scholarly journals Evaluation of Mud Weight Using Safe Mud Window Concept Based on Well Log Data: A Case Study of Well OP-002 in the North Sumatra Basin Area, Indonesia

2021 ◽  
Vol 1 (1) ◽  
pp. 248-266
Author(s):  
Aris Buntoro ◽  
Basuki Rahmad ◽  
Allen Haryanto Lukmana ◽  
Dewi Asmorowati
Keyword(s):  
Pore Pressure ◽  
Shear Failure ◽  
Depth Interval ◽  
Well Log ◽  
Log Data ◽  
Window Analysis ◽  
The North ◽  
Safe Mud Window ◽  
Fracture Gradient ◽  
North Sumatra

In the drilling operation of well OP-002 which is located in the North Sumatra Basin at a depth interval of 2887 - 3186 m occurred partial loss, and caving at a depth interval of 500 - 1650 m, where the drilling problem is caused by the use of inappropriate mud weight. Safe mud window analysis is carried out by processing well log data to build PPFG (Pore Pressure Fracture Gradient) and 1D Geomechanics model using several calculation methods. Furthermore, the results of the calculation of pore pressure and fracture gradient are validated with well test data from the well OP-002, so the safe mud window can be determined, and can be used as a basis in the analysis of the drilling problems that occur. The optimum mud weight can minimize wellbore instability, with a limit value that must be greater than the collapse pressure, but not exceeding the minimum insitu stress limit. From the results of the mud safe window analysis, it can be concluded that at a depth interval of 500 - 1650 m caving occurs, because the density value used is smaller than the shear failure gradient, and at a depth interval of 1619 - 2829 m, the density value used is greater than Shmin. To overcome this problem, a mud wight with a safe mud window concept is recommended, namely the selection of the optimum mud weight to be used must be greater than the pore pressure and shear failure gradient and does not exceed the minimum horizontal stress and fracture gradient values.

Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms

2021 ◽  
Author(s):  
Mohammad Farsi ◽  
Nima Mohamadian ◽  
Hamzeh Ghorbani ◽  
David A. Wood ◽  
Shadfar Davoodi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pore Pressure ◽  
Optimization Algorithms ◽  
Well Log ◽  
Log Data ◽  
Hybrid Machine ◽  
Formation Pore Pressure

scholarly journals Geophysical imaging of porosity variations in the Danish North Sea chalk

2008 ◽  
Vol 15 ◽  
pp. 17-20 ◽  
Author(s):  
Tanni Abramovitz
Keyword(s):  
North Sea ◽  
Seismic Reflection ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Well Log ◽  
Reservoir Properties ◽  
Seismic Reflection Data ◽  
Log Data ◽  
The North ◽  
The North Sea

More than 80% of the present-day oil and gas production in the Danish part of the North Sea is extracted from fields with chalk reservoirs of late Cretaceous (Maastrichtian) and early Paleocene (Danian) ages (Fig. 1). Seismic reflection and in- version data play a fundamental role in mapping and characterisation of intra-chalk structures and reservoir properties of the Chalk Group in the North Sea. The aim of seismic inversion is to transform seismic reflection data into quantitative rock properties such as acoustic impedance (AI) that provides information on reservoir properties enabling identification of porosity anomalies that may constitute potential reservoir compartments. Petrophysical analyses of well log data have shown a relationship between AI and porosity. Hence, AI variations can be transformed into porosity variations and used to support detailed interpretations of porous chalk units of possible reservoir quality. This paper presents an example of how the chalk team at the Geological Survey of Denmark and Greenland (GEUS) integrates geological, geophysical and petrophysical information, such as core data, well log data, seismic 3-D reflection and AI data, when assessing the hydrocarbon prospectivity of chalk fields.

scholarly journals Centimeter-Scale Lithology and Facies Prediction in Cored Wells Using Machine Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Thomas Martin ◽  
Ross Meyer ◽  
Zane Jobe
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Core Material ◽  
Depth Interval ◽  
Well Log ◽  
Log Data ◽  
Core Data ◽  
Labeling Scheme

Machine-learning algorithms have been used by geoscientists to infer geologic and physical properties from hydrocarbon exploration and development wells for more than 40 years. These techniques historically utilize digital well-log information, which, like any remotely sensed measurement, have resolution limitations. Core is the only subsurface data that is true to geologic scale and heterogeneity. However, core description and analysis are time-intensive, and therefore most core data are not utilized to their full potential. Quadrant 204 on the United Kingdom Continental Shelf has publicly available open-source core and well log data. This study utilizes this dataset and machine-learning models to predict lithology and facies at the centimeter scale. We selected 12 wells from the Q204 region with well-log and core data from the Schiehallion, Foinaven, Loyal, and Alligin hydrocarbon fields. We interpreted training data from 659 m of core at the sub-centimeter scale, utilizing a lithology-based labeling scheme (five classes) and a depositional-process-based facies labeling scheme (six classes). Utilizing a “color-channel-log” (CCL) that summarizes the core image at each depth interval, our best performing trained model predicts the correct lithology with 69% accuracy (i.e., the predicted lithology output from the model is the same as the interpreted lithology) and predicts individual lithology classes of sandstone and mudstone with over 80% accuracy. The CCL data require less compute power than core image data and generate more accurate results. While the process-based facies labels better characterize turbidites and hybrid-event-bed stratigraphy, the machine-learning based predictions were not as accurate as compared to lithology. In all cases, the standard well-log data cannot accurately predict lithology or facies at the centimeter level. The machine-learning workflow developed for this study can unlock warehouses full of high-resolution data in a multitude of geological settings. The workflow can be applied to other geographic areas and deposit types where large quantities of photographed core material are available. This research establishes an open-source, python-based machine-learning workflow to analyze open-source core image data in a scalable, reproducible way. We anticipate that this study will serve as a baseline for future research and analysis of borehole and core data.

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