About the Course
Decades of subsurface exploration and characterisation have led to the collation and storage of large volumes of well related data. The amount of data gathered daily continues to grow rapidly as technology and recording methods improve. With the increasing adoption of machine learning techniques in the subsurface domain, it is essential that the quality of the input data is carefully considered when working with these tools. If the input data is of poor quality, the impact on precision and accuracy of the prediction can be significant. Consequently, this can impact key decisions about the future of a well or a field.
This study focuses on well log data, which can be highly multi-dimensional, diverse and stored in a variety of file formats. Well log data exhibits key characteristics of Big Data: Volume, Variety, Velocity, Veracity and Value. Well data can include numeric values, text values, waveform data, image arrays, maps, volumes, etc. All of which can be indexed by time or depth in a regular or irregular way. A significant portion of time can be spent gathering data and quality checking it prior to carrying out petrophysical interpretations and applying machine learning models. Well log data can be affected by numerous issues causing a degradation in data quality. These include missing data - ranging from single data points to entire curves; noisy data from tool related issues; borehole washout; processing issues; incorrect environmental corrections; and mislabelled data.
Having vast quantities of data does not mean it can all be passed into a machine learning algorithm with the expectation that the resultant prediction is fit for purpose. It is essential that the most important and relevant data is passed into the model through appropriate feature selection techniques. Not only does this improve the quality of the prediction, it also reduces computational time and can provide a better understanding of how the models reach their conclusion.
This paper reviews data quality issues typically faced by petrophysicists when working with well log data and deploying machine learning models. First, an overview of machine learning and Big Data is covered in relation to petrophysical applications. Secondly, data quality issues commonly faced with well log data are discussed. Thirdly, methods are suggested on how to deal with data issues prior to modelling. Finally, multiple case studies are discussed covering the impacts of data quality on predictive capability
Andy McDonald is a Petrophysicist with Lloyd’s Register in Aberdeen and has over 15 years of industry experience. He currently provides petrophysical expertise to software development projects and specialises in Python development, artificial intelligence and applications of machine learning to petrophysics. Andy holds an MSc in Earth Science from the Open University, and a BSc (Hons) in Geology & Petroleum Geology from the University of Aberdeen. He has also co-authored several technical conference papers for the SPWLA and SPE on topics covering machine learning, heavy oil, geomechanics and low salinity waterflooding.
Karibu na kozi Miongo ya upelelezi wa chini ya ardhi na sifa imesababisha mkusanyiko na kuhifadhi kiasi kikubwa cha data zinazohusiana vizuri. Kiasi cha data inayokusanywa kila siku kinaendelea kuongezeka haraka kadiri teknolojia na njia za kurekodi zinavyoendelea kuboreshwa. Pamoja na kuongezeka kwa kupitishwa kwa mbinu za kujifunza mashine katika uwanja wa chini ya ardhi, ni muhimu kwamba ubora wa data ya pembejeo unafikiriwa kwa uangalifu wakati wa kufanya kazi na zana hizi. Kama data ya kuingia ni ya ubora duni, athari juu ya usahihi na usahihi wa utabiri inaweza kuwa kubwa. Kwa hiyo, hilo laweza kuathiri maamuzi muhimu kuhusu wakati ujao wa kisima au shamba. Utafiti huu unazingatia data ya kumbukumbu ya kisima, ambayo inaweza kuwa na vipimo vingi, tofauti na kuhifadhiwa katika muundo mbalimbali wa faili. vizuri log data exhibits sifa muhimu ya Big Data: kiasi, aina, kasi, ukweli na thamani. vizuri data inaweza ni pamoja na maadili ya tarakimu, maadili ya maandishi, data waveform, picha safu, ramani, kiasi, nk. Yote ambayo inaweza kuwa indexed kwa muda au kina katika njia ya kawaida au isiyo ya kawaida. Sehemu kubwa ya muda inaweza kutumika kukusanya data na ubora kuangalia kabla ya kufanya tafsiri petrophysical na kutumia mifano ya kujifunza mashine. vizuri kumbuka data inaweza kuathiriwa na masuala mengi kusababisha uharibifu katika ubora wa data. Hizi ni pamoja na data kukosa - kuanzia pointi moja data kwa curves nzima; data kelele kutoka masuala yanayohusiana na chombo; borehole washout; masuala ya usindikaji; makosa ya mazingira marekebisho; na data mislabeled. Kuwa na kiasi kikubwa cha data haimaanishi kwamba yote yanaweza kupitishwa katika algorithm ya kujifunza mashine na matarajio ya kwamba utabiri unaotokana ni sahihi kwa kusudi. Ni muhimu kwamba data muhimu zaidi na muhimu ni kupitishwa katika mfano kupitia mbinu sahihi kipengele uteuzi. Hii si tu kuboresha ubora wa utabiri, lakini pia hupunguza muda wa hesabu na inaweza kutoa uelewa bora wa jinsi mifano kufikia hitimisho lao. Karatasi hii inachunguza masuala ya ubora wa data kawaida inakabiliwa na petrophysicists wakati wa kufanya kazi na data ya kumbukumbu ya vizuri na kupeleka mifano ya kujifunza mashine. Kwanza, muhtasari wa kujifunza mashine na Big Data ni kufunikwa katika uhusiano na maombi petrophysical. Pili, masuala ya ubora wa data kawaida inakabiliwa na vizuri log data ni kujadiliwa. Tatu, mbinu ni alipendekeza juu ya jinsi ya kushughulikia masuala ya data kabla ya modeling. Katika utafiti wa utafiti wa hali ya juu, utafiti wa hali ya juu unaonyesha jinsi data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data ya data. Yeye sasa hutoa uzoefu wa petrophysical kwa miradi ya maendeleo ya programu na maalumu katika maendeleo ya Python, akili bandia na maombi ya kujifunza mashine kwa petrophysics. Andy ana shahada ya uzamili katika Sayansi ya Dunia kutoka Chuo Kikuu cha Open, na BSc (Hons) katika Jiolojia na Jiolojia ya Petroli kutoka Chuo Kikuu cha Aberdeen. Pia ameshiriki kuandika karatasi kadhaa za mkutano wa kiufundi kwa SPWLA na SPE juu ya mada zinazohusu kujifunza mashine, mafuta mazito, geomechanics na mafuriko ya maji ya chumvi ya chini.