Kiosk Usage Measurement using a Software Logging Tool

Abstract The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters. Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology. The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation. Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.

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Implementation of Vendor-Independent Stochastic Inversion for Improving Quality and Efficiency of Well Placement on the Field of Novatek Company

10.2118/206621-ms ◽

2021 ◽

Author(s):

Danil Andreevich Nemushchenko ◽

Pavel Vladimirovich Shpakov ◽

Petr Valerievich Bybin ◽

Kirill Viktorovich Ronzhin ◽

Mikhail Vladimirovich Sviridov

Keyword(s):

Real Time ◽

Well Placement ◽

Stochastic Inversion ◽

Mathematical Algorithm ◽

Service Companies ◽

Resistivity Data ◽

Resistivity Logging ◽

Azimuthal Resistivity ◽

Logging Tool ◽

Single Approach

Abstract The article describes the application of a new stochastic inversion of the deep-azimuthal resistivity data, independent from the tool vendor. The new model was performed on the data from several wells of the PAO «Novatek», that were drilled using deep-azimuthal resistivity tools of two service companies represented in the global oilfield services market. This technology allows to respond in a timely manner when the well approaches the boundaries with contrasting resistivity properties and to avoid exit to unproductive zones. Nowadays, the azimuthal resistivity data is the method with the highest penetration depth for the geosteering in real time. Stochastic inversion is a special mathematical algorithm based on the statistical Monte Carlo method to process the readings of resistivity while drilling in real time and provide a geoelectrical model for making informed decisions when placing horizontal and deviated wells. Until recently, there was no unified approach to calculate stochastic inversion, which allows to perform calculations for various tools. Deep-azimuthal resistivity logging tool vendors have developed their own approaches. This article presents a method for calculating stochastic inversion. This approach was never applied for this kind of azimuthal resistivity data. Additionally, it does not depend on the tool vendor, therefore, allows to compare the data from various tools using a single approach.

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The Limitation of Reservoir Saturation Logging Tool in a Case of a Deeper Reservoir Flow into a Shallower Reservoir Within the Same Wellbore

10.2118/207115-ms ◽

2021 ◽

Author(s):

P. Merit Ekeregbe

Keyword(s):

Decision Making ◽

Flow Condition ◽

Oil And Gas ◽

Static Condition ◽

Oil And Gas Industry ◽

Well Performance ◽

Liquid Loading ◽

Critical Rate ◽

Logging Tool ◽

Historical Production

Abstract Saturation logging tool is one key tool that has been successfully used in the Oil and Gas Industry. As important as the tool is, it should not be mistaken for a decision tool, rather it is a tool that aids decision making. Because the tool aids decision making, the decision process must be undertaken by interdisciplinary team of Engineers with historical knowledge of the tool and the performance trend of the candidate well and reservoir. No expertise is superior to historical data of well and reservoir performance because the duo follows physics and any deviation from it is attributable to a misnomer. The decision to re-enter a well for re-perforation or workover must be supported by historical production and reasonable science which here means that trends are sustained on continuous physics and not abrupt pulses. Any interpretation arising from saturation logging tools without subjecting same to reasonable science could result in wrong action. This paper is providing a methodology to enhance thorough screening of candidates for saturation logging operations. First is to determine if the candidate well is multilevel and historical production above critical gas rate before shut-in to screen-out liquid loading consideration. If any level is plugged below any producing level, investigate for micro-annuli leakage. All historical liquid loading wells should be flowed at rate above critical rate and logged at flow condition. Static condition logging is only good for non-liquid loading wells. The use of any tool and its interpretation must be subjective and there comes the clash between the experienced Sales Engineer and the Production/Reservoir Engineer with the historical evidence. A simple historical trending and analysis results of API gravity and BS&W were used in the failed plug case-study. Further successful investigation was done and the results of the well performance afterwards negated the interpretation arising from the saturation tool which saw the reservoir sand flushed. The lesson learnt from the well logging and interpretation shows that when a well is under any form of liquid loading, interpretation must be subjective with reasonable science and historical production trend is critical. It is recommended that when a well is under historical liquid loading rate, until the rate above the critical rate is determined, no logging should be done and when done, logging should be at flow condition and the interpretation subject to reasonable system physics.

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Precision timing synchronization in Crosswell Electromagnetic logging Tool

2015 Chinese Automation Congress (CAC) ◽

10.1109/cac.2015.7382657 ◽

2015 ◽

Author(s):

Kaibo Zhou ◽

Jin Tao ◽

Deqian Mo ◽

Yixin Han ◽

Yanyun Duan

Keyword(s):

Timing Synchronization ◽

Logging Tool

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Jotun 4D: Characterization of fluid contact movement from time-lapse seismic and production logging tool data

The Leading Edge ◽

10.1190/1.1825941 ◽

2004 ◽

Vol 23 (11) ◽

pp. 1187-1194 ◽

Cited By ~ 15

Author(s):

Wences P. Gouveia ◽

David H. Johnston ◽

Arne Solberg ◽

Morten Lauritzen

Keyword(s):

Time Lapse ◽

Logging Tool

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Numerical simulation of azimuthal acoustic logging in a borehole penetrating a rock formation boundary

Geophysics ◽

10.1190/geo2015-0265.1 ◽

2016 ◽

Vol 81 (3) ◽

pp. D283-D291 ◽

Cited By ~ 4

Author(s):

Peng Liu ◽

Wenxiao Qiao ◽

Xiaohua Che ◽

Xiaodong Ju ◽

Junqiang Lu ◽

...

Keyword(s):

Domain Model ◽

P Wave ◽

S Wave ◽

Angle Variation ◽

P Waves ◽

Acoustic Logging ◽

S Waves ◽

Rock Formation ◽

Logging Tool ◽

Difference Time

We have developed a new 3D acoustic logging tool (3DAC). To examine the azimuthal resolution of 3DAC, we have evaluated a 3D finite-difference time-domain model to simulate a case in which the borehole penetrated a rock formation boundary when the tool worked at the azimuthal-transmitting-azimuthal-receiving mode. The results indicated that there were two types of P-waves with different slowness in waveforms: the P-wave of the harder rock (P1) and the P-wave of the softer rock (P2). The P1-wave can be observed in each azimuthal receiver, but the P2-wave appears only in the azimuthal receivers toward the softer rock. When these two types of rock are both fast formations, two types of S-waves also exist, and they have better azimuthal sensitivity compared with P-waves. The S-wave of the harder rock (S1) appears only in receivers toward the harder rock, and the S-wave of the softer rock (S2) appears only in receivers toward the softer rock. A model was simulated in which the boundary between shale and sand penetrated the borehole but not the borehole axis. The P-wave of shale and the S-wave of sand are azimuthally sensitive to the azimuth angle variation of two formations. In addition, waveforms obtained from 3DAC working at the monopole-transmitting-azimuthal-receiving mode indicate that the corresponding P-waves and S-waves are azimuthally sensitive, too. Finally, we have developed a field example of 3DAC to support our simulation results: The azimuthal variation of the P-wave slowness was observed and can thus be used to reflect the azimuthal heterogeneity of formations.

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