Download or read book Field Application of an Interpretation Method of Downhole Temperature and Pressure Data for Detecting Water Entry in Horizontal highly Inclined Gas Wells written by Ochi I. Achinivu and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the oil and gas industry today, continuous wellbore data can be obtained with high precision. This accurate and reliable downhole data acquisition is made possible by advancements in permanent monitoring systems such as downhole pressure and temperature gauges and fiber optic sensors. The monitoring instruments are increasingly incorporated as part of the intelligent completion in oil wells where they provide bottomhole temperature, pressure and sometimes volumetric flow rate along the wellbore - offering the promise of revolutionary changes in the way these wells are operated. However, to fully realize the value of these intelligent completions, there is a need for a systematic data analysis process to interpret accurately and efficiently the raw data being acquired. This process will improve our understanding of the reservoir and production conditions and enable us make decisions for well control and well performance optimization. In this study, we evaluated the practical application of an interpretation model, developed in a previous research work, to field data. To achieve the objectives, we developed a simple and detailed analysis procedure and built Excel user interface for data entry, data update and data output, including diagnostic charts and graphs. By applying our interpretation procedure to the acquired field data we predicted temperature and pressure along the wellbore. Based on the predicted data, we used an inversion method to infer the flow profile - demonstrating how the monitored raw downhole temperature and pressure can be converted into useful knowledge of the phase flow profiles and fluid entry along the wellbore. Finally, we illustrated the sensitivity of reservoir parameters on accuracy of interpretation, and generated practical guidelines on how to initialize the inverse process. Field production logging data were used for validation and application purposes. From the analysis, we obtained the production profile along the wellbore; the fluid entry location i.e. the productive and non-productive locations along the wellbore; and identified the fluid type i.e. gas or water being produced along the wellbore. These results show that temperature and pressure profiles could provide sufficient information for fluid identity and inflow distribution in gas wells.

Download or read book Detection of Water Or Gas Entry Into Horizontal Wells by Using Permanent Downhole Monitoring Systems written by Keita Yoshioka and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With the recent development of temperature measurement systems, continuous wellbore temperature profiles can be obtained with high precision. Small temperature changes can be detected by modern temperature-measuring instruments, such as fiber optic distributed temperature sensors (DTS) in intelligent completions. Analyzing such changes will potentially aid the diagnosis of downhole flow conditions. In vertical wells, temperature logs have been used successfully to diagnose the downhole flow conditions because geothermal temperature differences in depth make the wellbore temperature sensitive to the amount and the type of fluids flowing in the wellbore. Geothermal temperature does not change, however, along a horizontal wellbore, which leads to small temperature variations in horizontal wells, and interpretations of temperature profiles become harder to make than those for vertical wells. For horizontal wells, the primary temperature differences are caused by frictional effects. Therefore, in developing a thermal model for producing horizontal wellbore, subtle temperature changes should be accounted for. This study rigorously derives governing equations for thermal reservoir and wellbore flow and develops a prediction model of temperature and pressure. With the prediction model developed, inversion studies of synthetic and field examples are presented. These results are essential to identify water or gas entry, to guide the flow control devices in intelligent completions, and to decide if reservoir stimulation is needed in particular horizontal sections. This study will complete and validate these inversion studies. The utility and effect of temperature and pressure measurement in horizontal wells for flow condition interpretation have been demonstrated through synthetic and field examples.

Download or read book Interpreting Horizontal Well Flow Profiles and Optimizing Well Performance by Downhole Temperature and Pressure Data written by Zhuoyi Li and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Horizontal well temperature and pressure distributions can be measured by production logging or downhole permanent sensors, such as fiber optic distributed temperature sensors (DTS). Correct interpretation of temperature and pressure data can be used to obtain downhole flow conditions, which is key information to control and optimize horizontal well production. However, the fluid flow in the reservoir is often multiphase and complex, which makes temperature and pressure interpretation very difficult. In addition, the continuous measurement provides transient temperature behavior which increases the complexity of the problem. To interpret these measured data correctly, a comprehensive model is required. In this study, an interpretation model is developed to predict flow profile of a horizontal well from downhole temperature and pressure measurement. The model consists of a wellbore model and a reservoir model. The reservoir model can handle transient, multiphase flow and it includes a flow model and a thermal model. The calculation of the reservoir flow model is based on the streamline simulation and the calculation of reservoir thermal model is based on the finite difference method. The reservoir thermal model includes thermal expansion and viscous dissipation heating which can reflect small temperature changes caused by pressure difference. We combine the reservoir model with a horizontal well flow and temperature model as the forward model. Based on this forward model, by making the forward calculated temperature and pressure match the observed data, we can inverse temperature and pressure data to downhole flow rate profiles. Two commonly used inversion methods, Levenberg- Marquardt method and Marcov chain Monte Carlo method, are discussed in the study. Field applications illustrate the feasibility of using this model to interpret the field measured data and assist production optimization. The reservoir model also reveals the relationship between temperature behavior and reservoir permeability characteristic. The measured temperature information can help us to characterize a reservoir when the reservoir modeling is done only with limited information. The transient temperature information can be used in horizontal well optimization by controlling the flow rate until favorite temperature distribution is achieved. With temperature feedback and inflow control valves (ICVs), we developed a procedure of using DTS data to optimize horizontal well performance. The synthetic examples show that this method is useful at a certain level of temperature resolution and data noise.

Download or read book Petroleum Abstracts written by and published by . This book was released on 1998 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Petroleum Abstracts Literature and Patents written by and published by . This book was released on 1989 with total page 1528 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Uses of Distributed Temperature Survey DTS Data written by Zhe Wang and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Temperature change plays an important role in many downhole processes, and temperature measurements have long been used to monitor the performance of producing wells, evaluate water-injection profiles and diagnose the effectiveness of fracture jobs, etc. However, for many years, the utilization of downhole temperature measurement was largely over-shadowed by other measurements obtained through sophisticated suites of logging tools. However, the development of fiber-optic technology has helped a resurgence of interest in temperature measurement. One characteristic of fiber-optic temperature measurement is that it is capable of measuring multiple points simultaneously. The fiber-optic tool used to measure temperature is called a Distributed Temperate Survey (DTS), which measures temperature along the whole interval covered by the fiber. In our study, we explored approaches of how to interpret DTS data. The significant contributions of this work include: 1. Building a wellbore/reservoir coupled thermal model The need to interpret wellbore temperature profiles measured by Distributed Temperature Sensors (DTS) requires a correspondingly sophisticated type of well model. To be specific, this model should be capable of predicting pressure and temperature distributions under a nonisothermal, multicomponent and multiphase production scenario. In our model, wellbore pressure and temperature were solved separately and then coupled by iteration. Accuracy was assured by the following three ways: a. Using the drift flux model to predict multiphase flow pressure; b. Fluid PVT properties were obtained by solving Equations of State, which is more accurate than the values obtained by averaging or mixing rules; c. Using numerical methods to solve the heat transfer between wellbore and formation, avoiding the assumption of an invariant relaxation length. This model was verified by comparing with several previously published models. 2. Estimating flowrate profile from temperature profile measurement Measuring flowrate profile can be a challenging job for traditional single-point flowrate measurement tools, and it becomes very unreliable especially for multi- phase flow and complex well geometries. However, temperature profile provides an alternative approach for measuring flowrate profiles. As the temperature in the wellbore is influenced by the properties and flowrate of the inflows from different entry points, measured temperature can be used to estimate flowrate. Therefore, the DTS data are very valuable for estimating flowrate profiles. In our study, we used two different inverse methods separately. Although the philosophy and performance of these two methods are different, both succeed in estimating flowrate profile from the temperature profile. The multiphase case was also considered, in which we found that it requires more input information than just temperature data to achieve a good estimate of flowrate profile. 3. Evaluating formation properties Temperature is also a function of formation properties, and thus it can be used to evaluate the formation. We found that temperature data are more sensitive to the properties in the near-well formation than pressure data. This finding is confirmed by our study on several different cases of single-layer reservoirs. Furthermore, multilayer and horizontal wells, both of which have multiple entry points in the well, were also studied. Finally, a successful analysis of a real case was helpful to verify our findings.

Download or read book Abnormal Pressures While Drilling written by Jean-Paul Mouchet and published by Editions TECHNIP. This book was released on 1989 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Comprehensive Statistically Based Method to Interpret Real Time Flowing Measurements written by and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With the recent development of temperature measurement systems, continuous temperature profiles can be obtained with high precision. Small temperature changes can be detected by modern temperature measuring instruments such as fiber optic distributed temperature sensor (DTS) in intelligent completions and will potentially aid the diagnosis of downhole flow conditions. In vertical wells, since elevational geothermal changes make the wellbore temperature sensitive to the amount and the type of fluids produced, temperature logs can be used successfully to diagnose the downhole flow conditions. However, geothermal temperature changes along the wellbore being small for horizontal wells, interpretations of a temperature log become difficult. The primary temperature differences for each phase (oil, water, and gas) are caused by frictional effects. Therefore, in developing a thermal model for horizontal wellbore, subtle temperature changes must be accounted for. In this project, we have rigorously derived governing equations for a producing horizontal wellbore and developed a prediction model of the temperature and pressure by coupling the wellbore and reservoir equations. Also, we applied Ramey's model (1962) to the build section and used an energy balance to infer the temperature profile at the junction. The multilateral wellbore temperature model was applied to a wide range of cases at varying fluid thermal properties, absolute values of temperature and pressure, geothermal gradients, flow rates from each lateral, and the trajectories of each build section. With the prediction models developed, we present inversion studies of synthetic and field examples. These results are essential to identify water or gas entry, to guide flow control devices in intelligent completions, and to decide if reservoir stimulation is needed in particular horizontal sections. This study will complete and validate these inversion studies.

Download or read book Application of the Back pressure Method for Determining Absolute Open Flows of Large Gas Wells written by Morris A. Schellhardt and published by . This book was released on 1944 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Utilizing Distributed Temperature Sensors in Predicting Flow Rates in Multilateral Wells written by Jassim Mohammed A. Al Mulla and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The new advancement in well monitoring tools have increased the amount of data that could be retrieved with great accuracy. Downhole pressure and temperature could be precisely determined now by using modern instruments. The new challenge that we are facing today is to maximize the benefits of the large amount of data that is being provided by these tools and thus justify the investment of more capital in such gadgets. One of these benefits is to utilize the continuous stream of temperature and pressure data to determine the flow rate in real time out of a multilateral well. Temperature and pressure changes are harder to predict in horizontal laterals compared with vertical wells because of the lack of variation in elevation and geothermal gradient. Thus the need of accurate and high precision gauges becomes critical. The trade-off of high resolution sensors is the related cost and resulting complication in modeling. Interpreting measured data at real-time to a downhole flow profile in multilateral and horizontal wells for production optimization is another challenge. In this study, a theoretical model is developed to predict temperature and pressure in trilateral wells based on given flow conditions. The model is used as a forward engine in the study and inversion procedure is then added to interpret the data to flow profiles. The forward model starts from an assumed well flow pressure in a specified reservoir with a defined well structure. Pressure, temperature and flow rate in the well system are calculated in the motherbore and in the laterals. These predicted temperature and pressure profiles provide the connection between the flow conditions and the temperature and pressure behavior. Then we use an inverse model to interpret the flow rate profiles from the temperature and pressure data measured by the downhole sensors. A gradient-based inversion algorithm is used in this work, which is fast and applicable for real-time monitoring of production performance. In the inverse model, the flow profile is calculated until the one that generates the matching temperature and pressure profiles in the well is identified. The production distribution from each lateral is determined based on this approach. At the end of the study, the results showed that we were able to successfully predict flow rates in the field within 10% of the actual rate. We then used the model to optimize completion design in the field. In conclusion, we were able to build a dependable model capable of predicting flow rates in trilateral wells using pressure and temperature data provided by downhole sensors.

Download or read book Temperature Prediction Model for a Producing Horizontal Well written by Pinan Dawkrajai and published by . This book was released on 2006 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: We further develop a numerical temperature model of a bottom water drive reservoir to demonstrate the uses of temperature profiles in detecting water entries. Water in this numerical model is initially located in a deeper and warmer zone below a horizontal well.

Download or read book Back Pressure Data on Natural Gas Wells and Their Application to Production Practices written by Edwin Lee Rawlins and published by . This book was released on 2013-03 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: United States Department Of The Interior, Bureau Of Mines, Monograph 7.

Download or read book A COMPREHENSIVE STATISTICALLY BASED METHOD TO INTERPRET REAL TIME FLOWING MEASUREMENTS written by A. D. Hill and published by . This book was released on 2004 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this project, we are developing new methods for interpreting measurements in complex wells (horizontal, multilateral and multi-branching wells) to determine the profiles of oil, gas, and water entry. These methods are needed to take full advantage of ''smart'' well instrumentation, a technology that is rapidly evolving to provide the ability to continuously and permanently monitor downhole temperature, pressure, volumetric flow rate, and perhaps other fluid flow properties at many locations along a wellbore; and hence, to control and optimize well performance. In this first year, we have made considerable progress in the development of the forward model of temperature and pressure behavior in complex wells. In this period, we have progressed on three major parts of the forward problem of predicting the temperature and pressure behavior in complex wells. These three parts are the temperature and pressure behaviors in the reservoir near the wellbore, in the wellbore or laterals in the producing intervals, and in the build sections connecting the laterals, respectively. Many models exist to predict pressure behavior in reservoirs and wells, but these are almost always isothermal models. To predict temperature behavior we derived general mass, momentum, and energy balance equations for these parts of the complex well system. Analytical solutions for the reservoir and wellbore parts for certain special conditions show the magnitude of thermal effects that could occur. Our preliminary sensitivity analyses show that thermal effects caused by near-wellbore reservoir flow can cause temperature changes that are measurable with smart well technology. This is encouraging for the further development of the inverse model.

Download or read book Interpreting Pressure and Flow Rate Data from Permanent Downhole Gauges Using Data Mining Approaches written by Yang Liu and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Permanent Downhole Gauge (PDG) is a promising resource for real time downhole measurement. However, a bottleneck in utilizing the PDG data is that the commonly applied well test methods are limited (practically) to short sections of shut-in data only and thus fail to utilize the long term PDG data. Recent technology developments have provided the ability for PDGs to measure both flow rate and pressure, so the limitation of using only shut-in periods could be avoided, theoretically. In practice however it is still difficult to make use of the combined flow rate and pressure data over a PDG record of long duration, due to the noise in both of the signals as well as uncertainty with respect to the appropriate reservoir model over such a long period. The successful application of data mining in computer science shows great potential in revealing the relationship between variables from voluminous data sets. This inspired us to investigate the application of data mining methodologies as a way to reveal the relationship between flow rate and pressure histories from PDG data, and hence extract the reservoir model. In this study, nonparametric kernel-based data mining approaches were studied. The data mining process was conducted in two stages, namely learning and prediction. In the learning process, the reservoir model was obtained implicitly in a suitable functional form in the high-dimensional kernel Hilbert space (defined by the kernel function) when the learning algorithm converged after being trained to the pressure and flow rate data. In the prediction process, a pressure prediction was made by the data mining algorithm according to an arbitrary flow rate history (usually a constant flow rate history for simplicity). This flow rate history and the corresponding pressure prediction revealed the reservoir model underlying the variable PDG data. In a second mode, recalculating the pressure history based on the measured flow rate history removed noise from the pressure signal effectively. Recalculating the pressure based on a denoised flow rate history removed noise from both signals. In the work, a series of data mining methods using different kernel functions and input vectors were investigated. Methods A, B, and C utilized simple kernel functions. Method A and Method B did not require the knowledge of breakpoints in advance. The difference between the two was that Method A used a low-order kernel function with a high-order input vector, while Method B used a high-order kernel function with a low-order input vector. Method C required the knowledge of the breakpoints. Nine synthetic test cases with different well/reservoir models were used to test these methods. The results showed that all three methods have good pressure reproduction of the training flow rate history and pressure prediction of the constant flow rate history. However, each of them has limitations in different aspects. The limitation of the simple kernel methods led us to a reconsideration of kernelization and superposition. In the simple kernel methods, the kernelization was deployed over the superposition which was reflected as the summation in the input vector. However, the architecture of superposition over kernelization would be more suitable to capture the essence of the transient, and this approach was implemented by using a convolution kernel in Method D. The convolution kernel was invented and applied in the domain of natural language machine learning. In the original linguistic study, the convolution kernel decomposed words into parts, and evaluated the parts using a simple kernel function. This inspired us to apply the convolution kernel method to PDG data by decomposing the pressure transient into a series of pressure responses to the previous flow rate change events. The superposition was then reflected as the summation of simple kernels (hence superposition over kernelization). 16 synthetic and real field test cases were tested using this approach. The method recovered the reservoir model successfully in all cases. By comparison, Method D outperformed all simple kernel methods for its stability and accuracy in all test cases without knowing the breakpoints in advance. This study also discussed the performance of Method D working under complicated data situations, including the existence of significant outliers and aberrant segments, incomplete production history, unknown initial pressure, different sampling frequencies, and different time spans of the data set. The results suggested that: 1) Method D tolerated a moderate level of outliers and aberrant segments without any preprocessing; 2) Method D might reveal the reservoir/well model with effective rate correction and/or optimization on initial pressure value when the production history was incomplete and/or when the initial pressure was unknown; and 3) an appropriate sampling frequency and time span of the data set were required to ensure the sufficiency of the basis functions in the Hilbert kernel space. In order to improve the performance of the convolution kernel method in dealing with large data sets, two block algorithms, namely Methods E and F, were also investigated. The two methods rescaled the original kernel matrix into a series of block matrices, and used only some of the blocks to complete the training process. A series of synthetic cases and real cases illustrated their efficiency and accuracy. The comparison of the performance between Methods D, E, and F was also conducted.

Download or read book Utilizing Distributed Temperature and Pressure Data to Evaluate the Production Distribution in Multilateral Wells written by Rashad Al Zahrani and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the issues with multilateral wells is determining the contribution of each lateral to the total production that is measured at the surface. Also, if water is detected at the surface or if the multilateral well performance declines, then it is difficult to identify which lateral or laterals are causing the production decline. One way to estimate the contribution from each lateral is to run production Logging Tools (PLT). Unfortunately, PLT jobs are expensive, time-consuming, labor-intensive and involve operational risks. An alternative way to measure the production from each lateral is to use Distributed Temperature Sensing (DTS) technology. Recent advances in DTS technology enable measuring the temperature profile in horizontal wells with high precision and resolution. The changes in the temperature profile are successfully used to calculate the production profile in horizontal wells. In this research, we develop a computer program that uses a multilateral well model to calculate the pressure and temperature profile in the motherbore. The results help understand the temperature and pressure behaviors in multilateral wells that are crucial in designing and optimizing DTS installations. Also, this model can be coupled with an inversion model that can use the measured temperature and pressure profile to calculate the production from each lateral. Our model shows that changing the permeability or the water cut produced from one lateral results in a clear signature in the motherbore temperature profile that can be measured with DTS technology. However, varying the length of one of the lateral did not seem to impact the temperature profile in the motherbore. For future work, this research recommends developing a numerical reservoir model that would enable studying the effect of lateral inference and reservoir heterogeneity. Also recommended is developing an inversion model that can be used to validate our model using field data.

Download or read book Downhole Pressure Temperature and Flowrate Measurements in Steam Wells at the Geysers Field written by and published by . This book was released on 1988 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently developed pressure-temperature-spinner (PTS) tools are used to collect reliable downhole measurements in geothermal systems, such as at The Geysers. PTS surveys in several flowing Geysers steam wells were used to quantify steam entry location and magnitude, wellbore heat loss, pressure drop due to friction, thermodynamic properties of the steam, and maximum rock temperature. Interwell cross flow/interference was identified in one well. Finally, a single-phase saturated steam wellbore model used to compare calculated to measured downhole values, was found to adequately predict the flowing pressure versus depth curves in vapor filled holes.

Download or read book Analytical and Data Processing Techniques for Interpretation of Geophysical Survey Data with Special Application to Cavity Detection written by and published by . This book was released on 1982 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: