Application of machine learning techniques for well pad identification in the Bakken oil field

Transcription

1 Applcaton of machne learnng technques for well pad dentfcaton n the Bakken ol feld Introducton Phlp G. Brodrck and Jacob G. Englander December, There has been ncreased scrutny n understandng the anthropogenc sources for methane emssons due to methane s potency as a greenhouse gas []. There are two approaches for studyng of methane emssons, emssons nventores (or bottom up studes), and remote measurements of methane fluxes (or top down studes). Emssons nventores calculate the leakage rates for fugtve methane over a gven area as: E = M j N A,j EF () where E s the emssons rate, A,j s the actvty (or the number of peces of equpment of type ) at well pad j, EF s the emssons factor for equpment type, N s the set of possble equpment types, and M s the set of wells n the locaton of nterest. The nner sum provdes the emssons rate for a specfc well pad. One of the sgnfcant problems wth the bottom up emssons nventory approach s that of scale. Even when very detaled measurements of the equpment and stages of operatons are conducted, dong so comes at the prce at lookng at a small and not necessarly representatve sample of the wells and operators. Ths s the most sgnfcant crtque of Allen et al. whch found lower leakage numbers when compared to other emssons nventores []. In order to better rectfy the dscrepances between the top down and bottom up studes, two smultaneous development need to occur. There needs to be more proper accountng for the number of devces and peces of equpment present on a partcular ste and there needs to be better samplng and estmaton of emssons factors at the devce level. The ntenton of ths project, whch s to apply machne learnng technques on hgh resoluton spatal magery to dentfy well pads, s meant to serve as an ntal step n ths process. Whle there s uncertanty n estmatng both the actvty counts and the emssons factors, there are also sgnfcant dsagreements n smply estmatng the number of well pads n a gven locaton. For example the SI to Brandt et al. report a 5x dfference n the number of reported well completons n the Unted States between two dfferent data sources (for the EPA reported 87 wells, whle IHS reported 85) []. The purpose of ths project s to dentfy well pads usng hgh resoluton spatal magery, whch s a frst step towards estmatng actvty counts over an entre producton feld. Machne learnng classfers are wdely utlzed for remote-sensng based classfcatons, and ther applcaton has been of partcular nterest n the lterature of classfcaton of hgh resoluton spatal magery. Common classfers utlzed n the lterature nclude Naïve Bayes, k-nearest Neghbor, Neural Network, Random Forests, and SVM. Mountraks et al., n a revew of the use of SVM for remote sensng applcatons, descrbe the ablty for ths classfer to better balance the bas-varance tradeoff as well as t ablty to use small tranng sets. They also demonstrate that SVMs are dsproportonally used n applcatons wth hgh resoluton magery [5]. Methods and data. Data and preprocessng The regon of study for ths project s the fast developng Bakken ol feld n Western North Dakota. Ths area s of partcular nterest as ol producton has ncreased from bbl/day n 5 to,, bbl/day n. Specfcally, the study area utlzed was a 5 km area of m pxel aeral photographs acqured n August

2 from the Natonal Aeral Imagery Program at the USDA. A map of the study area wthn the context of the Bakken can be found n Fgure. b) c) 9 The Bakken Regon 9 9 a) d) Segment NDVI_mean Brghtness Area (pxels) Roundness GLCM Entropy Fgure : Schematc for nput data: a) project study area wthn the context of the Bakken regon and North Dakota, b) Close up mage of study area, c) example of segmented mage, d) example of data exported from segmentaton. For most remote sensng applcatons, analyss and classfcaton s performed at the pxel level. However, ths has been found to be not partcularly effectve for hgh resoluton magery due to the the heterogenety of adjacent pxels []. The wdely accepted technque for extractng data out of hgh resoluton magery has been to aggregate pxels nto mage objects (or segments) [6]. In order to solate these mage objects, the ecognton software platform was utlzed usng the mult-resoluton segmentaton functon wth the followng parameters whch were utlzed to best dentfy well pads: scale (whch roughly corresponds to sze) =, shape (or the nfluence of color on segment) =.6, and compactness (whch corresponds to the ablty of segments to have varyng sze) =.9. The result of ths process dvded the mage nto 985 segments, whch serve as the nput rows for the classfer. Ths set was later trmmed down to 79 segments to avod edge effects around the permeter of the regon of nterest. A vsual example of the result of segmentaton can be found n Fgure. Each of the segments has 75 features (or columns) derved from three overarchng categores: spectral reflectance (RGB and near IR) and brghtness, segment geometry (shape and sze), and texture (whch s based on the grey-level co-occurance matrx from Haralck et al.) []. A matrx representaton wth the segments as the rows and the features as the columns s used as the nput data for the classfer. The output s a bnary ndcator for whether the test segment s or s not a well pad. The well pads were verfed wth GIS layers of known ol and gas facltes n North Dakota [7], and further refned by hand. A schematc of the model functon can be found n Fgure.. Models To perform the classfcaton we used the followng models and technques whch are commonly used n the lterature, utlzng default MATLAB functons:. Support Vector Machne (Gaussan Kernel)

3 NDVI_ Area GLCM Well Pad Segment mean Brghtness (pxels) Roundness Entropy (Y=/N=) Example of tranng data Model SVM Random Forest Neural Network Output Classfcaton Fgure : Schematc of model functon. Random Forest. Neural Network. Feature Evaluaton: Prncpal Component Analyss Results Well pads are geographcally scarce throughout the Bakken ol feld, and snce sze and shape are mportant segmentaton characterstcs utlzed n our applcaton of ecognton, are also scarce amongst the generated segments. Of the 79 segments used (post-processng), only 95 are well pads (.66%). Consequently, a classfcaton could have a 95% success rate whle successfully dentfyng every well pad n the area, and stll have falsely dentfed almost as many wells as actually exst. In the context of usng ths classfcaton to estmate fugtve emssons, ths degree of error s well above an acceptable lmt. Fgure demonstrates the scarcty of well pads n the data set, hghlghtng the mportance of hghly accurate classfcaton. Test Wells Tranng Wells Predcted locatons Fgure : Demonstraton of well pad classfcaton usng a Random Forest wth 75% of the data as a tranng set. To better analyze classfcatons results (such as shown n Fgure ) we defne both a true-postve and false-postve

4 rate. The frst, true-postve rate (tpr), s the commonly used expresson: m {y = y tpr = y = } m () y where y s the true classfcaton, y s the hypotheszed classfcaton, and m s the sze of the testng set. The second measure, false-postve rate (f pr) s defned n ths context slghtly more conservatve than s often seen, as: m { (y = y fpr = ) y = } m. () y A recever operatng characterstc (ROC) curve s examned n order to explore the trade-off between tpr and f pr n dfferent algorthms. To generate the curve, 75% of the avalable data s used as the tranng set, and 5% s used as the testng set. Each algorthm s appled ten tmes to (the same set of) random permutatons of the data, and the ROC curves are shown n Fgure. True Postve Random Forest Neural Network. SVM False Postve Fgure : ROC curve showng the trade-off between true-postve and false-postve rates.. The data set requred s small enough that the tme requred for classfcaton, even for the entre Bakken ol feld, s not prohbtvely expensve. However, the computaton tme requred to generate the features s; the data set used n the lmted area surveyed n ths work took roughly hours to generate. In order to determne the order n whch to nclude features, a prncple component analyss (PCA) s performed frst. Examnng the prncple components (the frst of whch are shown n Fgure 5(a)) t s clear that each component s heavly domnated be a small subset of features. An arbtrary (and conservatve) threshold of. s set, and (n order) the features wth coeffcents whose absolute value exceeds the threshold are selected. Ths roughly ordered set of features s then used to explore the effect of reducng the feature space, by classfyng the same random permutatons of the data set used above wth the best performng algorthm, the Random Forest, wth an ncreasng number of features. The resultng true and false-postve rates are shown n Fgure 5(b). Dscusson and Conclusons The results of ths work demonstrate the vablty of utlzng machne learnng classfcaton for well pad dentfcaton n the Bakken. As demonstrated n Fgure, the overall accuracy of well pad dentfcaton s hghly dependent on the acceptable rate of false postves. Though both the Neural Network and the Random Forest performed well overall, the Random Forest benefts the most wth a gve allowance of false postve error. The ROC curve shows a change n the relatve ncreased true postve rate as a functon of false postve rate at an approxmate % false-postve rate, ndcatng that ths pont results n the best model performance for the ntended applcaton.

5 Prncple Component Coeffcent Prncple Component Number (a) Vsualzaton of the frst prncple components True/False Postve Rate True Postve Rate False Postve Rate Rollng Average Number of Features Included (b) Demonstraton of the affect of data reducton on true-postve and false-postve rates. Addtonally, the result of the PCA allows for a dramatc reducton of the feature set, and consequently a sgnfcant reducton n computaton effort. Fgure 5(b) demonstrates that after the ncluson of 65 featuers, the accuracy of the classfers do not mprove consderably. We estmate that ths could lead to a reducton n computaton tme of 5-65%. In the process of ths analyss, we added 75 km of area to an ntal area of 5 km. Ths addtonal data was meant to provde an analyss on the effect of classfcaton accuracy as the tranng set became larger, however only a modest ncrease n classfcaton accuracy was observed. The lack of mprovement lkely resulted from usng the same segmentaton parameters between data sets, whch ntroduced some segmentaton errors upon examnng the segments. However, the computaton tme requred for segmentaton was prohbtve to expermentng wdely on the effect of segmentaton parameter selecton. Wth the analyss of feature space reducton performed n ths work, further analyss n ths area can be performed. Addtonally, the strct accuracy constrants for the calculaton (low acceptable false postve rate) lkely contrbuted to the modest gans n accuracy. Future work wll utlze the classfcaton results of ths work to solate magery n the mmedate vcnty of the well pads n order to perform a second segmentaton step n order to dentfy surface equpment such as tanks, pumpjacks, and compressors. Ths mght test the lmt of the spatal resoluton of these mages and as such hgher resoluton magery sources wll also be explored. References. T. Blaschke. Object based mage analyss for remote sensng. ISPRS Journal of Photogrammetry and Remote Sensng, 65(): 6, Jan.. do:.6/j.sprsjprs A. R. Brandt, G. A. Heath, E. A. Kort, F. O Sullvan, G. Petron, S. M. Jordaan, P. Tans, J. Wlcox, A. M. Gopsten, D. Arent, S. Wofsy, N. J. Brown, R. Bradley, G. D. Stucky, D. Eardley, and R. Harrss. Methane Leaks from North Amercan Natural Gas Systems. Scence, (67):7 75, Feb.. do:.6/scence.75.. EPA. Greenhouse Gas Reportng Program, subpart W, Petroleum and Natural Gas Systems. Techncal report, Envronmental Protecton Agency,. URL R. M. Haralck, K. Shanmugam, and I. Dnsten. Textural Features for Image Classfcaton. IEEE Transactons on Systems, Man, and Cybernetcs, (6):6 6, Nov. 97. do:.9/tsmc G. Mountraks, J. Im, and C. Ogole. Support vector machnes n remote sensng: A revew. ISPRS Journal of Photogrammetry and Remote Sensng, 66():7 59, May. do:.6/j.sprsjprs S. W. Mynt, P. Gober, A. Brazel, S. Grossman-Clarke, and Q. Weng. Per-pxel vs. object-based classfcaton of urban land cover extracton usng hgh spatal resoluton magery. Remote Sensng of Envronment, 5(5):5 6, May. do:.6/j.rse North Dakota Department of Mneral Resources. North Dakota Drllng and Producton Statstcs,. URL olgas/stats/statstcsvw.asp. 5