DPCM Compression for Real-Time Logging While Drilling Data

Transcription

1 28 JOURAL OF SOFTWARE, VOL. 5, O. 3, MARCH 21 DPCM Compresson for Real-Tme Loggng Whle Drllng Data Yu Zhang Modern Sgnal Processng & Communcaton Group, Insttute of Informaton Scence, Bejng Jaotong Unversty, Bejng 144, Chna E-mal: Sheng-hu Wang, Ke Xong, Zheng-dng Qu, Dong-me Sun Insttute of Informaton Scence, Bejng Jaotong Unversty, Bejng 144, Chna Abstract Loggng whle drllng (LWD) s a drllng technque whch obtans and transmts the loggng data durng ol/gas drllng operatons. Because of the lmted avalable transmsson bandwdth of mud channel, how to mprove the bandwdth utlzaton becomes a crtcal problem n LWD research. In ths paper, by dscussng the nformaton entropy of real loggng data, we fnd that the orgnal encodng of loggng data s neffcent. To prune the data redundancy effectvely and to mprove the bandwdth utlzaton, we propose a novel compresson method on the bass of Dfferental Pulse Code Modulaton (DPCM). To further mprove the compresson effcency wth less nformaton loss, we ntroduce the adjustable compresson parameters for dfferent knds of loggng data. Extensve experments wth the real loggng data show that our method can gve enough precse results wth the compresson rato of 5% at least. The experment results also show that our algorthm has the advantages of alterable compressng rato, excellent decode qualty and low algorthm complexty. Index Terms loggng whle drllng; LWD; DPCM; loggng data encode; I. ITRODUCTIO Loggng whle drllng (LWD) s a drllng technque, whch s used to collect and transmt the nformaton of the formaton durng ol/gas drllng operatons. Dfferent knds of nformaton, such as densty, gamma, resstvely and press, are requred to be avalable n real tme, because they are mportant to optmze drllng process, well placement, gude drllng parameter selecton and estmaton of the ol/gas reserves. However, t s very dffcult to transfer the loggng data from the bottom to the surface, whch makes LWD develop slowly. There are some hgh-speed transmsson technology such as cable, fber, drll strng and electromagnetsm, but these transmsson technologes are not sutable for practcal use because of some techncal and economc reasons. Mud-pulse transmsson technology, whose postve pulse mode has been avalable n commercal use, s consdered as the most practcal loggng data transmsson technque. However, t has a severe lmtaton that ts transmsson rate s no more than 1 bts per second (bps). The lmted data transmsson bandwdth avalable wth current LWD technology (typcal 4 bps) s one of the most challengng problems for real tme applcatons [1]. To solve ths problem, researchers exert every effort from dfferent aspects of the LWD system. Most of the researches focus on ncreasng data transmsson bandwdth [2-6]. They ntroduced new hghspeed transmsson technologes (e.g. cable, fber, drll strng and electromagnetsm) and tred to make them be sutable for practcal use n LWD system. But the fact s that the demand for real-tme data s growng much faster than the technology that can practcally transfer date ncreases [1]. So, only enhancng lmted transmsson bandwdth s not enough to meet the requrement of drllng and we have to maxmze the utlzaton of current lmted data transmsson bandwdth to satsfy the ever-growng demand for real-tme data. Compresson provdes one soluton about maxmzng the utlzaton of lmted data transmsson bandwdth. Thus, researchers have ntroduced some compresson technques and nformaton transmsson technologes to the LWD realm. Dfferent drllng sensors often provde dfferent amounts of data. All knds of loggng data have to be compressed due to the lmted bandwdth of the mud channel. However, most researches on loggng data compresson manly pay attenton to block data compresson, such as mage data [1], acoustc waveform data [7] and sesmc waveform data [8]). Less work has been done on low rate data compresson, such as gamma data, resstvty data, densty data and temperature data, whch are the basc and necessary measured data n LWD and must be transmtted n real-tme mode. Ref. [9] proposed a lossless data compresson method based on the characterstcs of loggng data, but ths method s not sutable for practcal use because t takes no consderaton to the lmtaton of the low power consumpton and the poor computng capablty of loggng data collecton nstruments on the bottom of the wells. In ths paper, we propose a new loggng data compresson approach, whch takes full consderaton of the requrement of real-tme and the lmtaton of the low power consumpton and the poor calculaton ablty of drllng devces. The man work and contrbutons of ths paper are as follows. Frstly, we analyze the nformaton entropy of loggng nformaton and fnd that there s redundancy n the loggng data, whch can be compressed. Secondly, we proposed a novel real-tme compresson approach. In our approach the Dfferental Pulse Code 21 ACADEMY PUBLISHER do:1.434/jsw

2 JOURAL OF SOFTWARE, VOL. 5, O. 3, MARCH Modulaton (DPCM), whch s a lossy compresson method, s used to compress the loggng data. Thrdly, by analyzng the loggng data, we fnd that the loggng data from dfferent nstruments has dfferent characterstc. Thus, we optmze the compresson parameters for dfferent loggng data respectvely n terms of ther own characterstc to nsure the dstorton sufferable. Fourthly, extensve experments are done on the bass of a great deal of real drllng data. Our experments show that the lossy compresson method can gve enough precse results when compresson rato s 5%. The experment results also show that our scheme has the advantages of alterable compressng rato, excellent decode qualty and low algorthm complexty, whch ndcate ts potental for applcatons. Somethng should to be noted s that we are cooperatng wth the nstrument manufacturer n manufacturng ndependent ntellectual property nstrument based on our proposal. The rest of ths paper s organzed as follows. Secton II analyzes the nformaton entropy of real loggng data and proposes the DPCM-based compresson method. Secton III performs extensve experments on the bass of a great deal of loggng data from fve real drllng wells. Fnally, Secton IV summarzes ths paper wth a concluson. II. LOGGIG DATA COMPRESSIO A. LWD data Informaton Entropy Analyss Accordng to nformaton theory, nformaton source X composed of a ( = 1,..., ) has nformaton entropy H( X ), H ( X) = p( a )log p( a ) (1) = pa ( ) s the probablty of a. H( X ) actually ndcates the mnmal code length wthout nformaton loss under lossless data compresson. Ths paper analyzes the gamma data and the resstvty data. The smlar analyzng approach can be appled to other loggng data, such as densty, neutron, acoustc, MR and pressure. The experments n Secton III wll be performed on several knds of loggng data, ncludng the gamma, the resstvty, the densty, the neutron and the temperature data, whch are the common measured data n drllng work. The electromagnetc wave resstvty sensor (EWR) measures two knds of resstvty data called SHALLOW and DEEP. The dual gamma ray sensor measures gamma data called DGR. These three knds of data are represented wth 8bts respectvely. The exstng LWD system does not compress the data by source compresson technques. We calculate the nformaton entropy of the three knds of data n terms of Equaton (2): j= 2 pa ( ) = ca ( )/ ca ( j ) (2) Where, = 255 and ca ( ) s the count of the tmes that a appears. Takng the values of pa ( ) nto Formula (1), we obtan the nformaton entropy of the three knds of data as shown n Table Ⅰ. TABLE I. IFORMATIO ETROPY OF LOGGIG DATA Well ID o. 1 o. 2 o. 3 o. 4 o. 5 Drllng Tme(hour) Drllng Depth (meter) 927~ ~ ~ ~ ~ 1363 DGR Data Quantty (bt) Informaton Entropy of DGR EWR Data Quantty (bt) Informaton Entropy of DEEP Informaton Entropy of SHALLOW Table I shows the nformaton entropy of loggng data whch come from fve dfferent drllng wells. We adopte weghted calculaton on data quantty to get the statstcal nformaton entropy. The nformaton entropy of DGR s about 4.73 bts, the nformaton entropy of DEEP s about 6.29 bts, and the nformaton entropy of SHALLOW s about 6.1 bts. The results show that the nformaton entropy of each data of the three knds of drllng measures s less than 8 bts. That s to say, there s redundancy n these loggng data, whch can be compressed by some compresson algorthms. The exstng compresson algorthms can be dvded nto two classes, the lossless compresson algorthms and the lossy compresson algorthms. The former can recover data wthout nformaton loss and the latter recovers the compressed data wth partal nformaton loss. However, the latter often has hgher compresson rato and lower computatonal complexty than the former. On one hand, the gamma and resstvty data whch are requred to be transmtted real-tmely and the workers just use them to do qualtatve analyss to gude the operaton of drllng on the spot, so the real-tme performance of the data s much more mportant than ther accuracy. That s to say, a certan data dstorton does not have bad nfluence on data analyss. On the other hand, due to the power lmtaton of the sensors on the bottom of the wells, low computatonal complextes of the lossy compresson algorthms have to be taken nto consderaton. Consequently, we select the lossy compresson algorthms to enhance the real-tme performance of drllng data. In ths case, the length of the compressed data wll be shorter than the nformaton entropes shown n Table I. B. Dstrbuton Probablty Analyss To mplement the lossy compresson algorthm effectvely wth less nformaton loss as possble as we can, n ths subsecton, we dscuss the probabltes dstrbuton of the three knds of loggng data. Fg. 1 shows the dstrbuton probabltes of DGR, DEEP and SHALLOW. From Fg. 1(a), we can see that the DEEP data s dstrbuted n two ntervals, 5~64 and 16~175, 21 ACADEMY PUBLISHER

3 282 JOURAL OF SOFTWARE, VOL. 5, O. 3, MARCH 21 and from Fg. 1(b) we can see that the SHALLOW data s dstrbuted n two ntervals, 4~64 and 16~16. Fg. 1(c) shows the DGR data s dstrbuted n 25~55. The reason that dfferent knd of measures dstrbutng n dfferent ntervals s that the geologcal characterstcs brng on loggng data s dstrbuton. That s to say, the same knds of loggng data whch are collected from dfferent drllng wells n the same area by the same knd of sensors have the smlar dstrbuton. Hence, we can use ths concluson to keep the accuracy of our compressng approach. Fg. 1(d), (e) and (f) show the frst dfference of the three knds of data respectvely. The results show that all the frst dfferences of the data are dstrbuted n -5~+5, whch means that data change mldly. Fg. 1(d) and (e) ndcate that the same knd of data have the smlar frst dfference dstrbuton. Fg. 1(d), (e) and (f) also show that dfferent knds of data have dfferent frst dfference dstrbuton. Thus, we use dfferent quantzaton rules and codebook data to reduce the nformaton loss caused by quantzaton error of dfferent loggng data DEEP data value DEEP frst dfference (a) (b) (c) SHALLOW data value SHALLOW frst dfference DGR data value (d).127 (e).122 (f) DGR frst dfference Fgure 1. Orgnal data and frst dfference dstrbuton Fg. 2 shows the normalzed correlaton coeffcent of DGR, DEEP and SHALLOW. From Fg. 2, all the frstorder correlaton coeffcents of the three knds of data are hgher than.98, whch mean the loggng data have very strong correlaton and they belong to the nformaton source wth memory. correlaton coeffcent correlaton coeffcent correlaton coeffcent order (a) DEEP normalzed correlaton coeffcent order (b) SHALLOW normalzed correlaton coeffcent order (c) DGR normalzed correlaton coeffcent Fgure 2. ormalzed correlaton coeffcent Fg. 1 and Fg. 2 ndcate that the loggng data have characters of centralzed dstrbuton, mld change and strong correlaton. The good compresson performance can be obtaned f we select a lossy compresson algorthm whch s sutable for the loggng data. C. DPCM-based Compressng Method As the loggng data have strong correlaton mentoned above and DPCM s a compresson technque whch compass data by reducng the correlaton among the data, we choose DPCM to perform loggng data compresson. Consderng the requrements of low complexty, good generalty, good maneuverablty and low nformaton dstorton, we use the frst order lnear predcton DPCM to compress the loggng data. The steps of our method are as follows: 1.Use a predctve functon to estmate next data on the bass of exstng data; 2.Quantfy the estmaton error between the estmated value and real value; 3.Encode the estmaton error on the bass of the codebook DPCM can narrow value range by encodng estmate error value rather than real data value. For an nput sample X at tme nstant, only data X J at tmes J are used n the encodng process to predct X ˆ as the estmated value at tme. e s the estmaton error and e ˆ = X X. Quantzer quantfy e and then get ts quantfed value e. q = e e, where q s the quantzaton error caused by the quantzer. The recever decodes the code and gets ts output Y n terms of Y ˆ = X + e. So DPCM error d ˆ = X Y = X X e = e e = q. The DPCM error s equal to the quantzaton error and s ndependent of the recever. Usng the optmal predctve functon and quantzer mentoned above, whch can be obtaned by tranng set under the condton of mnmum mean square error crteron, to compress data can reduce data dstorton effectvely. In ths paper, we select tranng set wth the real loggng data whch has the dstrbuton smlar to encodng data to get the optmal predctve functon and quantzer. The tranng set whch has the dstrbuton smlar to encodng data s easy to be obtaned because explotaton workers often have to drll a large number of wells n the same area to fnd ol/gas n general. The experments n ths paper use loggng data whch come from fve dfferent drllng wells n the same area. III. EXPERIMETS In order to valdate the effectveness of our method, we mplement extensve experments va usng DPCM to encode fve real drllng well loggng data. The loggng data nclude the DGR data and the EWR data (DEEP & SHALLOW). The drllng depth of the fve wells s about 21 ACADEMY PUBLISHER

4 JOURAL OF SOFTWARE, VOL. 5, O. 3, MARCH ~199 meters and the total drllng tme s about 19 hours. Moreover, the amount of the total loggng data we use s 461,784 bts. A. Loggng Data Preprocessng To enhance the encodng performance, we have to preprocess loggng data wth removng sngular value and translaton transformaton at frst. Consderng the lmtaton of low power consumpton and poor computng capablty of the drllng nstruments, preprocessng should be smple and have low computatonal complexty. Because of the nterference caused by the harsh drllng envronment [1], sensors may get sngular values sometmes. The sngular values are useless for drllng work. Moreover, they often enlarge the value range of the correct data, whch ncreases DPCM error and brngs down the performances of data codng. So we use the 3σ crteron [11] to detect sngular values and replace them wth the average value. 3σ crteron s used wdely n electronc measurement to remove abnormal data effectvely. Supposng X s the average of X, σ s the standard devaton. If X X > 3σ, then X s a sngular value, replace X wth X. To reduce computatonal complexty, we use formula (3) to get σ. 2 2 X ( ) 1 X = = 1 σ = 2 Accordng to formula (3), n order to obtan the standard devaton σ, we only have to do the extracton operaton once, do the multplcaton operaton fve tmes and do the addton operaton three tmes. The computatonal complexty s constant and does not ncrease wth the growng of data quantty. So the tme complexty s O(1). DGR value DGR value DGR sample (a) DGR orgnal curve DGR sample (b) DGRcurve after removng sngular value Fgure 3. Removng sngular value Fgure3 (a) shows the data curves before removng the sngular values, whch s the orgnal output of sensor. Fgure3 (b) shows the data curves that we had removed sngular data from normal data. Comparng Fgure3 (a) and (b), we fnd 3σ crteron s effectve to remove abnormal loggng data. After removng the sngular values, the data value range reduce to 2~56 from 18~141. (3) We fnd that all fve wells EWR data have no value between 64 and 16. The reason s that the geologcal characterstcs determne the loggng data s dstrbutng ntervals. Fg. 4 shows the translaton transformaton of DEEP data. We move the values whch are less than 64 to close to 16. Consequently, a narrow value range of loggng data s obtaned. The recever recovers the data va the nverse transformaton of the values. probablty probablty DEEP data value (a) DEEP data orgnal value probablty dstrbuton map DEEP data value (b) DEEP data translaton transformaton probablty dstrbuton map Fgure 4. Translaton transformaton B. Encodng Experments We defne the encodng dstorton as d to evaluate encodng performance. The encodng dstorton d s the rato of the mean square devaton and the mean value of orgnal data, whch s formulated by Equaton (4). 2 ( ) / = 1 = 1 (4) d = X Y X Where X s the value of the -th orgnal sample and Y s the decoded value of the -th sample. Table II gves the DGR encodng dstorton when usng dfferent order predctve functon and dfferent code length. The results show that the nfluence of predctve functon order s less than that of the code length. Based on the encodng dstortons of Table II, we choose 2 or 3 bts to encode DGR data n practcal producton. In ths paper, the crtera of code length selecton are d < 5%. Obvously, one order predctve functon and 3-bt length are the best choce to encode the 8-bt orgnal DGR data. TABLE II DGR LOGGIG DATA DISTORTIO Order DPCM Code Length 2bt 3bt 4bt 5bt Table III shows the encodng performance when usng one order predctve functon and 3 bts to encode the 8- bt orgnal DGR data. The frst row of Table III s the well ID of the tranng data. The frst column s the well ID of encodng data. The values of Table III are the values of dstorton d. The results show that all the values of d are less than 5%. 21 ACADEMY PUBLISHER

5 284 JOURAL OF SOFTWARE, VOL. 5, O. 3, MARCH 21 Encodng TABLE III. DGR ECODIG PERFORMACE Tranng Set Well ID ll o. 1 o. 2 o. 3 o. 4 o. 5 o o o o o Through analyzng Table III, we fnd that the maxmum of d s.373 and the mnmum of d s.239, whch means dfferent tranng set has a lttle nfluence on DGR encodng performance. DGR value DGR value DGR sample (a) DGR orgnal curve(8bt encode) DGR sample (b) DGR decodng curve(3bt DPCM) Fgure 5. Orgnal DGR curve and decodng curve when d=.373 Fg. 5 compares the orgnal DGR data wth the DPCM decoded DGR data under d =.373, whch s the maxmum of d n Table III. Somethng need to be stressed s that d s the maxmal can be seen as the worst case. The result shows that the two curves have the same changng trend. That s to say, our method does not dsturb LWD user s qualtatve analyss even n the worst case when usng one order predctve functon and 3 bts DPCM to encode the 8-bt orgnal DGR data. TABLE IV. EWR LOGGIG DATA DISTORTIO Type Order DPCM Code Length 2bt 3bt 4bt 5bt DEEP SHALLOW Table IV shows the EWR encodng dstorton when usng dfferent order predctve functon and dfferent code length. We can see that the nfluence of predctve functon order s less than that of the code length as same as DGR data encodng. Accordng to the values of d showed n Table IV, we can select 3 or 4 bts to encode the EWR data,.e., the DEEP data and the SHALLOW data, n practcal producton. In ths paper, we select the one order predctve functon and 4 bts to encode 8-bt orgnal DEEP/SHALLOW data. Encodng Well ID TABLE V. DEEP ECODIG PERFORMACE Tranng Set Well ID o. 1 o. 2 o. 3 o. 4 o. 5 o o o o o Table V shows the encodng performance when usng one order predctve functon and 4 bts DPCM to encode the DEEP data. The frst row of Table V s the well ID of the tranng data. The frst column s the well ID of encodng data. The values of Table V are the values of dstorton d.the maxmum of d s.93 and the mnmum of d s.253. Compare wth Table III, we can state that dfferent tranng data has greater nfluence on the encodng performance of the DEEP data than on the DGR data encodng performance. Moreover, the smaller the tranng set s (for example, the amount of data of well 1 s bts), the worse the encodng performance s (the maxmum of d s.93), and the bgger the tranng set s (for example, the amount of data of well 5 s bts), the better the encodng performance s (the maxmum of d s.412). So we select large tranng set, such as the well 2 and well 5, to compute the DEEP data encodng parameter n practcal producton. In ths case, d s less than 5%. DEEP value DEEP value DEEP sample (a) DEEP orgnal curve(8bt encode) DEEP sample (b)deep decodng curve(4bt DPCM) Fgure 6. Orgnal DEEP curve and decodng curve when d=.93 Fg. 6 compares the orgnal DEEP data wth the DPCM decoded DEEP data under d =.93, whch s the maxmum of d n Table V. The result shows that the two curves have the same changng trend. That s to say, our method does not dsturb LWD user s qualtatve analyss n the worst case when usng one order predctve functon and 4 bts DPCM to encode the 8-bt orgnal DEEP data. Encodng Well ID TABLE VI. SHALLOW ECODIG PERFORMACE Tranng Set Well ID o. 1 o. 2 o. 3 o. 4 o ACADEMY PUBLISHER

6 JOURAL OF SOFTWARE, VOL. 5, O. 3, MARCH o o o o o Table VI shows the encodng performance when usng one order predctve functon and 4 bts to encode orgnal 8-bt SHALLOW data. The frst row of Table VI s the well ID of the tranng data. The frst column s the well ID of encodng data. The values of Table VI are the values of dstorton d.the maxmum of d s.536 and the mnmum of d s.21. The result shows that dfferent tranng data has a lttle effect on the SHALLOW data encodng performance as same as DGR data encodng. Moreover, Table VI shows most values of d are less than 5%. From the results of the Table VI and the Table V, we can see that the encodng performance of SHALLOW data s better than that of the DEEP data. The reason s that the dstrbuton of SHALLOW data s more centralzed than that of the DEEP data as shown n Fg. 1(a) and Fg. 1(b). SHALLOW value SHALLOW value SHALLOW sample (a) SHALLOW orgnal curve(8bt encode) SHALLOW sample (b) SHALLOW decodng curve(4bt DPCM) Fgure 7. Orgnal SHALLOW curve and decodng curve when d=.536 Fg. 7 compares the orgnal SHALLOW data wth the DPCM decoded SHALLOW data under d =.536, whch s the maxmum of d n Table VI. The result shows that the two curves have the same changng trend. Thus, our method does not dsturb LWD user s qualtatve analyss n the worst case when usng one order predctve functon and 4 bts DPCM to encode orgnal 8-bt SHALLOW data. C. Other Loggng Data Encodng Experments The smlar analyzng approach can be appled to other loggng data, such as densty, neutron and temperature. To show the effectveness of our methods, the other four knds of loggng data are expermented, whch nclude the neutron loggng data called EAR and FAR, the densty loggng data called DE and the temperature loggng data called TEM. The four knds of data above are also represented wth 8bts n exstng LWD systems, respectvely. However, no source compresson has been performed on them. In followng tests, our method s executed on them. Table VII gves the encodng dstorton rates about the four knds of data when usng one order predctve functon and dfferent code length. TABLE VII LOGGIG DATA DISTORTIO Type DPCM Code Length 2bt 3bt 4bt 5bt DE EAR FAR TEM Based on the results of Table VII, we can select 3 or 4 bts to encode the orgnal data n practcal producton. In ths paper, we chose one order predctve functon and 3 bts to encode the orgnal densty, neutron and temperature data. From Table VII, t can be observed that the dstorton s not more than 6%. DE value DE value DE sample (a) DE orgnal curve (8 bt encode) DE sample (b) DE decodng curve (3 bt DPCM) Fgure 8. Orgnal DE curve and decodng curve when d=.526 Fg. 8 compares the orgnal densty data DE wth the DPCM decoded DE data under d =.526. The result shows that the two curves have the same changng trend. EAR value EAR value EAR sample (a) EAR orgnal curve (8 bt encode) EAR sample (b) EAR decodng curve (3 bt DPCM) Fgure 9. Orgnal EAR curve and decodng curve when d=.33 Fg. 9 compares the orgnal neutron data EAR wth the DPCM decoded EAR data under d =.33. The result shows that the two curves have the same changng trend. 21 ACADEMY PUBLISHER

7 286 JOURAL OF SOFTWARE, VOL. 5, O. 3, MARCH 21 FAR value FAR value FAR sample (a) FAR orgnal curve (8 bt encode) FAR sample (b) FAR decodng curve (3 bt DPCM) Fgure 1. Orgnal FAR curve and decodng curve when d=.526 Fg. 1 compares the orgnal neutron data FAR wth the DPCM decoded FAR data under d =.526. The result shows that the two curves have the same changng trend. Fg. 9 and Fg. 1 compare the orgnal neutron data wth the DPCM decoded ones. From the two fgures, we can obtan the followng conclusons. Lke the SHALLOW and DEEP, EAR and FAR are two knds of measured data come from the same sensor. Moreover, the encodng performance of EAR data s better than that of the FAR data, whch s just lke that the encodng performance of SHALLOW s better than that of the DEEP data. The reason s the value range of the shallow formaton measured data (SHALLOW and EAR s smaller than that of the deep formaton measured data (DEEP and FAR), whch results n that the dstrbuton of the shallow formaton measured data s more centralzed than the dstrbuton of the deep formaton measured data. TEM value TEM value TEM sample (a) TEM orgnal curve (8 bt encode) TEM sample (b) TEM decodng curve (3 bt DPCM) Fgure 11. Orgnal TEM curve and decodng curve when d=.67 Fg. 11 compares the orgnal temperature data TEM wth the DPCM decoded TEM data under d =.67. The results show that the two curves have the very smlar changng trend, and the TEM data has the best encodng performance n all seven knds of loggng data. The reason s that the change of temperature s often very mld, whch s very sutable for DPCM. D. Summarzaton Of The Experments In ths secton, we use one order predctve functon and 3 bts to encode the orgnal gamma, densty, neutron and temperature data, one order predctve functon and 4bts to encode the orgnal resstvty data. The extensve experments show that our method can make the results precse enough ( d < 5% ) wth the compresson rato of 5%. IV. COCLUSIO In ths paper, we proposed a novel real-tme compresson approach. In our approach the DPCM, whch s a lossy compresson method, s used to compress the most common loggng data (gamma, resstvty, densty, neutron and temperature data). To mprove the compressng performances, we analyzed the drllng data and found that the loggng data from dfferent nstruments has dfferent characterstc. So, we optmze the compresson parameters for dfferent loggng data respectvely n terms of ther own characterstc to nsure the dstorton sufferable. Extensve experments are done on the bass of a great deal of real drllng data. Our experments show that the lossy compresson method can gve enough precse results when compresson rato s 5% ACKOWLEDGMET Ths work s supported by atonal Hgh Technology Research and Development Program of Chna 863 program o. 27AA REFERECES [1] G. A. Hassan and P. L. Kurkosk, Zero latency mage compresson for real tme loggng whle drllng applcatons, Proceedngs of MTS/IEEE OCEAS, 25, vol.1, pp [2] X. P. Lu, J. Fang and Y. H. Jn, Applcaton status and prospect of LWD data transmsson technology, Well loggng technology, vol.32, no. 3, 28, pp [3] C. W. L, D. J. Mu, A. Z. L, Q. M. Lao, and J. H. Qu, Drllng mud sgnal processng based on wavelet, Proceedngs of the 27 Internatonal Conference on Wavelet Analyss and Pattern Recognton, Bejng, Chna, 27, pp [4] J. H. Zhao, L. Y. Wang, F. L, and Y. L. Lu, An effectve approach for the nose removal of mud pulse telemetry system, The Eghth Internatonal Conference on Electronc Measurement and Instruments, ICEMI 27, vol. 1, pp [5] X. S. Lu, B. L, and Y. Q. Yue, Transmsson behavor of mud-pressure pulse along well bore, Journal of Hydrodynamcs, vol. 19, no. 2, 27, pp [6] C. Y. Wang, W. X. Qao, and W. Q. Zhang, Usng transfer matrx method to study the acoustc property of drll strngs, IEEE Internatonal Symposum on Sgnal Processng and Informaton Technology, 26, pp [7] W. Zhang, Y. B. Sh, and Z. G. Wang, Wavelet neural network method for acoustc loggng-whle-drllng waveform data compresson, Journal of the Unversty of Electronc Scence and Technology of Chna, vol. 37, no. 6, 28, pp. 9-93, 921. [8] G. Bernascon, and M. Vassallo, Effcent data compresson for sesmc-whle-drllng applcatons, IEEE 21 ACADEMY PUBLISHER

8 JOURAL OF SOFTWARE, VOL. 5, O. 3, MARCH Transactons on Geoscence and Remote Sensng, vol. 41, no. 3, 23, pp [9] Z. X. Han, F. Guo, and L. K. Qn, A lossless data compresson method based on the characterstcs of loggng data, Journal of X'an Shyou Unversty (atural Scence Edton), vol.21, no.1, 26, pp [1] C. H. Lu, T. Zhang, and H. D. L, Mud pulse measurement whle drllng system, Geologcal Scence and Technology Informaton, vol. 24 (sup), 25, pp [11] B. Lu and G. P. Da, Adaptve wavelet thresholdng denosng algorthm based on whte nose detecton and 3σ rule, Chnese journal of sensors and actutors, vol.18, no. 3, 25, pp Yu Zhang receved hs B.S. degree n computer scence from Bejng Jaotong Unversty (formerly knows as orthern Jaotong Unversty), Bejng, Chna, n June 24. He s currently workng toward the Ph.D. degree at the Insttute of Informaton Scence, School of Computer and Informaton Technology, Bejng Jaotong Unversty, Bejng, Chna. Hs research nterests nclude dgtal sgnal processng, multmeda communcaton & processng and compressed sensng. predcton, processng, etc. Sheng-hu Wang receved hs Ph.D. degree n Dgtal Sgnal Processng from Bejng Jaotong Unversty n 27. He s now a lecturer at Insttute of Informaton Scence, Bejng Jaotong Unversty. Hs man research nterests nclude vdeo codng & transmsson, network traffc modelng and embedded technology, dgtal sgnal Ke Xong receved hs B.S. degree n computer scence from Bejng Jaotong Unversty (formerly knows as orthern Jaotong Unversty), Bejng, Chna, n June 24. He s currently workng toward the Ph.D. degree at the Insttute of Informaton Scence, School of Computer and Informaton Technology, Bejng Jaotong Unversty, Bejng, Chna. Hs research nterests nclude sgnal processng, multmeda communcaton, network performance analyss, network resource management, and the QoS guarantee n IP networks. Zheng-dng Qu receved B.S. degree n Communcaton Engneerng n 1967, and M.S. degree n Dgtal Sgnal Processng n 1981 from orthern Jaotong Unversty, Bejng respectvely. Durng 1967 to 1978, he was a communcaton engneer at LuZhou Ralway Bureau. Snce 1981, he worked at Insttute of Informaton Scence, orthern Jaotong Unversty, where he became a lecturer n 1983, an assocate professor n 1987 and a full professor n He was an oversea research scholar at Computer Scence Department of Pttsburgh Unversty, U.S.A., and joned n the program of parallel processng n sgnal processng. Durng 1999 to 2, he was an vstng research fellow at Electronc Engneerng Lab of Kent Unversty, U.K. and engaged n mage processng and bometrcs processng program. From 1993 to 21, professor Qu undertook program Research and Implement of Multmeda Conferences Termnal and System whch s one of Chnese H-tech projects and program the Research and Implement of Multmeda Servce Platform over IP network whch s a sub-project of Chnese atonal nth Fve-year key projects. Hs research feld ncludes dgtal sgnal processng, multmeda communcaton and processng and parallel processng. He s currently a fellow of Chnese Insttute of Communcatons, a senor member of Chnese Insttute of Electroncs and Ralway respectvely. Dong-me Sun receved her Ph.D. degree n Dgtal Sgnal Processng from Bejng Jaotong Unversty n 23. She s now an assocate professor at Insttute of Informaton Scence, Bejng Jaotong Unversty. Her research nterests nclude bometrcs technology, mage analyss, pattern recognton, nformaton securty and sgnal processng. Currently Mrs. Sun has been dong some projects supported by atonal atural Scence Foundaton of Chna, Specalzed Research Fund for the Doctoral Program of Hgher Educaton and atonal Scence & Technology Pllar Program. Recently she has been cooperatng wth Imagng Research Center at Unversty of Cncnnat (USA) on several medcal mage processng and analyss projects. So far she has publshed over 4 research papers n the domestc and nternatonal scentfc journals and conferences. 21 ACADEMY PUBLISHER