scholarly journals Real-Time Pore Pressure Detection: Indicators and Improved Methods

Geofluids ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Jincai Zhang ◽  
Shangxian Yin
Keyword(s):  
Real Time ◽  
Pore Pressure ◽  
Wellbore Instability ◽  
Abnormal Pressure ◽  
Measurement While Drilling ◽  
Pore Pressure Prediction ◽  
Pressure Indicators ◽  
Background Gas ◽  
Mud Logging ◽  
Improved Methods

High uncertainties may exist in the predrill pore pressure prediction in new prospects and deepwater subsalt wells; therefore, real-time pore pressure detection is highly needed to reduce drilling risks. The methods for pore pressure detection (the resistivity, sonic, and corrected d-exponent methods) are improved using the depth-dependent normal compaction equations to adapt to the requirements of the real-time monitoring. A new method is proposed to calculate pore pressure from the connection gas or elevated background gas, which can be used for real-time pore pressure detection. The pore pressure detection using the logging-while-drilling, measurement-while-drilling, and mud logging data is also implemented and evaluated. Abnormal pore pressure indicators from the well logs, mud logs, and wellbore instability events are identified and analyzed to interpret abnormal pore pressures for guiding real-time drilling decisions. The principles for identifying abnormal pressure indicators are proposed to improve real-time pore pressure monitoring.

scholarly journals Identification of Geo – Hazard Using Pore Pressure Analysis in ‘MAC’ Field, Niger Delta

2021 ◽  
Keyword(s):  
Pore Pressure ◽  
Gamma Ray ◽  
Pressure Data ◽  
Abnormal Pressure ◽  
Pore Pressure Prediction ◽  
Wireline Logs ◽  
Formation Pore Pressure ◽  
Resistivity Log ◽  
Gamma Ray Log ◽  
Pressure Analysis

Identification of geo-hazard zones using pore pressure analysis in ‘MAC’ field was carried out in this research. Suite of wireline logs from four wells and RFT pressure data from two wells were utilized. Lithologic identification was done using gamma ray log. Resistivity log was used to delineate hydrocarbon and non-hydrocarbon formations. Well log correlation helps to see the lateral continuity of the sands. Pore pressure prediction was done using integrated approaches. The general lithology identified is alternation of sand and shale units. The stratigraphy is typical of Agbada Formation. Three reservoirs delineated were laterally correlated. Crossplot of Vp against density (Rho) colour coded with depth revealed that disequilibrium compaction is the main overpressure generating mechanism in the field. Prediction of overpressure by normal compaction trend was generated and plot of interval transit time against depth show that there is normal compaction from 250m to about 1700 m on MAC-01, but at a depth of about 1800m, there was abnormal pressure build up that shows the onset of overpressure. A relatively normal compaction was observed on MAC-02 until a depth of about 2100m where overpressure was suspected. The prediction of formation pore pressure using Eaton’s and Bower’s method to determine the better of the two methods to adopt for pore pressure prediction shows that the pore pressure prediction using Eaton’s method gave a better result similar to the acquired pressure in the field. Hence Eaton’s method appears to be better suited for formation pore pressure estimation in ‘MAC’ field. The validation of the pore pressure analysis results with available acquired pressure data affirmed the confidence in the interpreted results for this study.

scholarly journals Statistical modeling for real-time pore pressure prediction from predrill analysis and well logs

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. ID1-ID12 ◽  
Author(s):  
Jacopo Paglia ◽  
Jo Eidsvik ◽  
Arnt Grøver ◽  
Ane Elisabet Lothe
Keyword(s):  
Pressure Distribution ◽  
Real Time ◽  
Pore Pressure ◽  
Real Data ◽  
Well Logs ◽  
Spatial Direction ◽  
North East ◽  
Likelihood Model ◽  
Pore Pressure Prediction ◽  
Geologic Model

The challenge of pore pressure prediction in an overpressured area near a well is studied. Predrill understanding of pore pressure is available from a 3D geologic model for pressure buildup and release using a basin modeling approach. The pore pressure distribution is updated when well logs are gathered while drilling. Sequential Bayesian methods are used to conduct real-time pore pressure prediction, meaning that every time new well logs are available, the pore pressure distribution is automatically updated ahead of the bit and in every spatial direction (north, east, and depth), with associated uncertainty quantification. Spatial modeling of pore pressure variables means that the data at one well depth location will also be informative of the pore pressure variables at other depths and lateral locations. A workflow is exemplified using real data. The prior model is based on a Gaussian process fitted from geologic modeling of this field, whereas the likelihood model of well-log data is assessed from data in an exploration well in the same area. Results are presented by replaying a drilling situation in this context.

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