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

Novel annular flow electromagnetic measurement system for drilling engineering

Downhole micro-flux control drilling technology can effectively solve drilling accidents, such as kick and loss in narrow density window drilling scenarios. Using a downhole annular flow measurement system to obtain real-time information of downhole annular flow is the core and foundation of downhole micro-flux control drilling technology. The research work of electromagnetic flowmeters in recent years creates a challenge for downhole annular flow measurement. This paper proposes a new method for an annular flow measurement system based on the electromagnetic induction principle. First, the annular flow measuring principle, the weight function, the density of virtual current, and the magnetic field of the annular flow electromagnetic measurement system are described. Second, the basic design of the annular flow electromagnetic measurement system is described. Third, model simulation and dynamic experiments on an annular flow electromagnetic measurement system are carried out. The simulation and experimental results show a linear relationship between the system output and the annular flow rate, and also verify the correctness of annular flow electromagnetic measurement theory

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

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

Modelling of flows in vertical pipes and its application to multiphase flow metering at high gas content and to the prediction of well performance

Imperial Users onl

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

Flow assurance and multiphase pumping

A robust understanding and planning of production enhancement and flow assurance is required as petroleum E&P activities are targeting deepwaters and long distances. Different flow assurance issues and their solutions are put together in this work. The use of multiphase pumps as a flow assurance solution is emphasized. Multiphase pumping aids flow assurance in different ways. However, the problem causing most concern is sand erosion. This work involved a detection-based sand monitoring method. Our objectives are to investigate the reliability of an acoustic sand detector and analyze the feasibility of gel injection as a method to mitigate sand erosion. Use of a sand detector coupled with twin-screw pumps is studied under varying flow conditions. The feasibility of gel injection to reduce slip and transport produced solids through twin-screw pump is investigated. A unique full-scale laboratory with multiphase pumps was utilized to carry out the experimental tests. The test results indicate that acoustic sand detection works in a narrow window around the calibration signature. An empirical correlation for predicting the twin-screw pump performance with viscous fluids was developed. It shows good agreement in the practical operational limits – 50% to 100% speed. The results indicate that viscous gel injection should be an effective erosion mitigation approach as it reduces slip, the principle cause of erosive wear. To correlate the performance of viscous fluid injection to hydroabrasive wear, further experimental investigation is needed