SPE = 1, then the. Application of this technique to a large number of wells in the Carthage field, Cotton Valley formation is presented.

Transcription

1 SPE Identfyng Infll Locatons and Underperformer Wells n Mature Felds usng Monthly Producton Rate Data, Carthage Feld, Cotton Valley Formaton, Texas Jalal Jalal, Shahab D. Mohaghegh, Raz Gaskar, West Vrgna Unversty Copyrght 2006, Socety of Petroleum Engneers Ths paper was prepared for presentaton at the 2006 SPE Eastern Regonal Meetng held n Canton, Oho, U.S.A., October Ths paper was selected for presentaton by an SPE Program Commttee followng revew of nformaton contaned n an abstract submtted by the author(s). Contents of the paper, as presented, have not been revewed by the Socety of Petroleum Engneers and are subject to correcton by the author(s). The materal, as presented, does not necessarly reflect any poston of the Socety of Petroleum Engneers, ts offcers, or members. Papers presented at SPE meetngs are subject to publcaton revew by Edtoral Commttees of the Socety of Petroleum Engneers. Electronc reproducton, dstrbuton, or storage of any part of ths paper for commercal purposes wthout the wrtten consent of the Socety of Petroleum Engneers s prohbted. Permsson to reproduce n prnt s restrcted to an abstract of not more than 300 words; llustratons may not be coped. The abstract must contan conspcuous acknowledgment of where and by whom the paper was presented. Wrte Lbraran, SPE, P.O. Box , Rchardson, TX U.S.A., fax Abstract Recent ncrease n global demand for energy and the consequent hgh prces have prompted a need for mprovng the recovery from mature reservors. Identfyng sweet spots n these felds for n-fll drllng and rankng the nfll locatons based on ther potental productvty as well as underperformer wells as canddates for remedal operatons are mportant for mprovng the economcs of mature felds. One of the most mportant ssues that make analyss of mature felds qute challengng s lack of data. Producton rate data s about the only data that can be easly accessed for most of the mature felds. The most accessble producton data usually does not nclude flowng bottom-hole or well head pressure data. Lack of pressure data serously challenges the use of conventonal producton data analyss technques for most of the mature felds. The motvaton behnd development of the technques that are presented n ths study s to demonstrate that much can be done wth only monthly producton rate data n order to help the revtalzaton of mature felds. Methods currently used for producton data analyss are declne curve analyss, type curve matchng, and hstory matchng usng numercal reservor smulators. Each one of these methods has ts strengths and weaknesses. They nclude sgnfcant amount of subjectvty when they are used ndvdually n the context of producton data analyss. In ths paper, ntellgent systems are used n order to teratvely ntegrate the abovementoned technques nto one comprehensve methodology for dentfcaton of nfll drllng locatons as well as underperformer wells that would be the prme canddates for restmulaton and/or workovers. Applcaton of ths technque to a large number of wells n the Carthage feld, Cotton Valley formaton s presented. Intoroducton Several producton analyss tools and strateges for estmatng the remanng reserve, dentfyng nfll drllng locatons and underperformer wells exst n the ol and gas ndustry. In order to make any conclusons usng most of these methods, a large amount of data such as producton data and reservor propertes are requred. Producton data analyss technques have mproved sgnfcantly over the past several years. These technques provde the engneer some of the reservor propertes and estmates of the hydrocarbon n place and ultmate recovery. Frst and most common method for producton data analyss s declne curve analyss. Declne Curve Analyss (DCA) s a method to ft the observed producton rates of ndvdual wells, group of wells, or reservors by a mathematcal functon n order to predct the performance of the future producton by extrapolatng the ftted declne curve. DCA was frst ntroduced by Arps 1 n 1940s usng mathematcal equatons. The reason for DCA beng wdely used s ts smplcty and snce t s an emprcal method, t does not requre any nformaton regardng the reservor or well parameters. The mathematcal functons are characterzed by three parameters; q (ntal flow rate),b (declne exponent), and D (ntal declne rate.) Whenb = 0, the declne s exponental. Whenb = 1, then the declne s harmonc. When 0 < b < 1, the declne s hyperbolc. Fetkovch 2 ntroduced declne curve analyss by type curves n 1980s by relatng Arps declne parameters to some reservor engneerng parameters for producton aganst constant bottom-hole pressures. Over the past few years, the type curve matchng methods have been mproved by several people n order for them to be used for dfferent reservor types and producng scenaros. Although declne curve analyss and type curve matchng technques are stll beng used wdely, but the results they provde are hghly subjectve.

2 2 SPE Reservor hstory matchng s also used n the ol and gas ndustry mostly n major companes. Performng hstory matchng for a reservor s a tme consumng process and t requres a large amount of data such as reservor propertes, producton and pressure data, and well parameters. Lack of any of these data wll result n poor conclusons about the reservor. The technque presented n ths paper ntegrates the three abovementoned technques (declne curve analyss, typecurve matchng, and sngle-well producton hstory matchng) through an teratve process n order to remove the subjectvty of each these methods f they are performed ndvdually and to come up wth a set of representatve reservor propertes. In addton, ntellgent technques such as fuzzy pattern recognton, neural networks and fuzzy logc n order to make decsons on dentfyng locatons for nfll drllng and underperformer wells. Actual producton data from Carthage feld, cotton valley formaton are used n ths analyss. declne curve analyss would be a set of q, b and D. Once the matchng process s completed and the three parameters have been dentfed, you can calculate Estmated Ultmate Recovery (EUR) for a certan number of years for the selected well. In ths analyss EUR s calculated for 30 years. The 30 year EUR for the well shown on fgure 1 s calculated to be 3,679 MMSCF. Methodology In ths secton, the procedure for ntellgent producton data analyss (IPDA) s ntroduced. The reader should keep n mnd that IPDA s developed for the stuatons that only producton data are avalable. In cases that other nformaton s avalable such as geologc data, pressure tests, core analyss, and etc., one mght choose to use other well establshed technques 3. Nevertheless, as more data such as those mentoned above are avalable, one may use them to ncrease the accuracy and the relablty of the methodology beng ntroduced here. IPDA has two major components. The frst component s an teratve process, whch declne curve analyss, type curve matchng and hstory matchng, are performed on the producton data of a partcular well n the feld untl convergence s acheved to a unfed set of reservor propertes. Gven the fact that each of these technques are qute subjectve by nature, by lettng each one technque to gude and keep an eye on the other two durng the analyss, the degree of confdence and relablty on the results as well as repeatablty of the analyss wll ncrease. Fgure 1 s an example of declne curve analyss for well W3506. The second step n ths process s type curve matchng where based on the b value obtaned from the frst step, a set of type curves wll be generated. These type curves are developed by Cox 4 for low permeablty gas reservors for producton rate data not pressure. Fgure 2 shows an example of type curve matchng for the same well shown on fgure 1 usng the actual producton data. The second component (fuzzy pattern recognton) s ntended to ntegrate the abovementoned nformaton n the context of the entre feld to llustrate the feld s status at any tme n the future n order to dentfy the underperformer wells and locatons for nfll drllng. The frst step s declne curve analyss, whch the producton rate and cumulatve producton data are plotted aganst tme on a sem-log scale and a declne curve s ftted. An automatc optmzaton routne fnds the best declne curve for the gven well, as both the rate and cumulatve versus tme are smultaneously matched. Fgure 1 shows an example of declne curve analyss for a well n Carthage feld, Cotton Valley. If one uses hyperbolc declne, then the results of the Fgure 2 s an example of type curve matchng for well W3506. In stuatons that the actual producton data are very scattered and a good match cannot be obtaned, one may use the data of the ftted declne from declne curve analyss as t s shown on fgure 3.

3 SPE The thrd step s producton hstory matchng usng a sngle-well reservor smulator. Reservor propertes that were the results of type curve matchng are used as the startng pont for hstory matchng and the objectve s to match the producton data of a partcular well. Hstory matchng s not a smple and straght forward procedure. One of the mportant tasks n hstory matchng s to match the reservor pressure. But what would we do f we don t have the reservor pressure data? The answer to ths queston could be that we wll contnue matchng only the producton data (f that s the only data we have) and try to come up wth a set of reservor propertes that make physcal sense. Fgure 3 shows the type curve matchng usng ftted declne curve data. In ths step, a good match s acceptable f the 30 year EUR calculated here s n a reasonable range wth the one calculated from declne curve analyss. The EUR value could be consdered as a controllng parameter that holds the ntegrty of these methods together. Ths value wll also be checked when hstory matchng s performed. In ths example, the EUR calculated from type curve matchng s 3,666 MSCF whch s very close to 3,679 MSCF calculated from declne curve analyss. The outcome of hstory matchng process s a set of reservor propertes such as permeablty, fracture half length, and dranage area. The type curve matchng requres knowledge about a set of reservor parameters. These parameters are used to calculate permeablty, fracture half length, dranage area and EUR. These parameters are: o Intal reservor pressure, o Average reservor temperature, o Gas specfc gravty, o Isotropcty ( k x / k y rato), o Dranage shape factor (L/W rato), o Average porosty, o Average pay thckness o Average gas saturaton, and o Average flowng bottom-hole pressure. Most of these parameters can be (and usually are) guessed wthn a partcular range that s acceptable for a partcular feld. The values of some of these propertes were selected to be n a close range wth the actual data found n the lterature 5, 6, 7. These data were used as default for the entre reservor. Many tmes the results of hstory matchng wll force us to go back to declne curve analyss and type curve matchng and reevaluate our declne curve results and type curve match. Ths rgorous teratve process s the key to the successful completon of ths process. Agan, the 30 year EUR s a controllng pont that the value we get n hstory matchng should be n a reasonable range wth the one calculated from declne curve analyss and type curve matchng. Once the match s obtaned, the ground work has been establshed for another mportant step n ths analyss, namely Monte Carlo Smulaton. Snce most probably we have dverged from the reservor characterstcs that we started the hstory matchng wth, t s reasonable to expect that we have converged on a range of values for each of the parameters rather than a sngle value. Monte Carlo Smulaton s a good way to get a more realstc look at the capabltes of a well and ts potental future producton. The result of the Monte Carlo Smulaton s the 30 year EUR n the form of a probablty dstrbuton. If our analyss for a partcular well has been done properly, the values of EUR calculated from declne curve analyss and hstory matchng should fall wthn the probablty dstrbuton that has been calculated from Monte Carlo Smulaton. As the values of these EURs get closer to the hgher probable values, our confdence on the accuracy of the ranges (of the reservor characterstcs) that were used n the Monte Carlo Smulaton wll ncrease. Fgures 4 and 5 show the results of hstory matchng and Monte Carlo Smulaton for a well n Carthage feld. If durng the type curve matchng process, a good match between the two EURs calculated from declne curve analyss and type curve matchng s not obtaned, we should return to declne curve analyss and try to get a match wth a dfferent value of b and by havng ths new value ofb, we wll repeat the type curve matchng process. Ths procedure should be repeated untl an agreement between the two calculated EURs s obtaned. Fgure 4 shows the results of hstory matchng for well W3107.

4 4 SPE map ndcate the RRQI values wth RRQI 1 (the darkest color) beng the hghest qualty. These maps wll help the engneer to dentfy the sweet spots as the best locatons for n-fll drllng and also by super mposng dfferent maps, dentfyng the underperformer wells. Fgure 5 shows the results of Monte Carlo Smulaton for W3107. Once the ndvdual analyss for all the wells n the feld s completed, the followng nformaton for all the wells n the feld s avalable: ntal flow rate, q, ntal declne rate, D, hyperbolc declne,b, permeablty, k, dranage area, A, fracture half length, X f, and 30 year Estmated Ultmate Recovery, EUR. Producton ndcators (PI) are calculated for each well. These PIs offer a measure of the well s producton capablty, whch can be used for comparson wth the offset wells. The PIs that are automatcally calculated for each well are the best 3, 6, 9, and 12 months of producton, frst 3, 6, 9, and 12 months of producton, three year cumulatve producton, fve year cumulatve producton, ten year cumulatve producton, and current cumulatve producton 8. Addtonal PIs can also be ncluded from declne curve analyss and type curve matchng. The reservor can be parttoned based on each one of these PIs and the Relatve Reservor Qualty Index (RRQI) values are generated. The dentfcaton of underperformer wells n IPDA s a mult-level analyss. Each level ncludes the nvolvement of two producton ndcators. In ths analyss, frst 3 months of producton and frst 3 years of producton are used as the producton ndcators for level one. In order for a well to be consdered as an underperformer, two condtons must be met: 1. Its value for the partcular PI that s beng analyzed must be n the bottom 25% of the PI values of all the wells that belong to the same RRQI. 2. Its PI value must be lower than the average PI value of all the wells that belong to the next RRQI (lower qualty porton of the reservor.) Results and Dscusson The methodology descrbed n ths paper was appled to producton data from 349 wells producton n Carthage feld Cotton Valley formaton n Texas. The only data used to perform the analyss descrbed here was the producton data that s publcly avalable. The frst step n the process s performng declne curve analyss, type curve matchng and hstory matchng usng a sngle-well radal reservor smulator on all the wells n the feld. These technques are performed smultaneously n an teratve process untl convergence to one unfed set of reservor characterstcs and EURs s obtaned for each ndvdual well whle keepng the ntegrty of the whole reservor as one system. Fgure 8 shows the results of declne curve analyss, type curve matchng and hstory matchng on well W3483 n Carthage feld. The EURs from these three methods are calculated to be 1233, 1235 and 1224 MMSCF, whch are reasonably close to one another. Fgure 7 shows the results of Monte Carlo Smulaton on W Fgure 6 shows the RRQI based on best 3 months of producton. As an example, fgure 6 shows a two-dmensonal map of 349 wells n the Carthage feld that have been parttoned based on the best 3 months of producton. The numbers on the Fgure 7 shows the results of Monte Carlo Smulaton on W3483.

5 SPE Fgure 9 shows the partton map of the frst 3 months of producton. Fgure 10 shows the partton map of the frst 3 years of producton. Fgure 8 shows the declne curve analyss, type curve matchng and hstory matchng on well W3403 Fgures 9 and 10 show the feld parttonng based on the frst 3 months and frst 3 years of producton. Notce the selected wells on the western part of the feld have been moved from one partton (RRQI 1) to another (RRQI 2.) Ths could ndcate the relatve reservor depleton throughout tme. Fgure 11 shows the parttonng of the reservor based on the last month s producton of the feld. Comparng the fuzzy pattern recognton curves along wth the lattude and longtude, one may notce sgnfcant changes between fgures 9 and 10 when compared to that of fgure 11. It s obvous that the sweet spot (partton wth RRQI 1) has moved to the northern part of the feld. Fgure 11 shows the partton map for the last month of producton. Fgure 12 shows the parttonng map of the feld based on permeablty. The underperformer wells are dentfed based on the rules descrbed n the methodology. These wells are shown n blue. Fgure 13 shows the three dmensonal vew of dranage area, fracture half length and permeablty patterns n Carthage feld, Cotton Valley formaton n Texas. The patterns show the hgh permeablty zones are located n the north western part of the feld. The dranage area map shows hgher values of dranage areas n the mdsecton towards north western part

6 6 SPE of the feld. Also, the mdsecton towards north western part of the feld has hgher values of fracture half length. Managers, engneers and geologsts would be able to use these maps to make strategc decsons for development of the feld, such as dentfyng the sweet spots for nfll drllng. 8. Gaskar, R., Mohaghegh, S.D. and Jalal, J., An Integrated Technque for Producton Data Analyss wth Applcaton to Mature Felds, SPE , 2006 Fgure 12 shows the parttonng map of the feld based on permeablty. The underperformer wells are shown n blue. Conclusons The technque presented n ths paper ntegrates the three producton data analyss technques (declne curve analyss, type-curve matchng, and sngle-well numercal reservor smulaton for hstory matchng) through an teratve process n order to remove the subjectvty of each of these methods to come up wth a set of representatve reservor characterstcs. In addton, ntellgent technques such as fuzzy pattern recognton are used n order to make decsons on dentfyng locatons for nfll drllng and underperformer wells. References 1. Arps, J.J., Analyss of Declne Curves, Trans., AIME, 1945, 160, Fetkovch, M.J., Declne Curve Analyss usng Type Curves, JPT, June 1980, Mohaghegh, S.D., Gaskar, R. and Jalal, J., New Method for Producton Data Analyss to Identfy New Opportuntes n Mature Felds: Methodology and Applcaton, SPE 98010, Cox, Kuuskraa and Hansen, Advanced Type Curve Analyss for Low Permeablty Gas Reservors, SPE 35595, D. Nathan Meehan, Numercal Smulaton Results n the Carthage Cotton Valley Feld, SPE 9838, Hnn Jr., R.L., Glenn, J.M., McNchol, K.C., Case Hstory: Use of a Multwell Model to Optmze Infll Development of a Tght-Gas-Sand Reservor, SPE 14659, E.J. Schell, Dranage Study n the Carthage (Cotton Valley) Feld, SPE 18264, 1988

7 Dranage Area Fracture Half Length Permeablty Fgure 13 shows the parttonng maps of dranage area, fracture half length and permeablty for the entre reservor.