Long (horizontal) completion intervals typically show a wide variation in the inflow distribution along their length due to either formation heterogeneity or (frictional) flow pressure losses. Monitoring of the inflow profiles in such wells is an important step in efficient reservoir management. Accurate temperature measurements (using distributed temperature sensors, permanent downhole gauges or other forms of production logging) have become more widely available in recent years. Many published papers describe temperature sensing and its phenomenological interpretation; but few attempts have been made recently to develop a comprehensive mathematical basis for the analysis of downhole temperature behaviour.This paper presents a holistic, analytical, mathematical model for calculation of the temperature profile in horizontal wells producing liquids for reservoirs where thermal recovery methods are not being employed. The model presented in this paper rigorously accounts for (1) the Joule-Thomson effect, (2) convection, (3) transient fluid expansion and (4) time-dependent heat loss to the surrounding layers.A synthetic horizontal well model has been built using a commercial, scientific simulator as a test-bed to provide the data to allow a rigorous evaluation of the efficacy of our novel analytical methods. Asymptotic, analytical solutions have also been found for transient and steady-state flow. It has also been found possible, in addition to these constant flow rate solutions, to apply the well known pressure analysis solution techniques for the estimation of (1) thermal properties and (2) inflow profiling.The methods proposed here can be applied to a wide variety of well completion types, flow conditions and system properties. They form the basis for the calculation of oil and water flow phase cuts and distributions based purely on temperature measurements. Their use will further increase the potential applications of the modern downhole monitoring and control capabilities currently being installed in wells. As such, they will form an essential element of the "digital oil field".
Advanced completions using technologies such as Inflow Control Devices or Interval Control Valves are successfully optimising oil and gas production. They are often accompanied by the installation of Distributed Temperature Sensors and Permanent Downhole Gauges, providing continuous, real-time monitoring of the downhole pressure and temperature. Interpretation of such data to locate influxes and to track zonal inflow performance is an important step towards a comprehensive well and field control strategy. Interpretation of these downhole measurements requires accurate modelling of the well's pressure and temperature. Modelling of the pressures associated with the production of wells with advanced completion has been available for sometime. However, the workflow required for temperature modelling is not yet complete. This paper discusses using available theoretical models and software tools to model the temperature distribution in wells with advanced completion. The strengths and weaknesses of the various approaches for data interpretation will be discussed. Several methods for the quantitative interpretation of downhole temperature measurements are proposed. The application of both currently available and novel theoretical models will be discussed. The workflow will be shown to be capable of providing both zonal flow rates and phase compositions. The interpretation and analysis techniques presented here form the basis of a well and/or field monitoring and production control workflow. 1.0 Introduction The installation of advanced well completions is increasing in popularity 1,2 worldwide. One of the main priorities of these technologies is the creation and improvement of hardware for monitoring, managing and controlling the well inflow at the level of an individual zone. The control capabilities of these devices can be subdivided into active {Interval Control Valves (ICVs)} and passive {Inflow Control Devices (ICDs)} while autonomous ICDs combine features from both devices. They are able to solve a wide range of fluid production problems 3,4. ICDs are installed to ensure a uniform production or injection profile along the complete completion length of the well, thus delaying breakthrough, improving clean-up, optimizing injection or steam-assisted gravity drainage, etc. ICVs are being used for recovery optimization in complex reservoirs and/or wells where the completion is required to dynamically respond to changing and uncertain production behaviour 5. Efficient inflow (or outflow) control rests on the availability of an advanced monitoring system which provides real-time values of the phase flow distribution in the well. Modern downhole monitoring devices, installed as part of the advanced completion, provide sufficient information that they, along with the flow control hardware, form the basis of the intelligent field. Advanced monitoring technologies, such as optical fibre based Distributed Temperature or Pressure Sensors (DTS or DPS), Permanently installed Downhole Gauges (PDG), multiphase flow meters, etc., produce large amounts of downhole data in real-time 6,7. Traditional analytical approaches have proved to be insufficient to retrieve useful information from the vast quantities of downhole data provided by the increasing employment of temperature and pressure measurement sensors. The realisation that temperature behaviour is a complex process that has had less research attention than its pressure counterpart resulted in the initiation of research in this area of petroleum data processing and analysis. A significant part of this effort has been directed towards the analysis of temperature data to evaluate flow rates and phase distributions at specific points along the completion's length In this paper we will discuss the success of several available analytical and simulation tools to accurately model steady-state, temperature and pressure profiles in wells. The ability to estimate phase rates in wells completed with ICDs and/or ICVs will be discussed. We will also present several novel analytical solutions on reservoir temperature distribution calculation. Finally a group of calculation methods to estimate inflow and permeability distributions and flow rate profiles will be presented.
Summary Field development and control optimization aim to maximize the economic profit of oil and gas production. Mathematically, this results in a complex optimization problem with a large number of correlated decision (also known as control) variables of various types at different levels (e.g., at the level of well location variables or at the level of well production/injection control also know as well control or control settings variables) and a computationally expensive objective function (i.e., a reservoir simulation model). Current multilevel optimization frameworks provide only a single optimal scenario for a field development and control problem. However, unexpected problems that commonly arise during field development and operations can impose extra constraints, resulting in operators having to eventually select an adjusted, and most-likely, nonoptimal scenario. This work proposes a novel multisolution optimization framework, based on sequential optimization of control variables at multiple levels, providing the flexibility for operators to make optimal decisions while considering operational constraints. An ensemble of close-to-optimum solutions is selected from each level (e.g., from the well location optimization level) and transferred to the next level of optimization (e.g., to the control settings optimization), and this loop continues until no significant improvement is observed in the objective value. Fit-for-purpose clustering techniques are also developed to systematically select an ensemble of solutions, with maximum differences in well locations and control settings but small variation in the objective values, at each level of the optimization. The developed framework has been tested on two benchmark case studies. The results demonstrate high economic and operational efficiency of the developed multisolution framework as compared to the traditional approaches that rely on single-solution optimization. It is shown that suboptimal solutions from an early optimization level could approach the optimal solution at the next level(s), highlighting the value of the developed multisolution framework to deliver operational flexibility by a more efficient exploration of the search space.
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 directly measure the flow rates and phase cuts required for optimal control of the well's producing zones are not normally installed. Instead, Multi-zone, Multi-phase Flow Metering (MFM) parameters are calculated from indirect measurements (e.g. from zonal pressures, temperatures, and well flow rates).To-date all published techniques of zonal flow rate allocation in multi-zone I-wells are "passive" in that they calculate the MFM parameters 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 paper describes an "active" monitoring technique which uses a direct search method based on Deformed Configurations to optimise the sequence of Interval Control Valve (ICV) positions during a routine multi-rate test in a "intelligent" well. This novel approach maximises the accuracy of the calculated reservoir properties and MFM parameters.Four "active monitoring" levels are available. Each one uses a particular combination of down-hole and surface measurements depending on the available 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 the in-flow performance, the volumes of produced fluids and the reservoir's properties at both a well and a zonal level.Our "active monitoring" workflow can be extended to a wide range of production and injection allocation problems for any well completion type. The workflow may also be used to improve the accuracy of production flow rate measurements and, ultimately, to maximise oil production rate and recovery.
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