o I n Pr i o A c c r u a o R k o St P r r s i n r T i Oi lfi l e d K w A r t c a t. I r n od c u iton

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1 017 nd International Conference on Applied Mechanics Mechatronics Engineering (AMME 017) ISBN: Study on I mproving P rediction A ccuracy R ock St rength P a rameters i n T arim Donghe O il Fi eld 1 1,, Zhong-h ua SONG * 1 1, Ke-xiong WANG, Shi-cheng ZHANG, Bo ZHOU Hui LIU School Petroleum Engineering, China Uni versity Petroleum, Beijing, 1049, C hina arim Oilfield Company, T C NPC, K orla, X injiang, , C hina * Corresponding author K eyw ords: T arim Donghe Oilfield, L ogging data, R ock strength parameter, R egional distribution, 3 D model, P rediction accuracy. A bstract. rock strength parameters a re an important basic parameters f or study sing mechanism, optimization s control methods implementation s control t echnology o f oil gas wells. refore, a ccurately d etermining rock strength parameters a region is especially important. r ock strength parameters are usually obtained by laboratory e xperiment, w hich c annot reflect distribution rock strength in a region with less cores e xperiment. we ll logging data can be used to predict rock strength parameters b ecause its reflecti ng r ich formation information. relationship model between logging data rock s trength parameters was established based on laboratory e xperiments three axial strength t he limited number cores in Tarim Donghe Oilfield. Three dimensional geological modeling stware is used to establish prediction model regional distribution rock strength p arameters with m odel. s ingle well prile a verage e rror was 10.% a nd 3D model a verage results error only 3. 9% compared with e xperimental results. Which v erified t he feasibility establishment three dimensional strength parameter model. i t has important practical value for improving prediction accuracy rock s trength parameters. Introd uction Th e p oor f ormation c ementation, l ower rock strength a nd t he serious s production w ith c ontinuous exploitation oil gas fields resulting in a significant declini ng oil gas recovery rati o, serious casing failure s o on. Which affect efficiency oil gas d evelopment [ 1]. factors leading to s production in addition to objective factors such as reservoir rock cementation material, cementation strength, size geological structure s tress, as well as e xploitation m ethod, technology time o il gas fields [-6]. r ock strength parameter is an important basic parameter for study si ng mechanism, o ptimization s control methods implementation s control technology o f t he oil a nd gas wells. refore, a ccurately d etermining r ock strength parameters a region is especially important. ro ck strength parameters are usually obtained by laboratory experiments. Which n eeds to collect a large number cores from scene. Because d rilling age i s o ld, s o it is very d ifficult to gar core. At same time, it is difficult to reflect distribution rock strength in a r egion with less core experiment r esults. To solve above problems, many scholars ha d carried o ut single well prile rock strength parameters predicti ng b y using a coustic logging data, are w idely used [7-10]. rock strength parameters w ere n ot same w ith b uried depth reservoir c hanged in a region, so single well prile prediction accuracy is low, which cannot provide accurate data for down- h ole s control. relationship model between logging data rock strength parameters is established based on l aboratory experiments three axial strength limited number cores in T arim Donghe oil field. th ree dimensional geological modeling stware is used to establish 85

2 prediction model regional distribution rock strength parameters with model, which can improve prediction accuracy rock strength parameters provide basis for down- hole s control. E stablishment S ingle Well Prile P rediction R ock Strength Prediction Model T hree Axis Strength Laboratory Experiment r eservoir r ock three axial strength laboratory experiments w ere c arried out with reservoir c ores at d ifferent buried depths in Tarim D onghe Oi lfield [ 11 ]. results experiment corresponding logging data w ere s hown in Table 1. Well n ame 1 # # 3 # E stablishment o f T able 1. Experimental W ell d epth r esults logging data reservoir cores in Donghe Oilfield. H orizon L ithology Rock s trength ( MPa) Acoustic m oveout ( μs/ft) n atural G amma # # t he 1D M odel linear, logarithmic function, power function, polynomial, exponential regression m ethod were used to a nalyze r elationship between strength parameters obtained from experiment l ogging data. regression analysis results w ere s hown in Table. According to correlation analysis between rock strength parameters logging data, it f ound that correlation between rock strength parameters natural gamma ray poor, re a significant correlation between rock strength parameters acoustic l ogging data. refore, s ingle well prile r ock strength prediction model e stablished by using acoustic logging data. Table shows that correlation coefficient e xponential function fitting regression R>0.8, F test value >44. correlation was very better, which can meet engineering needs. s ingle well prile r ock s trength is as: K=7.996e1 1.Tp. Logging t ype acoustic l ogging natural gamma- ray l ogging T able. C orrelation between rock strength parameters logging data. R elevant p arameter s traight line Logarithmic f unction power f unction p olynomial exponential f unction correlation c oefficient F test value correlation c oefficient F test value

3 Establishment Three Dimensional Rock Strength Parameter Prediction Model 3D Geological modeling stware were universal P etrel stware [ 1-15]. W e can build r egional rock strength parameters distribution 3D model by using Petrel s tware. Mainly u sed Carboniferous reservoir Donghe Oi lfield as an example to e stablish three dimensional rock s trength parameter prediction model. Da ta preparation Geological modeling b ased on database. basic d ata consist t he w ell location c oordinates d ata, single well logging data stratigraphic data. ( 1) W ell location coordinates data: location coordinates data is basic data 3D geological m odeling, including geodetic coordinates all wells in study area a nd th e e levation bushing. ( ) Single well logging data: select acoustic logging data as basis modeling data, at same time, reference to a variety logging data to provide a strong basis for stratification w ork. ( 3) R eservoir burial depth: according to drilling depth reservoir to determine trend o f region. E stablishment o f 3 D P rediction Model e leven w ells were selecte d i n designated area, n th e relative positions e leven wells in three-dimensional space were e stablishe d by inputting selected wells n umbers, wells respectively coordinates, bushing elevation inclination d ata. Carboniferous is divided into 3 layers, ea ch thickness 50 meters, which were used to simulate t hickness o f i n D onghe O ilfield, a s s hown in Figure 1. On this basis, each layer mesh division used in p lane 50 meters *50 meters, longitudinal step each 0 layer, total grid number was acoustic logging data is processed, n interpolation (Sequential Gaussiansimu interpolation method) is calculated to obtain three- dimensional model acoustic logging data, as shown in Figu re. F igure 1. Map o f reservoir thickness.figu re. 3 D model regional acoustic logging data. F igu re shows that th e acoustic difference maximum value 94. μs/ft, minimum value i s 46.7 μs/ft. S onic range i s mainly distributed in 61 μs/ft t o 78 μs/ft. Th e petrel model used to introduce relationship between rock strength parameters acoustic difference, we can calculate three- dimensional distribution rock strength p arameter values in region, as shown in Figure 3. regional distribution size rock s trength results s hown in Figure 4. 87

F igure 3. 3 D prediction model rock strength parameters. F igure 4. Histogram o f rock strength. F igure 4 s hows that reservoir rock s trength maximum v alue was 137.75 M Pa, minimum value 79.

R esults Analysis ( 1) Th e distribution rock strength parameters in Carboniferous strata is relatively stable, t he variation rock strength parameters can be seen from gradient color.

4 F igure 3. 3 D prediction model rock strength parameters. F igure 4. Histogram o f rock strength. F igure 4 s hows that reservoir rock s trength maximum v alue was M Pa, minimum value 79.67MPa in this area. rock strength mainly distributed between 100MPa to 136MPa, 116MPa to 10MPa distribution is more concentrated. R esults Analysis ( 1) Th e distribution rock strength parameters in Carboniferous strata is relatively stable, t he variation rock strength parameters can be seen from gradient color. ( ) re are obvious differences in rock strength parameters each lithologic section from d istribution t he vertical point, rock strength parameters w ere greater with overall trend is l arger. ( 3) T h e rock strength parameter bottom r egion in n orast l arger than t hat bottom r egion i n t he southwest b y comparing strata model, it can be seen that color s egment m ore yellow. Co nfirmation o f T hree Dimensional Prediction Model results rock strength parameters obtained from 3D model rock strength w ere c ompared with three axial strength test r esults single well prile model prediction results. c ompared results w ere s hown in Table 3. Core n umber T able 3. C omparison experimental results calculation results. Well d epth experime n tal result ( MPa) 1D calculation r esults ( Mpa) Error (%) 3D calculation r esults ( MPa) Error (%) 1 # # # # # # T able 3 shows that underground rock strength results was larger t han experiment r esults. B e cause o f t he core removed from underground placed for many years, actual underground rock density decreased, acoustic logging data reflects real s ituation. H owever, three axis strength experiments h ad b een simulated real situation underground, t herefore, experimental results e rror ratio o f a round 5% should be reasonable. single well p rile results maximum e rror r eaches 14. 5% c ompared with experimental r esults, average error is 10. %, 3D model r esults maximum e rror is o nly 6. 1%, with an average error about 3. 9%, which verified feasibility establishment t hree dimensional strength parameter prediction model. 88

5 C onclusion ( 1) According to correlation analysis between rock strength parameters logging data, it found that correlation between rock strength parameters natural gamma ray poor, re a significant correlation between rock strength parameters acoustic l ogging data. refore, s ingle well prile r ock strength prediction model e stablished by using acoustic logging data. () D onghe Oilfield 3D reservoir rock strength parameter prediction model prediction results show that rock strength parameters in horizontal distribution had no big changes, but larger in t he longitudinal changes with increase d b urial depth. (3) prediction accuracy 3D rock strength model raised more than 6 % compared with that single well p rile prediction. method can be used as a reference for prediction r ock strength parameters in or oilfields. (4) three dimensional prediction model can directly reflect change rock strength parameters in horizontal vertical direction, which can provide basic parameters for o ptimization s control method application s control technology. A cknowledgements authors gratefully acknowledge Xue- q ing Teng, Ling Li Cheng- xin Yang Tarim Oillfield Company for ir constructive s uggestions. This research was financially supported by CNPC. About corresponding author: Zhong-h ua S ong was born in 1968, S enior Engineer. main r esearch interests are rock mechanics research petroleum engineering petroleum d evelopment engineering. Ad d: N o. 18, r eservoir r oad, C h angping district, B eijing city, school p etroleum engineering, China University Petr oleum, 1049, C hina. R eference [ 1] Y. H. W ang, X. S. Liu, H. X. W ang. Review development application s production technology for oil wells [J]. P etroleum Drilling P roduction Technology, 1994, 05: 79-86( in Chinese). [ ] W. Q i, L. N. X iang, B. L. P ei. Study on dynamic prediction s production in Donghe oil f ield, Tarim Basin [J]. Xinjiang P etroleum Geology, 008, 03: [ 3] N. Morita, D.L.Whitfill et al. Realistic s production prediction: S PE A pr N umerical Approach. [ 4] N. M orita, D. L. W hitfill et al. Parametric study s production prediction: Analytical A pproach, SPE F eb [ 5] N. Kessler, Y. L. Wang et al. A simplified Pseudo 3D Model to E v aluate S Production Risk in Deviated Cased Holes. S PE Feb [ 6] A. Venkitaraman et al. Perforating requirements for s prediction, SPE [ 7] H.N. Xia, M.H. Yang. A computer method for prediction rock d rill ability using logging data [ J]. E xploration Engineerin g, 00, 1 (30): [ 8] K.X. Wang, F.Q. Wei. Application logging data in prediction formation Anti- Drilling c haracteristic parameters [ J]. P etroleum Drilling Technology, 003, 31 (5): 61-63(in Chinese). [ 9] P.Y. Wang, K.X. Wang, Y. H Zhai. Application seismic data to predict formation rock Anti- D rilling characteristic parameters [J]. P etroleum Drilling Production Technology, 006, 8 ( 3): 7-9 June 89

6 [ 10 ] M.Y. Liu. Analysis formation d rill ability u sing acoustic loggin g data [J]. Inner Mongolia P etrochemical Industry, 01, 38 (4): 56-58(in Chinese). [ 11] H.J. Y in. Practical 0-97 D rill ability o f Rock [M]. S hong: P etroleum University Press: 1989: [ 1] D.Y. Cao, Q. Y. Li. Discussion on 3D visualization mode l geological structure [ J]. Geology a nd Exploration, 001(4): 60-6(in Chinese). [ 13] T.X. Duang, X.M. Liu, Y. J. Zhang. S ome understing Petrel modeling [J]. Lithologic R eservoir, 007, 19 (): (in Chinese). [ 14] X.Y. Shi. A pplication Petr el stware in fine geological modeling [J]. Xinjiang Petroleum G eology, 007, 8 (6): [ 15] Q.Y. Zou, L. L. Mo, X.G. Bai. A pplication Petrel in structural modeling [J]. Journal Y angtze University, 011, 8 ():

Study on the Four- property Relationship of Reservoirs in YK Area of Ganguyi Oilfield

Study on the Four- property Relationship of Reservoirs in YK Area of Ganguyi Oilfield Study on the Four- property elationship of eservoirs in YK Area of Ganguyi Oilfield Abstract Xinhu Li, Yingrun Shang Xi an University of Science and Technology, Xi an 710054, China. shangyingrun@163.com