Abstract
Knowledge of the distribution of the rock mechanical properties along the depth of the wells is an important task for many applications related to reservoir geomechanics. Such these applications are wellbore stability analysis, hydraulic fracturing, reservoir compaction and subsidence, sand production, and fault reactivation. A major challenge with determining the rock mechanical properties is that they are not directly measured at the wellbore. They can be only sampled at well location using rock testing. Furthermore, the core analysis provides discrete data measurements for specific depth as well as it is often available only for a few wells in a field of interest. This study presents a methodology to generate synthetic-geomechanical well logs for the production section of the Buzurgan oil field, located in the south of Iraq, using an artificial neural network. An issue with the area of study is that shear wave velocities and pore pressure measurements in some wells are missing or incomplete possibly for cost and time-saving purposes. The unavailability of these data can potentially create inaccuracies in reservoir characterization n and production management. To overcome these challenges, this study presents two developed models for estimating the shear wave velocity and pore pressure using ANN techniques. The input parameters are conventional well logs including compressional wave, bulk density, and gamma-ray. Also, this study presents a construction of 1-D mechanical earth model for the production section of Buzurgan oil field which can be used for optimizing the selected mud weights with less wellbore problems (less nonproductive time. The results showed that artificial neural network is a powerful tool in determining the shear wave velocity and formation pore pressure using conventional well logs. The constructed 1D MEM revealed a high matching between the predicted wellbore instabilities and the actual wellbore failures that were observed by the caliper log. The majority of borehole enlargements can be attributed to the formation shear failures due to an inadequate selection of mud weights while drilling. Hence, this study presents optimum mud weights (1.3 to 1.35 g/cc) that can be used to drill new wells in the Buzurgan oil field with less expected drilling problems.
Highlights
Petroleum geomechanics is the discipline that integrates geophysical, petrophysical, and geological data with rock mechanics to quantify the response of the reservoir to different scenarios of rock failure during drilling operations and well production life (Al-Malikee & Al-Najim, 2018)
Accurate estimation of shear wave and pore pressure in carbonate reservoirs is an important task for many applications related to reservoir geomechanics
This study presents artificial neural network techniques (ANNs) models that have been developed to guarantee the accuracy of predicting Vs and pore pressure (Pp) using conventional well logs
Summary
Petroleum geomechanics is the discipline that integrates geophysical, petrophysical, and geological data with rock mechanics to quantify the response of the reservoir to different scenarios of rock failure during drilling operations and well production life (Al-Malikee & Al-Najim, 2018). Many correlations and models have been developed to forecast shear wave velocities and formation pore pressures based on various parameters (Bingham, 1965; Jorden & Shirley, 1966; Rehm & McClendon, 1971; Zamora & Lord, 1974; Eaton, 1975) These models have their own limitations, for example, some of them can only be used in clean shales, or they are only applicable to the pressure generated by the under-compaction mechanism (Ahmed et al, 2019). The accuracy of the reservoir characterization will be decreased which will potentially affect the drilling operations and recovery factors of oil reservoirs To overcome this challenge in missing data, artificial neural network techniques (ANNs) have been recently applied in petroleum industry to correlate the missing data or key parameters with conventional well logs so that the developed models can be used for further estimation of rock mechanical properties. Rock mechanical properties have been established and calibrated with core data to construct a 1-D mechanical earth model for optimum mud weights selection in Buzurqan oil field
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