About this Research Topic

About this Research Topic
The effective analysis and efficient information mining of subsurface geoscience big data can improve exploration and development of oil and gas, and support CO2 storage programmes. In recent years, subsurface instrumentation has led to a large amount of data collected, which is also complex and difficult to analyse. The rapid development of big data analysis, deep learning, and machine learning have led many geologists to try to use these emerging computer technologies in subsurface geoscience research, which will become an increasingly important means of subsurface developments in the future.

At present, the application of geological big data, deep learning, and machine learning in petroleum geology is limited due to various types and different amounts of geological data and incomplete data mining technology. Machine learning, deep learning, data mining, and other technologies have shown great potential in diagenetic facies identification using well logging data, geological modeling of sand bodies and other fields. However, the application of deep learning, machine learning and other technologies remains limited in petroleum geology. In addition, the representativeness of training data sets of deep learning, machine learning and other techniques is also hindering more widespread application in petroleum geology. It's worth noting that, some new numerical simulation methods can be used to establish training data sets, which is one of the considered topics.

Therefore, this special issue focuses on “Application of AI and Geological Big Data in Petroleum Geology”. We especially welcome the following content, but are not limited to them:
A. Research on the application of deep learning, machine learning and other emerging technologies and algorithms in petroleum geology
B. How to improve the generalization ability of deep learning, machine learning and other algorithms in petroleum geology;
C. Deep learning, machine learning, and other emerging technologies and algorithms in 3D geological modeling.
D. The application of emerging numerical simulation technology in petroleum geology, and its significance to the application of AI technologies in petroleum geology.

Keywords: Geological Big Data, Data Mining, Machine learning, Deep learning, Petroleum Geology

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Ushbu tadqiqot mavzusi haqida

Er osti geologiyasi bo'yicha katta ma'lumotlarni samarali tahlil qilish va samarali ma'lumot qazib olish neft va gazni qidirish va rivojlantirishni yaxshilashi va CO2 saqlash dasturlarini qo'llab-quvvatlashi mumkin. So'nggi yillarda er osti asboblari katta hajmdagi ma'lumotlar to'planishiga olib keldi, bu ham murakkab va tahlil qilish qiyin. Katta ma'lumotlarni tahlil qilish, chuqur o'rganish va mashinalarni o'rganishning jadal rivojlanishi ko'plab geologlarni ushbu rivojlanayotgan kompyuter texnologiyalaridan er osti geosiyosiy tadqiqotlarda foydalanishga urinishlariga olib keldi, bu esa kelajakda yer osti ishlanmalarining tobora muhim vositasiga aylanadi.

Hozirgi vaqtda neft geologiyasida geologik katta ma'lumotlarni, chuqur o'rganish va mashinani o'rganishni qo'llash har xil turdagi va har xil miqdordagi geologik ma'lumotlar va to'liq bo'lmagan ma'lumotlarni qazib olish texnologiyasi tufayli cheklangan. Mashinani o'rganish, chuqur o'rganish, ma'lumotlarni qazib olish va boshqa texnologiyalar quduqlarni ro'yxatga olish ma'lumotlaridan foydalangan holda diagenetik fasiyalarni aniqlash, qum jismlarini geologik modellashtirish va boshqa sohalarda katta imkoniyatlarni ko'rsatdi. Biroq, neft geologiyasida chuqur o'rganish, mashinani o'rganish va boshqa texnologiyalarni qo'llash cheklanganligicha qolmoqda. Bundan tashqari, chuqur o'rganish, mashinalarni o'rganish va boshqa texnikalar bo'yicha o'quv ma'lumotlar to'plamining reprezentativligi ham neft geologiyasida kengroq qo'llanilishiga to'sqinlik qilmoqda. Ta'kidlash joizki, ko'rib chiqilayotgan mavzulardan biri bo'lgan o'quv ma'lumotlar to'plamini yaratish uchun ba'zi yangi raqamli simulyatsiya usullaridan foydalanish mumkin.

Shu sababli, ushbu maxsus nashr "Neft geologiyasida AI va geologik katta ma'lumotlarni qo'llash" mavzusiga qaratilgan. Biz, ayniqsa, quyidagi tarkibni mamnuniyat bilan qabul qilamiz, lekin ular bilan cheklanmaydi:
A. Neft geologiyasida chuqur o'rganish, mashinani o'rganish va boshqa rivojlanayotgan texnologiyalar va algoritmlarni qo'llash bo'yicha tadqiqotlar
B. Neft geologiyasida chuqur o'rganish, mashinani o'rganish va boshqa algoritmlarni umumlashtirish qobiliyatini qanday yaxshilash mumkin;
C. 3D geologik modellashtirishda chuqur o'rganish, mashinani o'rganish va boshqa rivojlanayotgan texnologiyalar va algoritmlar.
D. Yangi paydo bo'lgan raqamli simulyatsiya texnologiyasining neft geologiyasida qo'llanilishi va uning neft geologiyasida AI texnologiyalarini qo'llashdagi ahamiyati.

Kalit so'zlar: Geologik katta ma'lumotlar, ma'lumotlarni qazib olish, mashinalarni o'rganish, chuqur o'rganish, neft geologiyasi

Muhim eslatma: Ushbu tadqiqot mavzusiga kiritilgan barcha hissalar o'zlarining missiya bayonotlarida belgilanganidek, ular topshiriladigan bo'lim va jurnal doirasida bo'lishi kerak. Frontiers ko'rib chiqishning istalgan bosqichida ko'rib chiqilmagan qo'lyozmani mosroq bo'lim yoki jurnalga yo'naltirish huquqini o'zida saqlab qoladi.