. These techniques are subject to model error, but also to errors stemming from measurement noise when there is insufficient data duplication for accurate parameter estimation. This thesis describes an “active” soft-sensing technique consisting of two sequential optimisation steps. First step calculates MPFRs while the second one uses a direct search method based on Deformed Configurations to optimise the sequence of Interval Control Valve positions during a routine multi-rate test in an I-well. This novel approach maximises the accuracy of the calculated reservoir properties and MPFRs. Four “active monitoring” levels are discussed. Each one uses a particular combination of available indirect measurements from well performance monitoring systems. Level one is the simplest, requiring a minimal amount of well data. The higher levels require more data; but provide, in return, a greater understanding of produced fluids volumes and the reservoir’s properties at both a well and a zonal level. Such estimation of the reservoir parameters and MPFRs in I-wells is essential for effective well control strategies to optimise the production volumes. An integrated, control and monitoring (ICM) workflow is proposed which employs the active soft-sensing algorithm modified to maximise I-well oil production via real-time zonal production control based on estimates of zonal reservoir properties and their updates. Analysis of convergence rate of ICM workflow to optimise different objective functions shows that very accurate zonal properties are not required to optimise the oil production. The proposed reservoir monitoring and MPFR allocation workflow may also be used for designing in-well monitoring systems i.e. to predict which combination of sensors along with their measurement quality is required for effective well and reservoir monitoring

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

Dynamic modelling and real-time monitoring of intelligent wells

Intelligent Wells (I-Wells) are the wells equipped with in-well Flow Control Devices (FCDs) and sensors. I-Wells offer a wide range of flow control and monitoring options, with the latter often being subject to how well the information is derived from the measured, raw data. Pressure or temperature are the measurements most commonly taken and requiring interpretation in I-Wells. This work develops innovative methods for modelling and monitoring of dynamic, transient flow in I-Wells. The topics cover: i. I-well clean-up modelling and analysis; ii. Integrated Pressure and Temperature Transient Analysis (PTTA) in wells; and iii. Pressure Transient Analysis (PTA) in I-Wells. This study starts with addressing the challenging clean-up process in I-Wells. A dynamic, coupled wellbore-reservoir modeling workflow is developed that simulates the whole process from fluid invasion to the flow back period. This is followed by investigating the role of different types of FCDs, e.g. autonomous and passive FCDs, well geometries etc. on the cleanup efficiency. General recommendations to facilitate the clean-up in I-Wells are further provided. This study continues with a novel methodology integrating mature PTA solutions with the relatively new Temperature Transient Analysis (TTA) ones for various applications such as reservoir characterization, flow rate allocation and completion monitoring. Several available TTA solutions are extended to describe the multiphase flow in the reservoir. The required modifications and workflow are developed and verified using synthetic case studies. The value of the integrated analysis is then demonstrated by presenting a new method applicable for multi-phase production rate allocation in multi-zone, vertical I-Wells. The variable rate problem in the TTA context is later studied where the distorted signal is reconstructed by proposing normalization methods and developing a data-driven deconvolution algorithm. Finally, the effect of non-linear pressure drop across FCDs in I-Wells on applicability of the classical PTA solutions is investigated. The corrections to incorporate this effect into the classical PTA solutions is implemented as well as a workflow to decompose the total skin is presented. The value and applicability of the proposed workflow are later illustrated using real field case studies. This thesis is an important contribution into the understanding, modelling, monitoring and analysis of dynamic flow process in advanced wells

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

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

A Technology Perspective and Optimized Workflow To Intelligent Well Applications

Today’s oil and gas industry is faced with several geographic and economic challenges that have significantly increased the pressure on companies engaged in oil and gas exploration and production. Technical as well as economic challenges like the highly volatile crude oil prices, global competition for depleting resources and pressure from shareholders for return on investment are threatening to the industry. In the quest to address these challenges, operators are continuously seeking advanced technology that could increase production, improve recovery, and minimize cost. Although advanced technology such as 3D and 4D seismic downhole sensors have significantly improved the amount of accessible realtime information, the amount of data is often massive and too complex to accurately analyze. Within the past decade, significant advances in drilling and completion techniques have been made to enable more active monitoring and control of production wells. Smart well technology, also known as Intelligent Well Completions (IWC), is one of such technologies that integrates permanent downhole sensors with surface-controlled downhole flow control valves, enabling operators to monitor, evaluate, and actively manage production (or injection) in real time. All of this is achieved without any well interventions, thus completely eliminating the risk and economic losses associated with well intervention. A comprehensive review of smart well technology, as well as real-world case studies will be presented. A case study simulation is performed to evaluate the additional value that is derived by adopting smart well technology. The simulation results clearly indicate that adopting smart well technology significantly reduced field water cut, accelerated the productions time and improved the Net Present Value (NPV) of the project. Finally, a workflow is presented which can be used to assess to applicability of a given field with multiple producing wells

Processing and analysis of transient data from permanent down-hole gauges (PDG)

The Permanent Downhole Gauge (PDG) can monitor the reservoir in real time over a long period of time. This produces a huge amount of real time data which can potentially provide more information about wells and reservoirs. However, processing large numbers of data and extracting useful information from these data brings new challenges for industry and engineers. A new workflow for processing the PDG data is proposed in this study. The new approach processes PDG data from the view of gauge, well and reservoir. The gauge information is first filtered with data preprocessing and outlier removal. Then, the well event is identified using an improved wavelet approach. The further processing step of data denoise and data reduction is carried out before analyzing the reservoir information. The accurate production history is very essential for data analysis. However, the accurate production rate is hard to be acquired. Therefore, a new approach is created to recover flow rate history from the accumulated production and PDG pressure data. This new approach is based on the theory that the relation between production rate and the amplitude of detail coefficient are in direct proportion after wavelet transform. With accurate pressure and rate data, traditional well testing is applied to analyze the PDG pressure data to get dynamic reservoir parameters. The numerical well testing approach is also carried out to analyze more complex reservoir model with a new toolbox. However, these two approaches all suffer from the nonlinear problem of PDG pressure. So, a dynamic forward modelling approach is proposed to analyze PDG pressure data. The new approach uses the deconvolution method to diagnose the linear region in the nonlinear system. The nonlinear system can be divided into different linear systems which can be analyzed with the numerical well testing approach. Finally, a toolbox which includes a PDG data processing module and PDG data analysis module is designed with Matlab

Interpretation of transient temperature data from Permanent Down-hole Gauges (PDGs)

With the installation of Permanent Down-hole Gauges (PDGs) during oil field development, a large volume of high resolution pressure, temperature and sometimes flow-rate data are available for real-time and continuous reservoir monitoring. In practice, interpretations of these data can optimize well performance, provide information about the reservoir and continuously calibrate the reservoir model. Although the wellbore is in a non-isothermal environment, heat transfer between the fluid in the wellbore and the formation is often ignored and temperature is usually assumed to be constant in the process of data interpretation, leading to misunderstanding of the pressure profile. Furthermore, the pressure transient analysis (PTA) often fails to determine accurate flow regimes, and may be erroneously applied in nonlinear reservoir-well systems. These problems motivated my detailed analysis of temperature data. In this thesis, firstly, a non-isothermal wellbore model that is capable of predicting the temperature, pressure, and flow-rate profiles under multi-rate and multiphase production scenarios is established. Then this numerical wellbore model is coupled with a reservoir model to reproduce the transient temperature behaviour at gauge locations. Secondly, a new workflow for integrating transient down-hole data processing is introduced. The relationship between temperature change and flow-rate change is interpreted and a new nonlinearity diagnostic function () is presented. Thirdly, new procedures of model-independent transient temperature analysis are performed, followed by diagnosing the wellbore storage regime, verifying the PTA interpretation results, and reconstructing the flow-rate history using transient temperature data. Several case studies are conducted to demonstrate how transient temperature analysis, along with the transient pressure analysis can greatly reduce the uncertainties in well testing interpretation. The applications of both synthetic datasets which are simulated by the fully coupled wellbore-reservoir model and real field datasets demonstrated that the temperature data can provide additional constraints for pressure analysis. Additionally, the reliability of the developed methods which reveal complementary reservoir information from transient temperature data has also been verified

Advancing Carbon Sequestration through Smart Proxy Modeling: Leveraging Domain Expertise and Machine Learning for Efficient Reservoir Simulation

Geological carbon sequestration (GCS) offers a promising solution to effectively manage extra carbon, mitigating the impact of climate change. This doctoral research introduces a cutting-edge Smart Proxy Modeling-based framework, integrating artificial neural networks (ANNs) and domain expertise, to re-engineer and empower numerical reservoir simulation for efficient modeling of CO2 sequestration and demonstrate predictive conformance and replicative capabilities of smart proxy modeling. Creating well-performing proxy models requires extensive human intervention and trial-and-error processes. Additionally, a large training database is essential to ANN model for complex tasks such as deep saline aquifer CO2 sequestration since it is used as the neural network\u27s input and output data. One major limitation in CCS programs is the lack of real field data due to a lack of field applications and issues with confidentiality. Considering these drawbacks, and due to high-dimensional nonlinearity, heterogeneity, and coupling of multiple physical processes associated with numerical reservoir simulation, novel research to handle these complexities as it allows for the creation of possible CO2 sequestration scenarios that may be used as a training set. This study addresses several types of static and dynamic realistic and practical field-base data augmentation techniques ranging from spatial complexity, spatio-temporal complexity, and heterogeneity of reservoir characteristics. By incorporating domain-expertise-based feature generation, this framework honors precise representation of reservoir overcoming computational challenges associated with numerical reservoir tools. The developed ANN accurately replicated fluid flow behavior, resulting in significant computational savings compared to traditional numerical simulation models. The results showed that all the ML models achieved very good accuracies and high efficiency. The findings revealed that the quality of the path between the focal cell and injection wells emerged as the most crucial factor in both CO2 saturation and pressure estimation models. These insights significantly contribute to our understanding of CO2 plume monitoring, paving the way for breakthroughs in investigating reservoir behavior at a minimal computational cost. The study\u27s commitment to replicating numerical reservoir simulation results underscores the model\u27s potential to contribute valuable insights into the behavior and performance of CO2 sequestration systems, as a complimentary tool to numerical reservoir simulation when there is no measured data available from the field. The transformative nature of this research has vast implications for advancing carbon storage modeling technologies. By addressing the computational limitations of traditional numerical reservoir models and harnessing the synergy between machine learning and domain expertise, this work provides a practical workflow for efficient decision-making in sequestration projects

Wellbore measurements and fracture diagnostics for hydraulic fracture optimization

This dissertation presents new methods for analyzing wellbore pressure and strain measurements to better diagnose and optimize hydraulic fracturing treatments. Pressure interference data between fractured wells in unconventional formations during fracturing can be used to estimate key unknown fracture parameters. Currently, only fracture to fracture or fracture to gauge fluid pressure measurements have been investigated, while little to no research has been done to understand the expected deformation/strain in the offset well due to hydraulic fracturing. To address this gap, laboratory experiments were conducted to clearly show the expected ranges of deformation under applied loading, while numerical modeling computed the total deformation and volume reduction of the casing during hydraulic fracture intersection. A new semi-analytical model to calculate poro-elastic pressure interference between fractured horizontal wells was developed, and a fully coupled fracture simulator was deployed to investigate the 3D strain field of a propagating fracture and its effects on a steel wellbore in the subsurface. Multiple techniques to interpret wellbore pressure data and deformation were developed to resolve additional variables and reduce uncertainty. These methods included developing workflows for Volume to First Response of a fracture arrival, fracture growth rate extrapolations, Monte-Carlo simulations, and wellbore azimuthal strain. Simulating fracture propagation from the treatment well and finely meshing the monitoring wellbore within the same mesh allows for computing the strain and pressure change within the monitor wellbore accurately. These measurements together with the wellbore pressure response can then be interpreted to obtain the timing of frac-hits, the fracture geometry, and the cluster efficiency. Finally, a diagnostic dashboard was developed to integrate the learnings from this and earlier work on measurements and methods to generate realizations of fracture geometry from pressure signatures. This dashboard sets the groundwork for real-time diagnostics of fracture interactions and provides the industry a tool in which to make faster decisions on a once tedious and difficult process. The results can be used to make important reservoir management decisions such as well spacing and well placement while optimizing hydraulic fracture treatments.Petroleum and Geosystems Engineerin