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Trade magazines and review articles describe MWD in casual terms, e.g., positive versus negative pulsers, continuous wave systems, drilling channel noise and attenuation, in very simple terms absent of technical rigor. However, few truly scientific discussions are available on existing methods, let alone the advances necessary for high-data-rate telemetry. Without a strong foundation building on solid acoustic principles, rigorous mathematics, and of course, fast, inexpensive and efficient testing of mechanical designs, low data rates will impose unacceptable quality issues to real-time formation evaluation for years to come. This all-new revised second edition of an instant classic promises to change all of this. The lead author and M.I.T.-educated scientist, Wilson Chin, has written the only book available that develops mud pulse telemetry from first principles, adapting sound acoustic principles to rigorous signal processing and efficient wind tunnel testing. In fact, the methods and telemetry principles developed in the book were recently adopted by one of the world's largest industrial corporations in its mission to redefine the face of MWD.

Mathematically rigorous, computationally fast, and easy to use, this new approach to electromagnetic well logging gives the reservoir engineer a new dimension to MWD/LWD interpretation and tool design Almost all publications on borehole electromagnetics deal with idealizations that are not acceptable physically. On the other hand, \"exact models\" are only available through detailed finite element or finite difference analysis, and more often than not, simply describe case studies for special applications. In either case, the models are not available for general use and the value of the publications is questionable. This new approach provides a rigorous, fully three-dimensional solution to the general problem, developed over almost two decades by a researcher familiar with practical applications and mathematical modeling. Completely validated against exact solutions and physics-based checks through over a hundred documented examples, the self-contained model (with special built-in matrix solvers and iteration algorithms) with a \"plain English graphical user interface\" has been optimized to run extremely fast – seconds per run as opposed to minutes and hours – and then automatically presents all electric and magnetic field results through integrated three-dimensional color graphics. In addition to state-of-the-art algorithms, basic \"utility programs\" are also developed, such as simple dipole methods, Biot-Savart large diameter models, nonlinear phase and amplitude interpolation algorithms, and so on. Incredibly useful to oilfield practitioners, this volume is a must-have for serious professionals in the field, and all the algorithms have undergone a laborious validation process with real use in the field.

This book promises to change all of this. The lead author and M.I.T. educated scientist, Wilson Chin, and Yinao Su, Academician, Chinese Academy of Engineering, and other team members, have written the only book available that develops mud pulse telemetry from first principles, adapting sound acoustic principles to rigorous signal processing and efficient wind tunnel testing. In fact, the methods and telemetry principles developed in the book were recently adopted by one of the world’s largest industrial corporations in its mission to redefine the face of MWD.

Resistivity logging represents the cornerstone of modern petroleum exploration, providing a quantitative assessment of hydrocarbon bearing potential in newly discovered oilfields. Resistivity is measured using AC coil tools, as well as by focused DC laterolog and micro-pad devices, and later extrapolated, to provide oil saturation estimates related to economic productivity and cash flow. Interpretation and modeling methods, highly lucrative, are shrouded in secrecy by oil service companies - often these models are incorrect and mistakes perpetuate themselves over time. This book develops math modeling methods for layered, anisotropic media, providing algorithms, validations and numerous examples. New electric current tracing tools are also constructed which show how well (or poorly) DC tools probe intended anisotropic formations at different dip angles. The approaches discussed provide readers with new insights into the limitations of conventional tools and methods, and offer practical and rigorous solutions to several classes of problems explored in the book. Traditionally, Archie's law is used to relate resistivity to water saturation, but only on small core-sample spatial scales. The second half of this book introduces methods to calculate field-wide water saturations using modern Darcy flow approaches, and then, via Archie's law, develops field-wide resistivity distributions which may vary with time. How large-scale resistivity distributions can be used in more accurate tool interpretation and reservoir characterization is considered at length. The book also develops new methods in \"time lapse logging,\" where timewise changes to resistivity response (arising from fluid movements) can be used to predict rock and fluid flow properties.

Because the dielectric constant of water is greater than that of oil and gas, dielectric logging sensors can effectively distinguish oil and gas reservoirs from water layers by measuring the dielectric parameters of formations. Under the special working conditions during the logging of boreholes drilled for oil and gas exploration, such as high temperature and pressure and a narrow working space, the endurance and effectiveness of the antenna in the dielectric logging sensor are crucial. This paper presents a design method for a dual-polarization slot antenna for such working conditions. We theoretically analyzed the working principle of this antenna, established the antenna model, and evaluated its radiation characteristics through simulation. Subsequently, we developed and tested the proposed antenna. The antenna could withstand a high temperature and pressure of 175 °C and 140 MPa, respectively. A dielectric logging sensor using the proposed antenna was successfully applied in oilfield logging.

The eighth member of Upper Triassic Yanchang Formation (Chang 8) is the major oil reservoir unit in Jiyuan oil field, though with the high potential for oil exploration. The Chang 8 sandstones are characterized with low porosity, low permeability and strong microscopic heterogeneities due to the complex deep-burial diagenetic history. Detailed petrological studies by thin section, X-ray diffraction, scanning electron microscopy, core analysis have been used to investigate the lithogology characteristics, diagenesis, diagenetic minerals and their coupling impacts on reservoir property. The results show that Chang 8 sandstones comprise fine to mediumgrained subarkoses, feldspathic litharenites. The pore systems are dominated by remaining primary intergranular pores, secondary dissolution porosity and micropores. Then, five diagenetic facies were divided in Chang 8 sandstones based on the type and degree of diagenesis, diagenetic minerals assemblages and their coupling effects on the reservoir quality. They consist of grain-coating chlorite weak dissolution facies, unstable component dissolution facies, tight compaction facies, clay minerals filling facies and carbonate cementation facies. The well logging response characteristics of various diagenetic facies are summarized on Gamma Ray (GR), Density Logging (DEN), Acoustic (AC), Compensated Neutron Logging (CNL), and True Formation Resistivity (RT) by translating diagenetic facies to well log responses, the diagenetic facies were defined by a set of log responses, and porosity, permeability ranges for each diagenetic facies were determined from core analyses. Well log data of Luo 13 and Chi 212 are processed to evaluate the accuracy of the predictive model. The diagenetic facies are predicted on the vertical profile based on the generated model. Predicted distribution of diagenetic facies precisely coincide with the microscopic observations, and diagenetic facies in Chang 8 sandstones are generally locally distributed. Tight compaction and carbonate cementation diagenetic facies mainly correspond to the non-reservoir and dry layers of Chang 8 sandstones, layers with higher oil potentials are mainly developed in the grain-coating chlorite weak dissolution and unstable component dissolution diagenetic facies. By translating diagenetic facies to well log responses, diagenetic facies and reservoir properties of intervals that lack core control could be predicted with the same or similar log responses. Le huitième membre de la formation du Trias supérieur de Yangchang (Chang 8) est le principal gisement de pétrole du champ pétrolifère de Jiyuan. Bien qu’offrant un potentiel élevé de prospection pétrolière, les grès de Chang 8 sont caractérisés par une faible porosité, une faible perméabilité et une importante hétérogénéité microscopique dues à une histoire diagénétique complexe liée à un enfouissement profond. Des analyses pétrologiques détaillées de lames minces (par diffractométrie de rayons X, par microscopie à balayage électronique) et de carottes ont été utilisées pour étudier les caractéristiques lithologiques, la diagenèse, les minéraux diagénétiques et leurs incidences sur les propriétés du réservoir. Les résultats indiquent que les grès de Chang 8 sont constitués de litharénites feldspathiques, subarkoses, de taille de grain fine à moyenne. Les systèmes poreux sont dominés par des pores primaires intergranulaires résiduels, une porosité secondaire de dissolution et des micropores. Cinq faciès diagénétiques des grès de Chang 8 ont ainsi été déterminés, selon leur type et degré de diagenèse, les assemblages des matériaux diagénétiques et leurs incidences sur la qualité du réservoir. Ils consistent en un faciès de dissolution faible à tapissage argileux de chlorites, un faciès de dissolution à composante instable, un faciès dense de compaction, un faciès de remplissage en matières argileuses et un faciès de cémentation carbonaté. Les caractéristiques de réponse des diagraphies de puits de divers faciès diagénétiques ont été résumées en des données Gamma Ray (GR), Density Logging (DEN), Acoustic (AC), Compensated Neutron Logging (CNL), et True Formation Resistivity (RT), en convertissant les faciès diagénétiques en des réponses de diagraphie de puits. Les faciès diagénétiques ont été définis par une série de réponses de diagraphie, tandis que leurs gammes de perméabilité et de porosité ont été déterminées à partir d’analyses de carottes. Les données de diagraphie des puits Luo 13 et Chi 212 ont été traitées afin d’évaluer la précision du modèle prédictif. Le faciès diagénétique est prédit sur un profil vertical à partir du modèle généré. La distribution des faciès diagénétiques ainsi obtenue coïncide avec les observations microscopiques, et les faciès diagénétiques des grès de Chang 8 sont généralement localement distribués. Les faciès denses de compaction et de cémentation carbonatée correspondent principalement aux couches non-réservoir et sèches des grès de Chang 8, les couches disposant d’un potentiel pétrolifère plus élevé étant principalement constituées des faciès de dissolution faible à tapissage argileux de chlorite et de remplissage en matières argileuses. En convertissant les faciès diagénétiques en des réponses de diagraphie de puits, les faciès diagénétiques et les propriétés de réservoir peuvent être prédits à partir des réponses des diagraphies ou de réponses similaires, sans besoin de contrôle par carottage.

Detection of the oil–water encroachment front is of great importance to water injection of oil reservoirs. In borehole-to-surface electrical imaging (BSEI), a high-power direct current is applied into a borehole through a well case and the electric potential on the surface, which is affected by the subsurface electrical change, is measured. However, further accurate interpretation of BSEI data is difficult due to the weak surface response, deep target layer, long inversion time, and uncertainty in unconstrained inversion. Therefore, a new method to enhance the surface response of the anomalous body and a new three-dimensional inversion approach based on the damped least-squares method are proposed. Simulation of the water injection and fracturing process was modeled in three dimensions using the finite difference method and the incomplete Cholesky conjugate gradient method. The inversion approach was applied by using the log data to construct a layered resistivity model and constrain the inversion. The forward modeling results suggest that the electric potential gradient can enhance the response of electrical variations in the target layer and help estimate the water injection direction, depending on the distance of electrical anomalies and the current source. In actual water injection monitoring, the BSEI inversion results suggest that layered resistivity model constrained three-dimensional inversion can improve the precision and accuracy of the resistivity inversion results and outline the water injection channeled to the adjacent wells.

The responses of logging-while-drilling (LWD) electromagnetic resistivity logging in horizontal wells are usually complex due to the effect of the strong heterogeneity and anisotropy of the shale oil reservoir. Therefore, the qualitative interpretation and quantitative evaluation of such data are still challenging. To this end, a correction method is developed for the LWD resistivity logging data acquired from a horizontal well for the shale oil reservoir. First, the electrical interpretation model, which considers the formation resistivity properties and various mud types, is established to accommodate the shale oil formation and horizontal well environment. The effects of the borehole on the measured signals, that is, phase shift and attenuation ratio, are simulated. The relationship between the complicated tool and point source responses in a homogeneous medium is established to suppress these effects. The point-by-point real-time anisotropy correction method is developed for the LWD resistivity data from thick beds based on this borehole correction process and the analytical algorithm. The boundary positions and relative dipping information for the thin formations should be obtained from the mapping logging data. Finally, the horizontal and vertical resistivities of each layer are extracted using the one-dimensional (1D) formation model in association with the gradient optimization algorithm. Numerical results show that the LWD resistivity data after the borehole correction can be simplified to 1D, laying the foundations for the fast data process. Moreover, the horizontal and vertical resistivities of shale oil beds are accurately determined using the anisotropy correction of LWD resistivity data from horizontal wells, thereby providing reliable electrical parameters for the interpretation and quantitative evaluation of the shale oil reservoir.

With the continued development of natural gas extraction technologies, the accurate determination of downhole temperature and pressure has become increasingly important. It is crucial for the optimization of gas well production and an important measure to prevent accidents. However, existing logging instruments have a series of deficiencies in measurement and cannot adequately monitor modern natural gas. In response to these problems, in this paper, we propose a new model-based systematic innovation design method for designing logging instruments and simulations using finite element software. Our research results confirm the theoretical and practical utility of this model-based design method and provide a novel approach to designing logging instruments.

Geophysical logging curves are crucial for oil and gas field exploration and development, and curve reconstruction techniques are a key focus of research in this field. This study proposes an inversion model for deep resistivity curves in marine carbonate reservoirs, specifically the Mishrif Formation of the Halfaya Field, by integrating a deep learning model called CNN-GRU-ATT, which combines Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and the Attention Mechanism (ATT). Using logging data from the marine carbonate oil layers, the reconstructed deep resistivity curve is compared with actual measurements to determine reservoir fluid properties. The results demonstrate the effectiveness of the CNN-GRU-ATT model in accurately reconstructing deep resistivity curves for carbonate reservoirs within the Mishrif Formation. Notably, the model outperforms alternative methods such as CNN-GRU, GRU, Long Short-Term Memory (LSTM), Multiple Regression, and Random Forest in new wells, exhibiting high accuracy and robust generalization capabilities. In practical applications, the response of the inverted deep resistivity curve can be utilized to identify the reservoir water cut. Specifically, when the model-inverted curve exhibits a higher response compared to the measured curve, it indicates the presence of reservoir water. Additionally, a stable relative position between the two curves suggests the presence of a water layer. Utilizing this method, the oil–water transition zone can be accurately delineated, achieving a fluid property identification accuracy of 93.14%. This study not only introduces a novel curve reconstruction method but also presents a precise approach to identifying reservoir fluid properties. These findings establish a solid technical foundation for decision-making support in oilfield development.