Development of Artificial Neural Networks (ANNs) to Synthesize Petrophysical Well Logs
Publish place: Petroleum Technical Conference and Exhibition
Publish Year: 1392
نوع سند: مقاله کنفرانسی
زبان: English
View: 1,305
This Paper With 9 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
PTCE01_022
تاریخ نمایه سازی: 16 مهر 1392
Abstract:
Porosity is one of the fundamental petrophysical properties which should be evaluated for hydrocarbon bearing reservoirs. Petrophysical well logs are the most essential instruments for the evaluation of hydrocarbon reservoirs. There are three main petrophysical logging tools for porosity determination namely: neutron, density and sonic well logs. Porosity can be determined using each of these tools; however, a precise analysis requires a complete set of these tools. Log sets are commonly either incomplete or unreliable for many reasons (i.e. incomplete logging, measurement errors and loss of data owing to unsuitable data storage). To overcome this issue, the current study presents an intelligent technique using Artificial Neural Networks (ANN) to synthesize petrophysical well logs including: neutron, density and sonic logs. To accomplish this, the petrophysical well logs data collected from six wells was utilized for constructing optimum ANN model and a seventh well data from the field was employed to evaluate the reliability of the model. The proposed methodology is presented with an application to field information of a carbonate oil reservoir, located in Persian Gulf, Iran. The corresponding correlation was obtained through the comparison of synthesized log values to real log amounts. The results demonstrate that ANNs are successful in synthesizing petrophysical well logs with a high degree of accuracy
Keywords:
Synthesizing petrophysical well logs , Artificial Neural Networks (ANNs) , Porosity , Well logs , Carbonate oil reservoir
Authors
Sh Esmaeilzadeh
Department of Petroleum Engineering, Imam Khomeini International University (IKIU), Qazvin, Iran
A. Afshari
Pars Oil and Gas Company (POGC), a subsidiary of National Iranian Oil Company (NIOC), Asaluyeh, Iran,
N. Sa`adatnia
Department of Petroleum Engineering, Abadan Faculty of Petroleum Engineering,
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :