Real-time Log Data Research Articles

Real-Time Drilling Parameter Optimization Model Based on the Constrained Bayesian Method

To solve the problems of the low energy efficiency and slow penetration rate of drilling, we took the geological data of adjacent wells, real-time logging data, and downhole engineering parameters as inputs; the mechanical specific energy and unit footage cost as multi-objective optimization functions; and the machine pump equipment limit as the constraint condition. A constrained Bayesian optimization algorithm model was established for the optimization solution, and drilling parameters such as weight-of-bit, revolutions per minute, and flowrate were optimized in real time. Through a comparison with NSGA-II, random search, and other optimization algorithms, and the application results of example wells, we show that the established Bayesian optimization algorithm has a good optimization effect while maintaining timeliness. It is suitable for real-time optimization of drilling parameters, can aid a driller in identifying the drilling rate and potential tapping area, and provides a decision-making basis for avoiding the low-efficiency rock-breaking working area and improving rock-breaking efficiency.

RANCANG BANGUN GROUND STATION DENGAN TAMPILAN GRAPHICAL USER INTERFACE PADA WAHANA ROKET BERBASIS PROCESSING DAN MENGGUNAKAN ATMEGA 328

The design of the Ground Station with a Graphical User Interface display on a Processingbasedrocket vehicle and using the ATmega328 is designed to be able to assist in monitoringthe environment of the rocket vehicle on the Ground Station with a Graphical User Interfacedisplay. The monitored environmental conditions include the Altitude parameter (Meter abovesea level) which is detected using an Altimeter, Attitude Indicator (the position of the rocketwhich includes the value of the rotation angle of the Pitch, Roll, and Yaw), the position of thevehicle according to the longitude value and latitude detected by GPS (GeographicalPositioning System), pointer display on each latitude and longitude point value in theGeographic Information System, Heading Indicator and real-time log data. GS can display datausing an IC (Integrated Circuit) which has been integrated with an accelerometer, gyro, GPSsensors, and a communication module on a Microcontroller that can be placed on a rocketvehicle so that the IC can be called a rocket module. The rocket module is then connected tocommunicate with the GS using the 2.4 GHz XBee. The results of this research indicate that theGUI can display the Pitch and Roll value angles of 32.75o and 4.15o which are implemented withthe attitude indicator display movement, get a Yaw angle value of 132.23o, can display anAltimeter with a ride height of 79 meters above sea level, followed by real-time log data displayand GIS as a map display containing points for each change in latitude and longitude.

DESIGN AND IMPLEMENTATION OF REAL-TIME LOG ANALYSIS SYSTEM OF MAP WORLD PLATFORM

Abstract. In the big data era, real-time log data analysis is becoming the important demands for Internet enterprises, behind these data hiding big value. Map Word, the National Platform for Common GeoSpatial Information Services in China, is a one-stop website providing geospatial information services to personal users, enterprises, professional agencies and governments. After 7 years’ development of the platform, the traffic increased significantly, reaching 200 million service requests per day. But due to the lack of effective analysis and processing technology, the log data did not play its value, which lead to disconnection between the top-level design of national common geospatial information services and the actual demands of the users in a way. Now, the geospatial information service in China is trying to shift from the data production driven to the demand driven actively, and how to understand the demands of users became one imperious issue for research. In addition, the access behaviour of group users to common geospatial information services has a social nature and there is a certain group access behaviour pattern. This mode has high intensity of access aggregation and spontaneity, and determines the demand of common geospatial information services for cloud computing resources. Parts of the above demands can be analysed from the log data. Therefore, how to develop a log analysis system for unified real-time collection, real-time analysis, centralized storage, and graphical display is the key to support the demands. Flume, Kafka, Storm, Redis and HBase have been integrated to design and implement a distributed real-time log analysis system supporting online and offline log analysis. The system is composed of log collection module, asynchronous communication module, real time analysis and calculation module, data cashing and storage module, and visualization module. The system was release and integrated with Map World in June 2017 successfully, and the implementation of the system indicates that it can efficiently solve the problems of real-time log data collection, real-time analysis, real-time storage, real-time query, massive data storage, offline analysis, etc. It played an important role in map data update, policy making, product decisions, online server load prediction, resource allocation optimization, Internet security improvement and operation funs evaluation.

RETRACTED ARTICLE: Gray relational clustering model for intelligent guided monitoring horizontal wells

When drilling CBM horizontal wells, in order to improve the drilling rate of coal seams, real-time monitoring the lithology encountered underground must be performed. There are a large number of real-time logging data and historical data during the drilling process. Through data mining methods, the relationship between the changes of the underground lithology and the logging data can be found. Therefore, an improved gray clustering algorithm combined with the principle of mechanical specific energy is proposed to form a set of gray clustering model that can realize intelligent guided monitoring horizontal wells. By introducing correlation degree analysis method, this model optimizes the original gray fixed weight clustering monitoring model, establishing a horizontal well-oriented gray-related clustering model. In addition, the concept of decision rough set is used to replace the subjective given clustering threshold for the cluster threshold problem to achieve rapidly monitoring the CBM horizontal well drilling process. Through field data optimization analysis, the results show that the improved gray correlation clustering model can quickly and accurately identify lithology through real-time logging data.

Feasibility of using the linac real-time log data for VMAT treatment verification

This study investigates the feasibility of using the real-time log data from a linac to verify Volumetric Modulated Arc Therapy (VMAT) treatment. The treatment log data for an Elekta Synergy linac can be recorded at a sampling rate of 4 Hz using the service graphing tool on the linac control computer. A treatment plan that simulates a VMAT treatment was delivered from the linac and all the dynamic treatment parameters including monitor unit (MU), Multileaf Collimator (MLC) position, jaw position, gantry angle and collimator angle were recorded in real-time using the service graphing tool. The recorded raw data were extracted and analysed using algorithms written in Matlab (MathWorks, Natick, MA). The actual treatment parameters logged using the service graphing tool was compared to the prescription and the deviations were analysed. The MLC position errors travelling at the speed range from −3.25 to 5.92 cm/s were between −1.7 mm to 2.5 mm, well within the 3.5 mm tolerance value (AAPM TG-142). The discrepancies of other delivery parameters were also within the tolerance. The real-time linac parameters logged using the service graphing tool can be used as a supplementary data for patient specific VMAT pre-treatment quality assurance.

Research on Rapid Identification and Evaluation Technology for Gas Formation during Underbalanced Drilling

The underbalanced drilling (UBD) technology has been widely implemented due to its advantages in drilling efficiency improvement and cost reduction. However, this advanced technology requires very special equipment and operational mechanism, which raises multiple challenges to traditional well logging techniques. In this study, a real-time logging system (MWD/LWD and mud logging) was developed and utilized during underbalanced drilling, to quickly identify and evaluate gas formation. This advanced system enables fast detection of gas formation and determining the formation type while drilling, by monitoring the changes in gas production. This real-time logging system provides a powerful technical support to the gas reservoir drilling and development. A case study has clearly shown that the interpretation and evaluation results based on the real-time logging data agree well with the results of conventional well logging. Therefore, this advanced real-time logging technique can be utilized as an effective guidance for field operation.

Numerical simulation of resistivity LWD tool based on higher-order vector finite element

Numerical simulation of resistivity logging-while-drilling (LWD) tool response in complex borehole environments is of great importance for interpretation of measurement data and characterization of oil reservoirs. The simulation results can provide important theoretical guidance for designing novel electrical logging instruments and interpreting real-time logging data. In this paper, higher-order vector finite element method had been used to investigate the resistivity LWD tool response by changing coils spacing, transmitting frequency and structure of antenna system in the anisotropic formation. Numerical simulation results indicate that the change of T–R 1 spacing is an obvious influence on the investigation depth and detecting precision of the resistivity LWD tool, and the change of R 1–R 2 spacing can affect the resolution of the thin-layer distinguish. The change of transmitting frequency can improve the resolution of low-resistivity formation and high-resistivity formation of resistivity LWD tool. The change of the structure of the antenna system can provide accurate geosteering drilling information to directional resistivity LWD tool. Simulation results also indicated that the higher-order vector finite element method has good convergence speed and calculation accuracy and it is suitable to simulate the response of resistivity LWD tools.

A study on directional resistivity logging-while-drilling based on self-adaptive hp-FEM

Numerical simulation of resistivity logging-while-drilling (LWD) tool response provides guidance for designing novel logging instruments and interpreting real-time logging data. In this paper, based on self-adaptive hp-finite element method (hp-FEM) algorithm, we analyze LWD tool response against model parameters and briefly illustrate geosteering capabilities of directional resistivity LWD. Numerical simulation results indicate that the change of source spacing is of obvious influence on the investigation depth and detecting precision of resistivity LWD tool; the change of frequency can improve the resolution of low-resistivity formation and high-resistivity formation. The simulation results also indicate that the self-adaptive hp-FEM algorithm has good convergence speed and calculation accuracy to guide the geologic steering drilling and it is suitable to simulate the response of resistivity LWD tools.

클라우드 환경에서 MongoDB 기반의 비정형 로그 처리 시스템 설계 및 구현

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

Reducing Geologic Uncertainty By Combining Seismic Models With Real-Time LWD Measurements: Mars A-8 Case Study

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 134181, ’Combining Seismic Models With Real-Time LWD Measurements to Reduce Geologic Uncertainty: A Case Study of the Mars A-8 Extended Reach Well,’ by Jeff Weiland, SPE, Matt Cuttitta, SPE, and Gerard Soto, Shell, and Matt Wandstrat, SPE, Brian Donadieu, SPE, and Segun Jebutu, SPE, Baker Hughes, prepared for the 2010 SPE Annual Technical Conference and Ex hibition, Florence, Italy, 19-22 September. The paper has not been peer reviewed. While surface-seismic data are used to evaluate potential targets, they lack the resolution needed to place those targets reliably. True-vertical-depth (TVD) and true-vertical-thickness (TVT) synthetic ties created from real-time logging data can improve that resolution. By use of TVT, the synthetic ties match the seismic model better, enabling updates to the well path and precise placement inside the target. Also, multiple velocity models are incorporated to obtain better time/depth relation-ships and to predict pore-pressure anomalies better. Introduction During the summer of 2006, the six-block unit covering the Mars field was set to contract and the shallow exploration well just drilled in the area had yielded marginal results. Mississippi Canyon (MC) Blocks 850 and 851 were set to expire if production could not be established. Shell had identified potential geologic targets on Block 851 but had not developed a well plan. To establish production in time to maintain control of the lease, it was necessary to drill the well from the Mars platform, which required an extended-reach well. Geologic uncertainties and mechanical complexities created a challenging well. Background The Mars field, shown in Fig. 1, is in the MC area of the Gulf of Mexico (GOM), 130 miles southeast of New Orleans. The first discovery well was drilled in 1989 on MC Block 763. The field was developed from a 24-slot tension-leg platform (TLP) installed on MC Block 807 in May 1996 in 2,940 ft of water. Production began in July 1996 and peaked in June 2000 at 208,000 BOPD and 217 MMcf/D of gas. Primary production from Blocks 763, 806, and 807 in 1996 established the unit. Block 762 was set to expire in 2006, followed by Blocks 850 and 851 2 years later. In June 2006, Well MC 762-6 came on production, establishing MC Block 762 in the unit. However, with marginal results from the subsequent MC 850-1 exploration well in mid-2006, the future of Blocks 850 and 851 remained in doubt. Shell used the last available slot on the TLP to execute the MC 851 A-8 exploration well. Need for Extended-Reach TLP Well. While subsea vertical wells would be easier and faster to drill, the time needed to build subsea manifolds, construct subsea trees, and lay flowlines was considerable. With production required to hold the leases, drilling extended-reach wells from the Mars TLP was judged the best option. Although drilling and completion operations would be more expensive and more complicated to execute, production could commence immediately after completing the well.