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`SPE 102681
`
`Restimulation: Candidate Selection Methodologies and Treatment Optimization
`L.P. Moore, SPE, and H. Ramakrishnan, SPE, Schlumberger
`
`
`
`Copyright 2006, Society of Petroleum Engineers
`
`This paper was prepared for presentation at the 2006 SPE Annual Technical Conference and
`Exhibition held in San Antonio, Texas, U.S.A., 24–27 September 2006.
`
`This paper was selected for presentation by an SPE Program Committee following review of
`information contained in an abstract submitted by the author(s). Contents of the paper, as
`presented, have not been reviewed by the Society of Petroleum Engineers and are subject to
`correction by the author(s). The material, as presented, does not necessarily reflect any
`position of the Society of Petroleum Engineers, its officers, or members. Papers presented at
`SPE meetings are subject to publication review by Editorial Committees of the Society of
`Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper
`for commercial purposes without the written consent of the Society of Petroleum Engineers is
`prohibited. Permission to reproduce in print is restricted to an abstract of not more than
`300 words; illustrations may not be copied. The abstract must contain conspicuous
`acknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O.
`Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.
`
`Abstract
`a vast
`represents
`existing wells
`Restimulation of
`underexploited resource. A successful refracturing treatment
`is one
`that creates a fracture having higher fracture
`conductivity and/or penetrating an area of higher pore pressure
`than the previous fracture. Refracturing requirements are
`different in highly permeable formations (high fracture
`conductivity) as compared to low permeable ones (moderate
`fracture conductivity). Understanding these basic differences
`is essential to a successful restimulation.
`
`In the past, candidate selection methodology has
`
`focused on underperforming wells. This simplistic approach
`has yielded disappointing results and has led to a common
`misconception that restimulations “don’t work.” Production
`statistics of a well alone may not offer an effective
`restimulation candidate selection methodology.
` Other
`parameters such as high BHP (remaining reservoir energy),
`recoverable reserves, f -h1 and favorable response to original
`fracture jobs (IP) could play an equally important role, if not
`greater, in determining the success of restimulation. In fact,
`studies have shown that selecting poor or underperforming
`wells for restimulation is likely to result in worse outcomes
`than random selection of workover candidates.
`
`Studies performed to date have concluded that no
`
`selection criteria can be universally applied to every situation;
`rather that the selection methodology for workover candidates
`must be customized to fit particular situations. This paper
`explores the common traits shared by fields likely to have
`underexploited
`restimulation
`potential
`and
`suggests
`methodologies that should be applied to various field types.
`The case histories illustrated in this paper will highlight the
`various
`treatment parameters optimized
`for successful
`restimulation. The conclusions of this paper are based on our
`work in selecting restimulation candidates as well as published
`
`results of other operators. Application of the correct candidate
`selection methodology to a particular field type will inevitably
`lead to a higher success rate of restimulation walkovers and
`the capture of an underexploited resource.
`
`Introduction
`Selection criteria of refracture candidates from a given set of
`wells in a field will vary depending on the reservoir under
`question and the prevailing well conditions. To be successful,
`refracturing treatments must result in longer and/or mo re
`conductive propped fractures, or expose more net pay
`thickness to the wellbore (establishing linear flow into the
`wellbore) as compared to the well conditions that exist prior to
`restimulation.
`
`In light of the above, two key important aspects of
`
`any restimulation program or attempt are: (i) learn from
`exis ting experience in the field or area about restimulation and
`formulate a reservoir specific selection criteria that will
`capture the key ingredients for the success of restimulation
`and (ii) a thorough understanding of the treatment parameters
`that govern the success of a restimulation job so as to be able
`to optimize the treatment for maximum rate of return.
`Advances in the design and evaluation software, improved
`diagnostic
`techniques, etc, have played a key role
`in
`restimulation success during the past ten years, as have the
`technological advances in stimulation fluids and proppants.
`This paper will focus on the common attributes shared by
`known successful restimulation candidates in the industry. An
`attempt will be made to highlight the attributes mentioned in
`this paper through some well-known case histories. Based on
`the exis ting knowledge base on the restimulation identification
`process, a systematic and comprehensive candidate selection
`methodology will be presented in this paper. Towards the
`end, a list of factors to look for while evaluating a field or
`reservoir for restimulation will be highlighted for reference.
`
`Restimulation Activity – Past and Present
`The objective of any fracture stimulation treatment either
`during
`initial completion or during restimulation
`is
`to
`definitely bypass the near-wellbore damage, penetrate deeper
`into the reservoir and increase connectivity with the reservoir.
`Refracturing attempts started soon after the introduction of
`hydraulic fracturing in about 1947, but early applications
`required significant effort in problem diagnosis and candidate
`selection, with mixed results.
` Until
`the
`last decade,
`restimulation was generally not considered a good option. It
`was sometimes believed that restimulation treatments could
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`polymer concentrations were gradually reduced, nitrogen was
`used to enhance flowback and the overall fluid and proppant
`volumes were increased. Further improvements were made in
`the form of increasing the breaker systems and elimination of
`solid fluid-loss additives. These improvements in the job
`treatment design resulted
`in continuous increases in well
`productivity. Slick-water treatments became the predominant
`stimulation method in 1997. Refracturing with large job
`volumes yielded much better productivities than the initial
`completions (Fig. 3). Much of the success of restimulation in
`the Barnett has come from slick-water jobs that followed
`cross-linked jobs.
`
`Analysis of near- and far-stress fields in the Barnett
`
`indicate that the new fractures created during restimulation
`followed the original fracture plane for a short distance before
`diverging along a new direction,7,8. As the Barnett shale has
`minimum anisotropy, fracture reorientation is a definite
`possibility and so is the development of a complex fracture
`network that will further enhance well productivity by
`contacting virgin rock away from the wellbore. Knowledge of
`these concepts and its potential implications are being studied
`even today. It has also been noted that fracture reorientation
`does not always occur on every restimulation performed in
`Barnett Shale.
`
`Increased understanding of
`fracture
`reorientation and complex fractures is made possible with the
`help of micro-seismic hydraulic
`fracture monitoring
`techniques. While it is felt that fracture reorientation is an
`important factor in the success of Barnett restimulations,
`additional study will be required to predict where it will be a
`key driver in the candidate selection process.
`
`Case History #2: Vicksburg Basin Restimulation
`Candidates Selection (Traits #1, #2 and #4)
`Like the Barnett, the Vicksburg is a complex reservoir that has
`undergone a technology evolution since wells were originally
`stimulated. However, it also has common trait number 4,
`mu ltiple producing horizons. While it is a safe bet that a field
`possessing 3 of
`the 4 common
`traits associated with
`restimulation potential will have candidates, the selection of
`those candidates requires careful screening.
`
` Completions in this basin date back to the 1970’s and
`1980’s. Typically, Vicksburg is a tight gas sand with
`permeabilities ranging between 0.005md and 0.1md. A
`Vicksburg basin study identified underperforming wells and
`explored the use of improved well-completion practices and
`restimulation techniques. Many wells had multiple zone
`completions with limited entry techniques being applied on
`some of these zones. Schlumberger together with Kerr-
`McGee formulated an
`integrated workflow
`to
`identify
`refracture opportunities. Key elements are Moving Domain
`Analysis (MDA) to sort through and organize production
`statistics and, development of a specific petrophysical model
`to identify bypassed gas zones. Fig. 4 gives an example of the
`various factors that were considered for initial screening
`before doing a detailed production analysis on a well-by-well
`basis. The Vicksburg restimulation program had a success
`rate greater than 80% because key performance drivers were
`identified and a highly customized selection methodology was
`
`not be economically justified or that it might even be
`preferable to abandon the well, as operators were wary about
`resorting to restimulation due to bad experiences with
`refracturing in the past.
`
`Between 1996 and 1998, the Gas Research Institute
`
`(GRI), now called the Gas Technology Institute (GTI),
`conducted a study investigating fracture restimulation as a
`means of enhancing well productivity and adding recoverable
`reserves. This preliminary evaluation study resulted in the
`identification of significant on-shore gas potential in the
`United States (Fig. 1)2. High product prices and higher
`development costs have cultivated a renewed interest in
`restimulation among operators worldwide.
` From
`the
`restimulation activity so far in United States and other areas,
`including China, Algeria, Brazil and Russia, it is evident that
`great refracturing potential exists worldwide, even in mature
`oil fields3,4,5,6.
` Fig. 2 represents a map showing the
`restimulation activity areas in the southern part of the United
`States. Some of the cases histories presented in the later
`sections of this paper fall in this area.
`
`Common Traits of Potential Restimulation Areas
`The common traits of areas with restimulation potential can be
`classified as follows:
`1. complex reservoirs with problematic initial completions;
`2. plays with important technological improvements;
`3. older wells that have suffered damage during production;
`4. plays with multiple producing horizons that may have been
`stimulated with limited entry techniques.
`
`These traits will be highlighted in each of the case
`
`histories discussed in this paper to emphasize the sub
`comp onents of each of these traits. Though the above-
`mentioned set of traits is a broad classification, it can
`definitely serve as a guideline for candidate selection and key
`parameters
`to consider while selecting candidates
`for
`restimulation.
`
`Restimulation – Case Histories
`
`Case History #1: Barnett Shale Gas Restimulation
`Program (Traits #1 and #2)
`The Barnett Shale exhibits common traits 1 and 2. It is an
`extremely complex reservoir that has undergone an evolution
`in stimulation practices during the history of its development.
`The Barnett is a Mississippian-age shale formation, which has
`been deposited in a deep marine environment. Barnett Shale
`consists of layered mudstone, siltstone and some interbedded
`limestone with open and calcite-filled natural fractures 2.
`Matrix permeability is extremely low about .0001 to .001 mD.
`With a calculated recovery of 8 to 10% of the gas in place,
`massive
`fracturing
`treatments are
`required
`to achieve
`economic production from this shale formation.
`
`Barnett Shale completions started in the 1980s, when
`
`fracturing treatments involved the use of high polymer gel
`loading,
`crosslinked-gel
`fluids, moderate
`proppant
`concentrations with minimal external breaker added to the
`fluid. With time, the stimulation treatments evolved and the
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`SPE 102681
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`3
`
`Methodology
`
`–
`
`
`Case History #5: Artificial Neural Networks to identify
`Restimulation Candidates
`In this paper12, artificial neural network analysis is discussed
`as a way to identify restimulation candidates in the Red Oak
`field. Development of an artificial neural network for any
`application involves preparing the data, determining the
`relative significance of variables, training the neural network
`and evaluating its suitability in predicting outcomes. It must
`be mentioned that several of the key input variables used in
`this network training have to do with refracture procedure
`applied during well recompletion such as refracture fluid type,
`fluid volume, proppant weight and proppant type.
`
`Selection
`Candidate
`Comprehensive Approach
`An effective selection methodology is essential to a successful
`restimulation program. There is no one set of selection criteria
`that can be applied to every situation. However, it is possible
`to formulate a framework for proceeding with the candidate
`selection (for a certain field) irrespective of circumstances.
`The following steps were formulated as a comprehensive
`approach to candidate selection by Schlumberger Data and
`Consulting Services.
`
`1. Literature Review
`The first step is to review any published information that
`could shed light on the area in question, particularly published
`data regarding stimulation techniques. Particularly for areas
`that have either of common traits 1 or 2, it can be very useful
`to learn from the published experiences of others. This is a
`critical first step.
`2. Moving Domain Analysis (MDA) or Artificial Neural
`Network - Phase 1 Scoping Study
`Numerous papers have been written describing the utility of
`MDA in identifying and quantifying infill opportunities.
`MDA is also quite useful for organizing statistical data and
`identifying drivers of well performance. An analysis of the
`field area using data available in the public domain will shed
`light on the area and may point to superior completion
`techniques that could lead to restimulation opportunities.
`Artificial Neural Network software serves a similar function
`by providing a more automated means to screen a large
`number of wells. The purpose of both of these techniques is
`not to select candidates but to eliminate them.
`3. Performance Based Screening
`It has been demonstrated that in some areas the poorest
`performers also make the poorest restimulation candidates. In
`general,
`this
`tends
`to be
`the case for complex,
`low
`permeability, noncompetitive reservoirs.
` In other,
`less
`complex, competitive reservoirs, under-performance can prove
`to be an effective selection criterion. However, even when
`restimulation candidates cannot be advanced based on under-
`performance, it may still be possible to screen candidates
`based on performance, if only to eliminate from consideration
`under-performing wells. In other words, performance can
`nearly always be used as a screening for selection of
`restimulation candidates. How it will be used is dependent on
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`A
`
`developed.
`
`Case History #3: Codell DJ Basin Refracture Program
`(Traits #1 and #2)
`The Codell restimu lation program involved the successful
`restimulation of literally thousands of wells. It shares
`common traits 1 and 2.
`
`
`Codell Sandstone is Upper Cretaceous9,10 in age and
`produces condensate and gas with little water at true vertical
`depths of 7,000 to 8,000 ft. The permeability in the Codell
`interval is very low due to the small and tortuous pore
`network.
` Though
`limited entry
`treatments provided
`substantial cost savings in completing Codell and Niabrara
`formations together, the amount of proppant pump ed into
`Codell formation was also lower. Besides using limited entry
`technique for stimulation, high polymer loading fluids with
`basic persulfate breakers were used as the fracturing fluid.
`Thus, wells were not being stimulated effectively resulting in
`shorter fracture half-lengths and gel damage from the high
`polymer fracturing fluids. These are the two major reasons for
`the initiation of the Codell refracture program.
`
`During the course of the refracture program, the high
`
`polymer hydroxypropyl guar (HPG) was replaced with a low
`polymer fluid system Carboxy Methyl Guar (CMG) polymer.
`This polymer
`is a single derivative guar and
`is not
`propoxylated like Carboxy Methyl HPG. For the CMG to
`achieve the enhanced chain expansion, clean potable water
`with extremely low total dissolved solids is required. The
`success of this refracture program is to be attributed to the
`evolution of fracture technology and real-time supervision
`(fracture diagnostics) to keep the fracture in the zone of
`interest. Fracture reorientation phenomena have also been
`observed quite commonly in this basin to a point that
`sometimes the same zone has been restimulated twice after the
`initial stimulation. Fig. 5 shows a typical post-refracture
`treatment response in the Codell.
`
`Case Hi story #4: Weighted Parameter Candidate Selection
`Methodology applied to a Gas Field in China (Traits #2
`and #3)
`This field exhibited common traits 2 and 3, i.e., an area where
`technology improvements created opportunities and older
`wells that had been damaged during the course of their
`producing lives.
`
`
`The SinoPec Xinchang gas reservoir is located in
`southwest China11. It is a tight sandstone formation with
`natural fissures. As the production rate of individual wells
`was going through a decline, restimu lation was considered as
`an option to maintain the overall field production. The
`preliminary candidate selection methodology used to rank the
`wells
`involved a weighted parameter approach with
`parameters like kh, cumulative production, previous treatment
`etc. (Fig. 6) It was determined that drainage area has
`significant effects on long term production and so it was key
`to obtain longer fracture half-length and optimize well
`placement. Fig. 7 shows the production increase obtained
`after restimulation in one of the selected wells.
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`SPE 102681
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`influenced by factors unrelated to the selection process, such
`as unforeseen mechanical issues arising during the workover.
`In addition to hitting homeruns with the initial attempts at
`restimulating wells in a field, one of the primary objectives at
`this stage in the program is to fine-tune the selection process
`by verification that the drivers used to identify wells in need of
`restimulation are effective. Lessons learned from actual
`results will
`typically
`reveal other
`important selection
`considerations.
`
`the type of reservoir being studied. Before proceeding to Step
`4, if wa rranted, production type curve matching to provide an
`indication of reservoir quality and completion efficiency can
`be performed. In all cases, there must be sufficient remaining
`reserve potential for a given well to warrant consideration for
`restimulation.
`
`4. Well Data Review
`The primary objective of the preceding three steps was to shed
`some light on which wells in a field might hold promise as
`restimulation candidates without actually having to perform a
`close examination of well file data. The review of public data
`and production histories should have narrowed the focus of
`further review considerably, both in terms of numbers of wells
`to scrutinize and what, in particular, to look for in a
`restimulation candidate. For example, fracture fluid, proppant
`type, perforation scheme, or some other completion peculiarity
`may have been identified as a potential cause of under-
`performance from the screening phase. A review of well file
`data will enable an evaluation of the current well condition as
`well as a complete compilation of the well history. It is likely
`that additional wells could be eliminated from consideration in
`this step based on their mechanical condition.
`
`Identification of Key Drivers and Indicators
`5.
`A thorough review of a number of well files from the field of
`interest should confirm the existence of key drivers of well
`performance. Based on these drivers, a candidate ranking
`system can be devised that will serve to further screen wells in
`the field. Production data analysis can be performed on the
`wells surviving this additional level of screening so that
`forecasted production resulting from a refracturing treatment
`can be derived. The top candidate wells based on this analysis
`can move on to the next step.
`6. Evaluation of Best Candidate Wells
`Integrated Evaluation Methodology)
`integrated
`(well,
`reservoir, completion) evaluation
`An
`methodology should be performed on every well before the
`restimulation, at least in the beginning of the study. This
`integrated evaluation methodology will help to verify that the
`key drivers identified in step 5 are just that, and that there are
`not other equally important, though less obvious, indicators
`that could have been missed, e.g., misidentification of pay in
`the original completion.
`7. Restimulation of Best Candidate Wells
`At this stage in the process, the operator should stimulate the
`wells shown to be the best prospects. The integrated
`evaluation should have resulted in an optimized treatment that
`will yield a longer and/or more conductive fracture or contact
`portions of the reservoir that were untouched in the original
`treatment.
`8. Evaluation of Results and Revision of Selection
`Criteria
`In practice, it is necessary to validate the selection of the
`candidate wells by pumping the restimulation treatments and
`comparing actual results with forecasted rates. Ideally, at least
`three wells should be restimulated because of the likelihood
`that the results of one or two workovers could be unduly
`
`(using an
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`
`Key Factors Leading To Restimulation Candidate
`Identification
`Given below is a partial list of key factors to consider or
`compare to be able to identify restimulation candidates:
`• Relatively high reservoir pressure (energy) indicating
`that a significant portion of initial reserves is still in
`place.
`• Low productivity relative
`to other wells with
`comp arable pay in a homogenous reservoir.
`• Damaged wells with a high ratio of remaining
`reserves to the existing production rate.
`• Wells with high k and high skin value
`• Old completions with less than optimal treatments.
`• Limited entry completions.
`• Scale, embedment of proppant, proppant crushing,
`proppant flowback.
`• Possibility of fracture reorientation.
`• Previous treatments that had screened out.
`• Treatments that had used incompatible fluids.
`• Smaller original stimulation treatment in comparison
`to the deliverability of the reservoir.
`• Reservoir
`complexity
`leading
`problems.
`
`to
`
`completion
`
`
`
`Optimizing Fracture Penetration And Fracture
`Conductivity
`Treatment optimization of any restimulation treatment will
`essentially follow the same guidelines as for a new completion
`stimulation treatment. According to Pratz13, the steady-state
`productivity improvement of fracturing was related to the
`Dimensionless Fracture Capacity, FCD. This term is described
`as the ratio of fracture’s ability to flow fluids from fracture tip
`to the wellbore, to the reservoir’s ability to flow fluids from
`the reservoir to the fracture face and is defined as:
`
`
`
`where kfw is the fracture conductivity, k is the res ervoir
`permeability, and X f is the fracture half-length. Fig. 8 is a log-
`log plot developed after Prats of the steady-state folds of
`increase (FOI) versus Relative Conductivity for fracture half-
`lengths of 100, 500 and 1000 ft. Based on this observation,
`restimulation treatments in wells in permeable formations
`should be designed
`to maximize fracture conductivity.
`Alternatively, restimulation treatments in low permeability
`formations should be designed to increase fracture half-length.
`
`
`FCD = (kfw/kXf)
`
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`European Petroleum Conference, Paris, France, October 24-
`25, 2000.
`
`7. Siebrits, E., Elbel, J. L., Hoover, R. S., Diyashev, I. R.,
`Griffin, L. G., Wright, C. A., Davidson, B. M., Steinsberger,
`N. P., Hill, D. G.: “Refracture Reorientation Enhances Gas
`Production in Barnett Shale Tight Gas Wells,” paper SPE
`63030, presented at the SPE Annual Technical Conference
`and Exhibition, Dallas, Texas, USA, October 1-4, 2000.
`
`8. Wright, C. A. and Conant, R. A.: “Reorientation of propped
`refracture treatments,” paper SPE 28078, presented at the
`Eurock SPE/ISRM Rock Mechanics
`in Petroleum
`Engineering Conference, Delft, The Netherlands, August
`29-31, 1994.
`
`9. Shaefer, M. T. and Lytle, D. M.: “Fracturing Fluid
`Evolution Plays a Major Role in Codell Refracturing
`Success,” paper SPE 71044, presented at the SPE Rocky
`Moutain Petroleum Technology Conference, Keystone,
`Colorado, USA, May 21-23, 2001.
`
`10. Sencenbaugh, R. N., Lytle, D. M., Birmingham, T. J.,
`Simmons, J. C. and Shaefer, M. T.: “Restimulating Tight
`Gas Sand: Case Study of the Codell Formation,” paper SPE
`71045, presented at the SPE Rocky Moutain Petroleum
`Technology Conference, Keystone, Colorado, USA, May
`21-23, 2001.
`
`11. “Best Practice: Comprehensive Candidate Selection Process
`Leads
`to
`the Success of Refracturing Project” –
`Schlumberger InTouch Best Practice Article shared by
`Schlumberger, China.
`
`Identify
`12. Shelley, R. F.: “Artificial Neural Networks
`Restimulation Candidates in the Red Oak Field,” paper SPE
`52190, presented at the SPE Mid-Continent Operations
`Symposium, Oklahoma City, Oklahoma, USA, March 28-
`31, 1999.
`
`13. Reese, J. L., Britt, L. K. and Jones, J. R.: “Selecting
`Economic Refracturing Candidates,” paper SPE 28490,
`presented at the 69th SPE Annual Technical Conference and
`Exhibition, New Orleans, Louisiana, USA, September 25-
`28, 1994.
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`Conclusions
`Restimulation is an area, which is fast attracting attention
`among operators due to the increasing need and confidence in
`the process. Needless to say, candidate selection remains a
`key step in any restimulation program to have a high
`probability of success.
`
` The areas ripe for restimulation have one or more of the
`common traits mentioned in this paper.
`• The
`the
`in
`ranking system applied
`to candidates
`the
`to
`Vicksburg basin
`study yielded
`success
`restimulation program whereas the gas field in China
`followed a weighted average approach for candidate
`selection.
`• When considering a field for restimulation that has more
`than a few wells, a quick first pass evaluation using
`moving domain analysis or neural network
`type
`techniques using the readily available data could narrow
`down or prioritize the wells for in-depth analysis.
`• Stimulation treatment parameters such as total fluid
`volume, fluid type, and proppant volume and proppant
`type are observed to have a major impact on the outcome
`of any restimulation campaign.
`• Candidate selection methodology must be tailored to the
`field considered for restimulation.
`• Though there is not a universal candidate selection
`methodology that can be applied to every field, the
`generic steps outlined in this paper takes the user through
`the evaluation process in a systematic fashion to arrive at
`a few candidates for restimulation.
`
` •
`
`
`References
`1. Fleming, M. E.: “Successful Refracturing in the North
`Westbrook Unit,” paper SPE 24011, presented at the SPE
`Permian Basin Oil and Gas Recovery Conference, Midland,
`Texas, USA, March 18-20, 1992.
`
`2. Dozier, G., Elbel, J., Fielder, E., Hoover, R., Lemp, S.,
`Reeves, S., Siebrits, E., Wisler, D. and Wolhart, S.:
`“Refracturing Works,” Schlumberger Oilfield Review
`Article, Autumn 2003.
`
`3. Pospisil, G., Lynch, K. W., Pearson, C. M. and Rugen, J. A.:
`“Results of a Large-Scale Refracture Stimulation Program,
`Kuparuk River Unit, Alaska,” paper SPE 24857, presented
`the 67th SPE Annual Technical Conference and
`at
`Exhibition, Washington, DC, USA, October 4-7, 1992.
`
`4. Olson, K. E.: “A Case Study of Hydraulically Refractured
`Wells in the Devonian Formation, Crane County, Texas,”
`paper SPE 22834, presented at the 66th SPE Annual
`Technical Conference and Exhibition, Dallas, TX, USA,
`October 6-9, 1991.
`
`5. Wright, C. A., Stewart, D. W., Emanuele, M. A., Wright, W.
`W.: “Reorientation of Propped Refracture Treatments in the
`Lost Hills Field,” paper SPE 27896, presented at the SPE
`Western Regional Meeting, Long Beach, California, USA,
`March 23-25, 1994.
`6. Marquardt, M. B., van Batenburg, D. and Belhaouas, R.:
`“Production Gains from Re-Fracturing Treatments in Hassi
`M essaoud, Algeria,” paper SPE 65186, presented at the SPE
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`Fig. 1: Areas with restimulation potential in the USA based on the Gas Technology Institute (GTI) study
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`Codell DJ Basin
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`Anadarko Basin
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`Brown Dolomite
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`Granite Wash
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`Barnett Shale
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`Cotton Valley
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`Vicksburg
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`Fig. 2: Restimulation activity areas in the southern part of United States
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`6
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`100,000
`Gas
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`10,000
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`1,000
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`1997 1998 1999 2000 2001 2002
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`Fig. 3: Typical Restimulation Response for a Barnett Shale Well
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`Candidate Indicator
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`Gas Best 12
`
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`60 Productive Months Cum
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`(Ranking for workover review &
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`pre-production trend analysis)
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`Water prod. vs. Gas prod ratio
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`Gas Decline Trend
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`**Proppant type & amount
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`(Ranking for production
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`modelling, economic analysis,
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`24
`10
`14
`pressure survey)
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`Excellent
`Poor
`Marginal
`Result
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`Fig. 4: Sample Screening Criteria used in Vicksburg Basin Study
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`Well #3
`5
`5
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`10
`5
`5
`4
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`Well #1
`3
`2
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`Well #2
`3
`1
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`5
`4
`4
`1
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`4
`2
`3
`1
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`Fig. 5: Typical Response of a Codell Refracture Treatment
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`Fig. 6: Weighted Parameter Approach used for Preliminary Candidate Selection
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