Permanent Downhole Gauges Research Articles

New approach for interpreting pressure and flow rate data from permanent downhole gauges, least square support vector machine approach

Permanent downhole gauges (PDGs) are permanently installed in oil and gas wells to monitor real-time downhole pressure, temperature and sometimes flow rate. Recording these variables continuously plays a key role in reservoir analysis. Conventional welltesting methods as one group of approaches for reservoir characterization are generally used in a way that interpreting the obtained reservoir pressure (reservoir response) will be easy. For this target to be achieved welltesting often deals with constant flow rate sections of data in a short period of time. As an alternative approach, machine learning deals with large volumes of data (which are recorded in a long period of times) to find the relationship among the related variables (Liu and Horne, 2013a). Here, we aim to describe the usage of the least square support vector machine (LS-SVM) as a machine learning method for analyzing data from permanent downhole gauges. This approach is a tool for analysis of data that are in the presence of noise, gaps and outliers which reveals another advantage of using LS-SVM instead of conventional well-testing methods in which designed tools in controlled environments are needed. Last but not least, this method is one of the few ways which can use the flow-rate data with uncertainty. In this document, the application of radial basis function (RBF) as a function approximation for mapping the data to a high dimensional feature space is investigated. The LS-SVM method proved to be successful in predicting pressure especially when the wellbore/reservoir model contains a nonlinear pattern or when the pressure derivative curve has a nonlinear trend. Since PDG records data during production and not during an imposed flow rate condition, the existence of nonlinear features is common which proves the applicability of the presented method.

PDG Pressure Estimation in Offshore Oil Well: Extended Kalman Filter vs. Artificial Neural Networks

Abstract The Permanent Downhole Gauge (PDG) pressure measurement is of great importance for offshore oil well modeling and control since it is measured close to the bottom hole. The PDG is installed in a remote undersea environment, which makes expensive the maintenance in case of fault. For this reason, PDG measurements are frequently unavailable. To overcome this limitation, the PDG pressure can be estimated using other available measurements. The estimation is not a simple task since, depending on process operational conditions, the multiphase flow might present limit cycles. In this work, Artificial Neural Network (ANN) and Extended Kalman Filter (EKF) are proposed as potential techniques for the PDG pressure estimation. The comparison of the results shows that ANN returns precise estimation for a short-time window after the failure, but fails when a different process operating condition is applied, while EKF returns good estimation in all the cases.

Rigorous gas field production data analysis

With the advent of permanent downhole gauges and automated flowing tubing head pressure measurements, today’s engineers have a veritable plethora of production data on which to characterise gas reservoirs and estimate their ultimate recovery. As consultants, the authors see datasets that have not always been examined to their fullest potential. More often than not this is due to a singular approach to analysis, rather than application of a range of analyses. This paper discusses how combining traditional and more advanced production data analysis techniques has provided insight into fields ranging from tight gas reservoirs to conventional reservoirs under active waterdrive. Such insight is not obtained from brute force application of one size fits all techniques but understanding and using the appropriate combination of techniques that are likely to illuminate the underlying physics of the reservoir at hand. The authors have seen examples where basic data analysis has identified resource ranges outside the range estimated from sensitivity studies with detailed and sophisticated but effectively singular models.

Advancing Deep Learning to Improve Upstream Petroleum Monitoring

Data analytics is rapidly growing field in both academia and industry dealing with processing and interpreting large and complex data sets. It has got already many successful applications via advancing machine (ML) and deep learning (DL) techniques, starting to evolve in the upstream petroleum industry as well. The industry operates now with huge amount of sensors installed in different facilities, particularly in production and injection wells. These sensors provide millions of measurements, such as pressure, temperature, and rate every year for every well. The measurements may be highly correlated and carry crucial information for decision making. This paper concentrates on pressure-rate data sets accumulated with massive installation of permanent downhole gauges in such wells. The non-linear autoregessive (NARX) and the long short term memory (LSTM) neural networks were assembled and tested on a synthetic data set to compare results of pressure prediction, already addressed in the literature. The LSTM provided better predictions, but did not manage to capture entirely the pattern of the data. The shifting window method was then applied to improve the LSTM prediction capabilities, based on previous successful application in forecasting electricity demand. The method implies smooth transition from training to prediction improving network performance. The LSTM with the shifting window provided more accurate results for pressure prediction, and it was then successfully applied for rate prediction. Testing of different configurations of the LSTM network has shown that the pressure prediction performs well with less number of nodes in the hidden layers if compared with the rate predictions. Significant error decrease is achieved relatively fast (after 20 iterations) for both prediction tasks, making such predictions feasible for large data sets. The results provide basis for filling gaps in well monitoring data and short-term performance forecast, crucial tasks for decision making in all the industries operating with wells.

Модели и алгоритмы адаптивной интерпретации результатов комбинированных газогидродинамических исследований интеллектуальных скважин

Актуальность работы обусловлена необходимостью создания моделей и алгоритмов обработки результатов газогидродинамических исследований интеллектуальных скважин, оснащенных стационарными информационно измерительными системами, позволяющими определять параметры пластов и скважин в процессе проведения испытаний в режиме реального времени. Цель исследования: повышение эффективности и качества интегрированных систем моделей (дебитов, забойных давлений, дополнительных априорных данных и экспертных оценок параметров газовых пластов) и алгоритмов адаптивной идентификации и интерпретации результатов комбинированных газогидродинамических исследований скважин на стационарных, по индикаторной кривой, и нестационарных, по кривой восстановления давления, режимах испытаний. Методы. Использованы теоретические и практические разработки в области газогидродинамических исследований скважин, системного анализа, идентификации систем, оптимизации функций и линейной алгебры. Для анализа точности и устойчивости моделей и алгоритмов идентификации и интерпретации использовались промысловые данные газодинамических исследований скважин месторождения Тюменской области по индикаторной кривой и кривой восстановления давления, экспертные оценки пластового давления и фильтрационных параметров пласта. Результаты. Проведен анализ эффективности и качества моделей и алгоритмов идентификации и интерпретации на примерах обработки результатов испытаний нефтяных и газовых скважин по индикаторной кривой и кривой восстановления давления. Показано, что интегрированные системы моделей и алгоритмы адаптивной идентификации и интерпретации позволяют: определять фильтрационные параметры и энергетическое состояние пластов и скважин, число режимов и время завершения исследований в процессе их проведения в режиме реального времени; обеспечить устойчивость и повысить точность оценок проницаемости, пъезопроводности пласта, пластового давления, скин-фактора скважины за счет использования и корректировки дополнительных априорных данных и экспертных оценок пластового давления и фильтрационных параметров пластов; обрабатывать короткие недовосстановленные кривые забойного давления скважины после ее остановки.

Are complex black-box models for Permanent Downhole Gauge pressure estimation necessary?

The Permanent Downhole Gauge (PDG) is a pressure and temperature sensor located subsea near the perforation point in the offshore oil production system. This sensor is very useful in operation problems detection (fouling, valves, plugging, etc.), multiphasic flow analysis, production tests adjustment, control strategy and model identification. Given its location under hazardous conditions, it may come to failure or imprecisions in measurement. The high costs related to the maintenance make it infeasible to perform. To overcome this issue, a methodology based on digital signal processing (DSP) is proposed in order to design a low pass digital filter aiming the PDG pressure reconstruction. The proposed methodology requires no plant model, low computational cost and only the Christmas-Tree pressure measurement. The proposed methodology is compared with two commonly employed black-box methodologies: Neural Network and Transfer Function Estimation. The results show the success of the designed filter and the advantages over the black-box approaches. The estimation of the PDG pressure from three operating wells using real process data was performed.

New Understanding of Transient Pressure Response in the Transition Zone of Oil-Water and Gas-Water Systems

Well test analysis requires a preselected model, which relies on the context input and the diagnostic result through the pressure logarithmic derivative curve. Transient pressure outer boundary response heavily impacts on the selection of such a model. Traditional boundary-type curves used for such diagnostic purpose are only suitable for single-phase flow in a homogeneous reservoir, while practical situations are often much more complicated. This is particularly true when transient pressure is derived during the field development phase, for example, from permanent down-hole gauge (PDG), where outer boundary condition such as an active aquifer with a transition zone above it plays a big role in dominating the late time pressure response. In this case, capillary pressure and the total mobility in the transition zone have significant effect on the pressure response. This effect is distinctly different for oil-water system and gas water system, which will result in the pressure logarithmic derivatives remarkably different from the traditional boundary-type curves. This paper presents study results derived through theoretical and numerical well testing approaches to solve this problem. The outcome of this study can help in understanding the reservoir behavior and guiding the management of mature field. According to the theoretical development by Thompson, a new approach was derived according to Darcy’s law, which shows that pressure response in the transition zone is a function of total effective mobility. For oil-water system, the total effective mobility increases with an increase in the radius of transition zone, while for gas-water system, the effect is opposite.

First Three-Zone Intelligent Completion in Brazilian Presalt: Challenges and Lessons

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper OTC 28136, “First Three-Zone Intelligent Completion in the Brazilian Presalt: Design Considerations, Challenges, and Lessons Learned,” by Filipe Del Vecchio, Potiani Maciel, Francisco Salom, José Chagas, and Antonio Ortiz, Baker Hughes, a GE Company, prepared for the 2017 Offshore Technology Conference Brasil, Rio de Janeiro, 24–26 October. The paper has not been peer reviewed. Copyright 2017 Offshore Technology Conference. Reproduced by permission. Since the first intelligent completion was installed 20 years ago, the systems have become increasingly complex in order to reach productivity and optimization goals, allowing real-time independent monitoring and management of each zone in the well. Accompanying the search for greater system efficiency is the goal of reliability assurance. This paper presents the complexity and challenges of planning and installing the first intelligent completion in a subsea well with three producing zones in the Brazilian presalt. Introduction Below water depths greater than 7,200 ft, below salt layers up to 6,500 ft thick, and at true vertical depths (TVDs) of approximately 18,000 ft lie the presalt fields. The distance from shore (as much as 300 km), high carbon dioxide and hydrogen sulfide concentrations, high temperatures (sometimes greater than 100°C), and the existence of multiple intervals with different porosities all add to the challenges of producing from the presalt successfully. Through the installation of permanent downhole gauges (PDGs), intelligent completion allows real-time monitoring of downhole parameters such as pressure and temperature, providing a better understanding of what is happening at any given time. Hydraulically actuated flow-control valves give the operators the ability to access each interval with the touch of a button, greatly improving reaction time and reducing the need for costly interventions. Furthermore, valves with adjustable choke settings increase ultimate recovery from the reservoir by fine tuning the production/injection of each individual zone. Chemicals can be injected to ensure productivity and avoid potential problems to production such as scale and asphaltene buildup. Equipment Design and Configuration Flat-pack (FP) and control-line protectors (clamps) were designed to ensure maximum flexibility, allowing for wells to be completed with one, two, or three zones without the need for additional equipment. The splice subs also were designed with flexibility in mind. The lower the splice sub is positioned, the fewer the slots that are necessary. However, by supplying all of the subs with the same number of slots, the same slot sizes, and the same slot positioning, the equipment could be used in any type of well layout and inventory management was greatly improved. The design of the pressure and temperature gauges and their carriers was also optimized. Instead of the PDG dictating whether the data are acquired from the tubing or the annulus, the mandrels do that. This was made possible by having two different configurations for the area where the gauge is mounted to the carrier. The tubing mandrel has a through hole allowing fluid from the inside of the string to contact the gauge’s inlet port, while the annulus mandrel has a blank interface with cross-shaped slots through which annulus fluids enter the sensor’s inlet.

Комплексный анализ замеров забойного давления и продуктивности пластов многопластовых объектов в скважинах, оборудованных системами непрерывного мониторинга

Комплексный анализ замеров забойного давления и продуктивности пластов многопластовых объектов в скважинах, оборудованных системами непрерывного мониторинга

Development of soft sensors for permanent downhole Gauges in deepwater oil wells

Downhole pressure is an important process variable in the operation of gas-lifted oil wells. The device installed in order to measure this variable is often called a Permanent Downhole Gauge (PDG). Replacing a faulty PDG is often not economically viable and to have an alternative estimate of the downhole pressure is an important goal. Using data from operating PDGs, this paper describes a number of issues dealt with in the development of soft sensors for several deepwater gas-lifted oil wells. Some of the tested models include nonlinear polynomials, neural networks, committee machines, unscented Kalman filters and filter banks. The variety of model classes used in addition to the diversity of oil wells considered brings to light some of the key-problems that have to be faced and reveal the strengths and weaknesses of each alternative solution. A major constraint throughout the work was the use of historical data, hence no specific tests were performed at any time. The aim of this work is to discuss the procedures, pros and cons of the tested solutions and to point to possible future directions of research.

Improved analysis of transient temperature data from permanent down-hole gauges (PDGs)

Improved analysis of transient temperature data from permanent down-hole gauges (PDGs)

Down-hole transient temperature, pressure and flow-rate data processing and integrated interpretation for nonlinearity diagnosis using wavelet transform

During oil and gas field development, continuous and real-time pressure, temperature and, occasionally, flow-rate data are available. Considering that the transient data acquisition process is relatively independent, integrating the analysis of the transient temperature, pressure and flow-rate data can include a large amount of useful information and can significantly reduce interpretation uncertainties.Typically, permanent down-hole gauge (PDG) data contain noise, outliers and other types of error signals that do not comport with known physical properties due to the uncontrolled collection conditions. For the purpose of correctly interpreting transient pressure and temperature data, data processing is an essential preliminary step. Several effective methods associated with pressure data processing have been applied in practice, but temperature data processing has not been discussed at length. In addition, the nonlinearities, which are caused by gas/multi-phase flow, non-Darcy flow or a change in well-reservoir parameters, may result in the erroneous application of pressure transient analysis (PTA) methods. As a result, an effective method for nonlinearity diagnosis needs to be developed.This paper describes how transient temperature, pressure and flow-rate data were processed and interpreted. First, the theory of wavelet transform was briefly introduced. Second, a workflow for the transient down-hole data processing was developed. Third, the simplest wavelet transform method, the Haar wavelet, was selected as the transient identification algorithm. In the pressure case and the temperature case, it was found that large amplitudes in the wavelet detail coefficients were caused by flow events. Fourth, the relationship between the wavelet transform amplitude and rate change (unit-rate change coefficient) was analysed, and an improved diagnostic method of nonlinearity from both transient pressure and temperature data was developed. According to the derived analytical solutions, the nonlinearities caused by a change in well-reservoir properties can be identified. Finally, the way in which the unit-rate change coefficients behave with the changes of different reservoir-well parameters was also researched by conducting sensitivity studies.As the errors that are caused by measurement technical problems or interpretation constraints in pressure data are not expected to appear in the transient temperature data, the applications of both synthetic datasets and real field datasets demonstrated that the temperature data can provide additional constraints for pressure data. Additionally, the reliability of the developed methods, which reveal complementary reservoir information from transient temperature data, was verified.

Technology Update: New Intelligent Completion System Enables Compartment-Level Control in Multilateral Wells

Technology Update Although early inflow control devices and intelligent completions (ICs) were introduced almost 20 years ago, completion technology has not kept pace with advancements in drilling technology. Today, wells completed in multilayered reservoirs, multilaterals with compartments of varying pressure, and extended-reach drilling (ERD) with wellbores as long as 12 km are becoming common. In complex, hard-to-reach reservoirs and tighter formations, operators need to maximize reservoir contact in every well to optimize reservoir drainage and minimize costs. Yet the evolution of ICs has lagged, creating a technology gap with significant ramifications. IC reliability has been steadily increasing over the years, reaching more than 97% for the life of the well in many applications. Most permanent downhole monitoring and control solutions still consist of a separate “kit” of products rather than an integrated system. Traditional IC systems lack sufficient real-time measurements in individual producing zones to facilitate “cause and effect” decision making. Without detailed compartmental information and control in case of water or gas breakthrough, operators are forced to restrict production, intervene, and in some instances work over the entire well. Until recently, there has been no way to maintain production from other unaffected zones during the long diagnostic process of production logging, well testing, interpretation, and execution of an appropriate well intervention. The typical optimization cycle substantially raises operating costs while delaying vital production for weeks or months. Consider Saudi Aramco’s experience with ICs in remote oil fields, where tight formations and a shortage of suitable surface locations drove the need to dramatically increase reservoir contact per wellhead. Before 2007, the company had successfully drilled and completed a number of multilateral ERD wells, achieving more than 5 km (16,400 ft) of reservoir contact per well. These maximum reservoir contact (MRC) wells were completed with then state-of-the-art IC technology. Completions consisted of permanent downhole gauges (PDGs) and downhole flow-control valves (FCVs), which could be partially or fully opened or closed by hydraulic control lines from the surface. Monitoring and control stations were installed in the motherbore above the junction to each side lateral.

Diagnostic of changes in reservoir properties from long-term transient pressure data with wavelet transform

For conventional well testing which is carried out over several days or several weeks, reservoir properties are usually assumed to be constant, but this assumption is often not valid for analyzing long-term transient pressure data acquired from a permanent down-hole gauge, which can record down-hole transient pressure data for several years. For example, skin factor may increase due to formation damage, or decrease due to stimulation operations during the time of long-term production. Monitoring changes in reservoir properties is important for reservoir management. This paper proposes a novel method of diagnosing the change in reservoir properties with the wavelet transform. A new diagnostic function has been defined that can detect and quantify changes in reservoir properties. Theoretical analysis and a field example have been conducted and identified to demonstrate this new method and its potential for application in oilfield.

Machine Learning Applied to Multiwell-Test Analysis and Flow-Rate Reconstruction

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 175059, “Machine Learning Applied to Multiwell-Test Analysis and Flow- Rate Reconstruction,” by Chuan Tian and Roland N. Horne, Stanford University, prepared for the 2015 SPE Annual Technical Conference and Exhibition, Houston, 28–30 September. The paper has not been peer reviewed. Permanent downhole gauges (PDGs) can provide a continuous record of flow rate and pressure, which provides extensive information about the reservoir and makes PDG data a valuable source for reservoir analysis. In previous work, it has been shown that kernel-ridge regression-based machine learning is a promising tool to interpret pressure transients from a single PDG. In this work, the machine-learning framework was extended to two applications: multiwell testing and flow-rate reconstruction. Introduction Analysis of PDG data is challenging because of the inherent characteristics of the data, including continuously variable flow rate, noise, and the large data volume. Until now, most efforts in PDG-data analysis have been concentrated on pressure-transient analysis on single wells, although there have also been some studies on temperature-transient analysis and multiwall-data analysis. Recently, however, there have been some attempts to apply machine-learning techniques for PDG-data analysis. The fundamental idea is to learn the patterns behind PDG data, where the patterns contain the reservoir information implicitly. A previous study on single-well pressure analysis showed that machine learning has the potential to handle the complexities in PDG-data analysis, and learn the reservoir model successfully. In this work, the authors use machine learning as the tool for investigation, but address two different problems—namely, multiwell testing (multiwell pressure-transient analysis) and flow-rate reconstruction. Both topics are important in PDG-data analysis in practical engineering. The authors have developed a machine-learning model for each problem and have tested the models on different real and synthetic data sets. The test results validated the developed approach, and illustrated the flexibility of the machine-learning framework for different applications by adapting the features and the targets. Problem Statement. On the basis of the background information and literature review, the objectives of this research were to Extend the machine-learning framework for pressure analysis on a single well to multiwell systems. The framework should capture the well interference accurately and be able to test a greater area of the reservoir. Develop a machine-learning model to reconstruct the flow-rate history by use of pressure data. Ensure that both models maintain the advantages of the machine-learning- based single-well pressure interpretation in terms of the accuracy of prediction, computational efficiency, and tolerance to noise. A review of methodologies, including machine-learning concepts (linear regression, kernel method, and model regularization), multiwell testing, and flowrate reconstruction, is provided in the complete paper.

A Nonisothermal Wellbore Model and Its Application in Well Testing

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper IPTC 18152, “A Nonisothermal Wellbore Model With Complex Structure and Its Application in Well Testing,” by Shiyi Zheng, SPE, London South Bank University, and Yiqun Zhang, SPE, Heriot-Watt University, prepared for the 2014 International Petroleum Technology Conference, Kuala Lumpur, 10–12 December. The paper has not been peer reviewed. Although the wellbore is in a nonisothermal environment, heat transfer between the fluid in the wellbore and the formation is often ignored and temperature is usually assumed constant in data interpretation, which will lead to misunderstanding of the pressure profile. In this work, wavelet transform and a nonlinear-regression-analysis method were used to study the relationships between downhole temperature, pressure, and flow rate. A nonisothermal wellbore model was established that is capable of predicting temperature, pressure, flow rate, and liquid-fraction profiles under multirate and multiphase production scenarios. Introduction In order to monitor the downhole production and injection conditions, as well as make decisions for well-performance optimization, the dynamic temperature data acquired from permanent downhole gauges (PDGs) and distributed-temperature sensors (DTSs) are often interpreted widely. In this paper, first, the relationships among bottomhole-pressure (BHP), -temperature (BHT), and flow-rate data were analyzed briefly by use of wavelet transform and nonlinear regression. Next, a nonisothermal wellbore model with complex structure was derived theoretically step by step. Then, the wellbore model was coupled with an existing reservoir model through BHT. After that, several synthetic cases were simulated to verify the wellbore model. Finally, on the basis of the coupled wellbore/ reservoir model, the transient temperature behavior during flowing and shut-down periods was drawn out for well-testing analysis and some typical thermodynamic parameters were estimated by use of a set of field data. BHT, BHP, and Flow-Rate Relationships It is worth noting that formation pressure and temperature emerged in many similar characteristics. First, temperature and pressure meet the same diffusion equation. Second, at the junction between formation and well, near-wellbore damage should be considered for pressure. At the same time, on the temperature side, heat loss should also be considered, located in the formation, casing, tubing, and cement. Third, wellbore storage is compared with heat-capacity coefficient in wells. Fourth, constant-pressure and impermeable boundaries have been set for pressure boundaries, but the temperature boundary is always regarded as infinite ground. Analyses of the measured PDG data show that the temperature can also be used to provide reservoir information.

Pressure-Transient Analysis as an Element of Permanent Reservoir Monitoring

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 170740, “Pressure-Transient Analysis as an Element of Permanent Reservoir Monitoring,” by A.A. Shchipanov, R.A. Berenblyum, and L. Kollbotn, IRIS, prepared for the 2014 SPE Annual Technical Conference and Exhibition, Amsterdam, 27–29 October. The paper has not been peer reviewed. Permanent downhole gauges (PDGs) provide vast amounts of pressure-transient and rate data which may be interpreted with improved pressure-transient-analysis (PTA) approaches to gain more knowledge about reservoir dynamics. Permanent pressure and rate measurements allow for analysis of time-lapse pressure transients and comparative interpretation of flowing and shut-in periods. The approaches used provided the basis for an improved methodology of interpreting permanent pressure measurements, where the scope of the standard PTA application may be extended to integrate new data sources. A Methodology of PDG Interpretation Focusing on Both Flowing and Shut-In Periods Practical Remarks on Comparison of Pressure Transients and Choosing a Model. Comparison of different pressure transients is usually carried out on the basis of plotting all the transients and derivatives on the same log-log plot. In practice, a difference between time-lapse shut-in pressure transients does not necessarily indicate change in well reservoir parameters because such a comparison is usually carried out for rate-normalized data with a chosen reference transient. The rate before the shut-in period of interest governs the pressure-transient location on the log-log plot. An approximate value may be attributed to this rate because of the averaging of flow data, while permanent rate measurements may help in reducing this uncertainty. Flowing pressure transients are usually normalized subject to variable rates during the flowing period. This makes comparison of these pressure transients more reliable. Pressure derivatives are more representative in this sense, because well history before and during a pressure transient is accounted for by use of the superposition principle commonly used for the derivative calculation. At the same time, assuming radial flow as the main flow regime in the superposition calculation—as well as averaging, cutting, or possible errors in rate history before the pressure transient of interest— may have an impact on the derivative trend, especially for late elapsed times and interpreting boundary effects. Simulation of the well history in the linear scale with the analytical model used for the pressure and derivative interpretations in the log-log scale improves reliability of the analysis. The simulated pressure response may help in evaluating the impact of superposition effects and in revealing changes in well/reservoir parameters. Comparison of time-lapse pressure transients and derivatives may be used for diagnostics of changes in well/reservoir parameters, while only simulation of the well history, or at least a part of the history, would provide reliable conclusions on such changes. Choosing a proper model to describe the well, the reservoir, and boundaries is crucial for analysis and forecasting and for drawing conclusions. The usual practice is that the chosen model should represent basic well and reservoir features that are known before the analysis, such as well type; reference fluid and stimulation performed; and well environment, including neighboring wells and faults.

A semi-analytical solution to the transient temperature behavior along the vertical wellbore after well shut-in

Temperature measured from a permanent downhole gauge in a well after the well shut-in provides a unique perspective for surveillance and production management. For example, in deepwater environment, production engineer needs to know the temperature of a shut-in well before it is being restarted since different temperature profiles along the wellbore may need very different restarting strategies for flow assurance purpose. A number from the “rule-of-thumb” or running complicated numerical simulation is either not reliable or impractical. This paper presents a semi-analytical model to estimate the temperature transient behavior after the well shut-in. The temperature at a point along the wellbore, which is useful for production management and flow assurance, is solved and the solution can be easily implemented for calculating temperature history. The temperature profile solution for locations along the wellbore is also presented in the paper. Furthermore, matching the calculated temperature with the measured temperature at the same location will yield the well local heat transmissibility coefficient. The applications of the solution to this model are multiple folders and a deepwater field example is provided to demonstrate some applications and valid the results.

Creating Value With Permanent Downhole Gauges in Tight Gas Appraisal Wells

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 164039, ’Creating Value Through Permanent Downhole Gauges in Oman Block A Tight Gas Appraisal,’ by Badar Al Busafi, SPE, Robert A. Clark Jr., SPE, and Imtiaz Adil, SPE, BP, prepared for the 2013 SPE Middle East Unconventional Gas Conference and Exhibition, Muscat, Oman, 28-30 January. The paper has not been peer reviewed. Downhole instrumentation of the Khazzan tight gas appraisal wells provides a rare opportunity to quantify reservoir pressure and temperature dynamics. Several appraisal wells were tested initially for 3–4 weeks and subsequently shut in for approximately 1 year. The continuous downhole-gauge recordings of the resulting pressure buildups were then analyzed to quantify understanding of stimulation effectiveness, reservoir quality away from the wellbore, total producible connected gas, geomechanics, and wellbore hydraulics. This paper examines the pressure-transient analysis (PTA) of one representative well in the field. Value of Permanent Downhole Gauges in Tight Gas Dual permanent downhole gauges (PDHGs) were spaced approximately 100 m apart in the upper- completion design, close to the perforations, to fulfill various surveillance requirements and provide the ability to measure gradients in the wellbore in real time. The completion could be tested with PDHGs and surface gauges. A considerable effort was made to ensure that the completions could withstand multiple high-pressure fracture-stimulation jobs. This included installing polished-bore packers and special-thickness tubulars. Preplanning of fracture designs using a variety of friction factors and likely fracture gradients was performed to narrow options and design the completions. Monobore completions were planned to enable running various plugs to isolate the pay intervals as required and to ease the running of logs, tools, and coiled tubing into the wellbore. The size of the completions was optimized for fracturing, not for fluid flow. Monitoring Hydraulic-Fracturing-Job Performance. PDHGs have proved to be of considerable value in real-time monitoring of downhole treating pressure in stimulation jobs. For example, early detection of screenout events (before any indication of pressure problems was identified on the surface) has been demonstrated. In one case, a job was altered while pumping and proppant injection was halted before the wellbore actually screened out (Fig. 1). The surface pressure data did not reveal the pending screenout. Only a very small amount of proppant remained in the wellbore after halting pumping operations.

Parallel simulation of fully-coupled thermal-hydro-mechanical processes in CO2 leakage through fluid-driven fracture zones

The safety of CO2 storage in geological formations relies on the integrity of the caprock. However, the elevated fluid pressure during CO2 injection changes the stress states in the caprock, and may lead to reactivate pre-existing fractures or even fracture the caprock. It is necessary to develop an efficient and practical monitor technology to detect and identify CO2 leakage pathways. To this end, we should understand the transport behavior of CO2 coupled with geomechanical effects during injection. In this work, we first developed an efficient parallel fully-coupled thermal-hydro-mechanical simulator to model CO2 transport in porous media. The numerical model was verified through classical problems with analytical solutions. Then, based on this simulator, we investigated the fluid flow behavior when CO2 leakage occurs through fluid-driven fracture zones. We proposed an implicit, physics-based model to simulate the fluid-driven fracturing process by using several practical correlations, including fracturing pressure functions, porosity/permeability–stress relationships. A set of numerical simulations have been conducted by considering various scenarios, such as different injection rates, locations and distributions of fracture zones, and initial fracture permeability. The results show that there are several characteristics can be used to detect CO2 leakage pathways, and it is possible to develop an advanced inverse modeling and monitoring technology to identify leakage locations, times and rates using measured pressure data of permanent downhole gauges and our simulator.