SPE-170702-MS

Optimizing the Selection of Lateral Re-Entry Wells through Data-DrivenAnalytics

Andrei S. Popa, Chevron Corporation

Copyright 2014, Society of Petroleum Engineers

This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in Amsterdam, The Netherlands, 2729 October 2014.

This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contentsof the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflectany position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the writtenconsent 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 maynot be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract

A new intelligent model that successfully learns from high-dimensional data and effectively identifieshigh production areas and optimum Lateral Re-Entry well candidates is presented. The model is entirelydata-driven and uses WM rules extraction, fuzzy logic, pattern recognition and Voronoi mapping. Weapplied our model to a large field with thousands of wells and multiple production layers. We found thatit significantly outperforms the previous methodologies, not only by identifying new production areas butalso returning a two fold production increase in 2013 compared to prior approach.

The paper showcases a successful project focused on revitalizing poor performing vertical producersby sidetracking and drilling lateral sections into zones with remaining opportunity in order to improvemargins and drain reserves more efficiently. The approach uses artificial intelligence technology tools toscreen reservoir targets and generate potential candidates. In the first step WM rules extraction algorithmswere run for each one of the main reservoir attributes (permeability, thickness, saturation, temperature,etc.) in the nine formations. The results were used to build a fuzzy decision system to generate aconfidence index for each grid block in the 155 million cell full field earth model. Confidence index mapswere created to pinpoint prime productive areas and help evaluate optimum directional paths. Lastly,Voronoi delineation were used to estimates a drainage area per well, remaining reserves and movable oilin place.

Applying analytics has breathed life into an asset previously thought to have limited growth potential.The future looks bright with a queue of hundreds of well candidates identified and awaiting execution overthe next years.

IntroductionData-driven modeling is finding a growing number of significant applications in a variety of fields rangingfrom pattern recognition, classification, prediction, system approximation, and process control [Chiu1997]. The extent of the application covers almost every engineering domain from chemical, mechanical,civil, electrical, and biological engineering to name a few. This paper demonstrates the application of thetechnology to a relative new domain, petroleum engineering, and particularly for production opportunityidentification and wellwork optimization.

Kern River Field is the single largest producing onshore heavy oil asset in North America. Thestructure is a homoclinal, dipping southwest into the basin, with nine distinctive productive formations.The field has been produced with vertical wells through a combination of primary and thermal enhancedrecovery. A detail geological description and production history is presented in the work completed byBeeson, [Beeson, 2012].

With more than 100 years production and more than 20,000 wells drilled since its discovery in 1899,the field presents a great opportunity for application of data driven analytics. In spite of the large numberof wells and the age of the field, the data is very accurate and complete. The large number of logs recordedin the wells, together with the information collected from the observation wells allowed the developmentof a state-of-art full field 3D earth model consisting of 155 million cells [Swartz, 2008].

The horizontal well program was re-initiated in the Kern River field in 2007, and due to its success theprogram witnessed a significant growth year after year. An intelligent approach using fuzzy logic wasintroduced in 2012, which allowed identification of new horizontal well opportunities previously missedby the conventional methodology, [Popa, 2013]. More than 500 horizontal wells were drilled in the fieldtoday. This large number of wells provided a great data set for extracting reservoir related knowledge forfuture optimization.

The work presented in this study proposes a practical, easy-to-use methodology for identification ofhigh production areas and optimizes the workover program. The methodology extracts fuzzy rules fromhigh-dimensional, nonlinear input-output relationships existing between reservoir and production data andfeeds a fuzzy inference system which is applied on the full field 3D earth model to identify and rankproduction improvement opportunities.

The the paper is organized as follows: Section II provides the project opportunity; Section IIIintroduces the lateral re-entry workover program, Section IV provides the theoretical background of rulesextraction, fuzzy logic and Voronoi diagrams; Section V explains step by step the methodology and showsthe results, and lastly Section VI draws the conclusion and discuss future opportunities.

Observation and Project OpportunityThe average Kern River production per vertical well is currently just below 7 bopd. Analytics run onproduction data showed that more than 3,900 wells produce less than 3 bopd, while about 2,200 produceanywhere below 1 bopd, Figure 1. This observation creates a significant opportunity for productionimprovement. The large amount of poor performing vertical wells contrasted with the success of thehorizontal wells led to the idea of re-utilizing the existing wellbores by drilling lateral sections into zoneswith remaining opportunity. If successful these new lateral drills would not only improve productionoutput but also help drain remaining reserves more efficiently. These sidetracks were named Re-entryLaterals wells.

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The Re-entry Laterals sidetracks are directionally drilled high angle sections (15 degree/100= DLS)aiming at maximizing reservoir contact area. The wells are completed with slotted liners and penetrateseveral target sands offering higher well exposure to each formation intersected. In contrast with thehorizontal wells which can reach horizontal sections of 2,000ft or more, the Re-entry Laterals wouldextend no more than 400-500 ft at very high angle (85) into formations. Figure 2 shows an example ofa Re-entry Lateral. The vertical line represents the original wellbore while the directional curve shows thehigh angle Lateral Re-entry. The cross section presented in Figure 2 is described as follows; the pale bluecolor indicates siltstones and clays; the white areas indicate reservoir rock that is has very low or no oilsaturation; the legend on the right side shows the color gradation for the oil saturation in the sands; andthe black curve is normalized resistivity. The shaded yellow curve is an indicator of potential air or steamdepending on what the temperature existing at that depth.

Figure 1Distribution of Well Production, bopd

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Similar to the horizontal well placement, the Lateral Re-Entry wells are intended to target theremaining hot oil existing at the base of high quality sand formations. Thus the horizontal sections of thewells could penetrate multiple formations for a significantly longer section compared to vertical entries,thus maximizing the formation exposure to the well.

Technology BackgroundThe model proposed in this study is entirely data-driven and employs tools for rules and knowledgeextraction which feed a fuzzy logic system. Finally Voronoi diagrams are generated to identify remainingreserves and help with well orientation. This section is structured as follows: the Wang-Mendel (WM)method for rules extraction is reviewed first, a brief overview of fuzzy logic systems is provided, andlastly the Voronoi diagrams are introduces and discussed.

Wang-Mendel (WM) MethodWang and Mendel [Wang and Mendel, 1992] proposed a method, now known as the Wang-Mendel (WM)method, for combining both numerical and linguistic information into a fuzzy rule base. The WM methodwas originally proposed to address a control problem. A human controller was an essential component and

Figure 2Cross-Section showing the original completion and the Lateral Re-Entry well

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the environment facing this human controller was so complicated that no mathematical model existed forit. The rules generated using the WM method are if-then rules, e.g., IF x1 is A AND x2 is B, THENy is C, where x1 and x2 are input variables, y is the output variable, and, A, B and C are linguistic terms.

Several methods for extracting fuzzy rules have been developed over the years. However, WM methodwas the first method to extract fuzzy rules from numerical data. It has been applied to a variety of differentproblems [Cox, 1999, Wang, 1994], and is one of the benchmark methods in the field [Hao, 2012]. Givenis relative simplicity and stability the method was selected for this study. A brief review of the originalWM method is provided in this section.

The WM as presented in the original work [Wang Mendel, 1992], can be summarized as five simplesteps:

1. Divide the input and output spaces into fuzzy regions. In this study, this step is done by applyingthe Fuzzy c-Mean algorithm separately to input and output data.

2. Generate fuzzy rules from given data pairs. Suppose we divide both input and output spaces intothree regions using linguistic terms Small, Medium and Large (Figure 3). For the given input-output pair (x1,x2;y), observe that Large(x1)1.0, Small(x2)1.0 and Medium(y)0.5; therefore,the rule generated from this data pair is:

3. Assign a degree to each rule. According to (Wang and Mendel, 1992), since there are usually lotsof data pairs and each data pair generates one rule, it is highly probable that there will be someconflicting rules, e.g., rules have the same IF part but a different THEN part. To resolve thisconflict, the WM method also assigns a degree to each rule, namely, the product of all themembership function values involved in a rule. Using the same example as in Step 2, the degreeof the generated rule is (x1)(x2)(y)1.01.00.50.5.

4. Create a combined fuzzy rule base. This step takes care of the conflicting rules by only keepingthe rule with the highest degree computed in Step 3 and ignoring all others.

5. Determine a formula for the combined fuzzy rule base. This step is not the emphasis of this paper.Additional information and details are presented in the original article [Wang and Mendel, 1992].

Fuzzy Logic SystemsThe fuzzy inference system (FIS) is a computing system which is based on the concepts of fuzzy settheory, fuzzy if-then rules, and fuzzy reasoning [Ross, 1995]. Several fuzzy inference systems havebeen developed and employed in different applications along the years. The most commonly knownmodels are the Mamdani fuzzy model, Takagi-Sugeno-Kang (TSK) fuzzy model, Tsukamoto fuzzy modeland Singlenton fuzzy model [El-Shayeb, 1997]. Among all, the Mamdani model is one of the most

Figure 3Divisions of the input and output spaces into fuzzy regions and the corresponding membership functions, Small(x), Medium(x) andLarge(x). [Hao, 2012]

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common algorithms used in fuzzy systems. A detail overview of Fuzzy Logic system can be found in thework done by El-Shayeb, [El-Shayeb, 2013].

Voronoi DiagramsEngineering analysis often require algorithms able of dividing space into a number of regions. Furtherthese regions or zones can be characterized by their inherit attributes. One of the most popular algorithmsused in computational geometry is the Voronoi diagrams. Given a set of points (called seeds) in theplanar space there is a corresponding region consisting of all points closer to that seed than to any other,[Wikipedia, 2014]. The regions are called Voronoi cells. A simpler definition of the Voronoi diagram isthe division of space into regions around each point that are shaped so that the borders of the regions areequidistant from the two nearest points. Consequently, the Voronoi cells are a visual way of showing theboundaries of influence from each data point.

Voronoi diagrams can be found in a large number of fields in science and technology, even in art, andthey have found numerous practical and theoretical applications. Examples of its application can be foundin health care, where Voronoi diagrams are used to correlate sources of infections in epidemics, nearestneighbor queries where one might want to find the nearest hospital, hotel, airport, etc. or the most similarobject in a database; in climatology, where they are used to calculate the rainfall of an area based on aseries of point measurements; in chemistry, Voronoi cells defined by the positions of the nuclei in amolecule are used to compute atomic charges; in machine learning, used in one-dimension classificationproblems, [Atsuyuki, 2000]. Lastly, of a particular interest is the use of Voronoi diagrams in the miningindustry, where polygons are used to estimate the reserves of valuable materials, minerals, or otherresources. In this particular case the exploratory drillholes are used as the set of points to generate theVoronoi polygons.

The application of Voronoi diagrams in the oil industry is very similar. The existing wells act and theseeds given their location coordinates and the field is divided in regions corresponding to each one ofthe wells. Figure 4a shows an example of Voronoi diagram for a section of the field, where the black dotsrepresent the producing wells. Figure 4b shows the same diagram, however this time the Voronoi cellswere colored using one the well attributes, in this case wellhead temperature.

Figure 4aClassic Voronoi Diagram Figure 4bVoronoi Diagrams - Temperature Distribution

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History of the Lateral Re-Reentry WellsThe Laterel Re-Entry well program started as a Pilot project in 2012 with a ten well package designed totest the lateral sidetrack concept and build upon drilling and completion best practices. The Pilot projectaddressed mainly two components; first the candidate selection practice and second the well design,completion and capital stewardship. The latter will not be discussed here.

Candidates Selection processs Conventional ApproachThe initial candidate selection process followed closely the conventional approach used for horizontalwells. Individual layer maps using strict cut-offs were created for the main attributes involved in theselection criteria. The experience and field knowledge showed that temperature, thickness, permeabilityindex and saturation, are among the most important when selecting high potential areas. Therefore a goodtarget sand should have higher than 160F temperature, minimum of 30 ft of sand and good oil saturation.Additionally, the target well should have low production and good mechanical integrity, such as nodownhole issues and preferable larger casing size. This will allow a successful sidetrack at high anglebuild sections to target the undrained reservoir.

The methodology was time consuming and burdensome with engineers and earth scientists trying tofind first the poor producing wells, and then visualy analyze the temperature, saturation and thicknessmaps sand by sand in order to decide whether or not to re-entry the wells. A total number of 10 wells wereselected and sidetracked as part of the Pilot. The results were encouraging and the Pilot provided a lot oflearnings and best practices as well as a lot of opportunities.

Data-Driven Methodology - Intelligent ApproachBy the end of 2012, a new approach using Fuzzy Logic (FL) technologies was introduced for identifi-cation and selection of horizontal well candidates, [Popa, 2013]. The process employs so called fuzzyconfidence maps generated using all critical attributes considered in the conventional approach. Addi-tionally, it eliminates the rigid hard cut-off boundaries and allows the use of entire attribute range interval,however overridden by expert developed knowledge domain rules.

In the initial work both the fuzzy sets and rules were developed in a group exercise with a large teamof engineers and earth scientists. The approach seems to be working and provided good validation whentested on the existing wells. However, the large number of horizontal wells drilled in the field (more than500) presented a great opportunity for a data driven approach. The WM method was selected and used asa rules extraction technology. The methodology applied four input attributes x1, x2, x3 and x4, one outputvariable y respectively. We will define the x1, x2, x3 and x4, as the temperature, thickness, permeabilityindex and saturation respectively while the output variable y defines the output production. To apply theWM methodology a set of historical data pairs , representing the reservoirrelative to the production output is required. This data set is compiled from all the existing horizontal wellsdrilled so far.

The first step of the methodology is to extract the membership function for the input variables. Sincethe reservoir has nine production layers, each layer with different rock properties, the workflow for onesand only is presented here. The approach is identical for the remaining eight sands.

The following process was employed to derive the membership functions. First, the Fuzzy c-Mean(FCM) algorithm was used to define three clusters for each one of the four reservoir attributes as well asfor the production output. The state-of-art full field 3D earth model was used to extract all grid blocks ineach layer. More than 10 million grid blocks were thus used in the clustering algorithm for each attribute.In contrast to the input attributes which come from the model, we used the three months peak productionfor each well as the system output. A graphical representation of the Fuzzy c-Mean algorithm results areshown in Figures 5a, 6a and 7a. Using this representation the shapes of the MFs of words such as Small,Medium and Large are not the same for the different variables. Although the MFs look like continuous

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curves, in practice, they are not. Each point on the curve represents one of the reservoir grid blocks forthe specific productive layer. The FCM algorithm assigns a membership value to each measurement foreach of the three clusters, i.e., Small, Medium and Large. In this example the graphical representation ofthe membership functions look continuous given that more than 10 million grid blocks per layer are usedin the computations.

The membership functions obtained from Fuzzy c-Mean algorithm cannot be used yet in the WMmethod since they do not accurately represent the behavior of the attribute and do not make linguisticsense. A methodology named Linguistic Modified FCM (LM-FCM) [Korjani and Mendel, 2012] wasadopted and used to derive the representation used in rule extraction. A simplification of the approach ispresented by Hao, [Hao, 2012] and is summarized below.

Given three linguistic membership functions: left-shoulder (Small), right-shoulder (Large) and interior(Medium). Each FCM cluster has to be assigned to one of the three linguistic terms. First, maximumbreakpoint is defined as the maximum of the membership functions (namely, 1) and the minimumbreakpoint as the nearest local minimum point to the maximum breakpoint. Then:

1. For a right-shoulder cluster, all membership values to the right of the maximum breakpoint are setto one and membership values to the left of the minimum breakpoint are set to zero. Membershipvalues between the breakpoints are kept as is.

2. For a left-shoulder cluster, all membership values to the left of the maximum breakpoint are setto one and membership values to the left of the minimum breakpoint are set to zero. Membershipvalues between the breakpoints are kept as is.

3. For an interior cluster there are two minimum breakpoints, one to the left and one to the right ofthe maximum membership. Membership values between the breakpoints are kept as is, and allothers are set to zero.

Using the Linguistic Modified FCM methodology described above, the final representation of themembership functions for each one of the attributes was derived. The representations for temperature,permeability index and formation thickness are shown in Figures 5b, 6b and 7b respectively. Theproduction output representation is shown in Figure 8a and 8b.

Figure 5aFCM Algorithm Temperature Figure 5bModified Membership Functions Temperature

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The second step of the methodology is to extract the data driven rules. With the membership functionsfor the reservoir attributes and production defined, Wang-Mendel method is applied as described above.An example of a rule extracted using WM method is presented below:

If (Temperature) is High AND (Saturation) is Medium AND (Permeability) is High AND (thickness)is Low THEN (Production) is Low

The third step of the methodology is to develop the fuzzy logic system with the set of rules extracted fromdata and apply it to each of the productive formations. However, prior to imbedding the sets and the rules inthe system a quick assessment was conducted on both the fuzzy sets and the rules extracted. It was observed

Figure 6aFCM Algorithm Permeability Index

Figure 7aFCM Algorithm Thickness

Figure 6bModified Membership Functions Permeability Index

Figure 7bModified Membership Functions Thickness

Figure 8aFCM Algorithm Production Figure 8bModified Membership Functions Production

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that initial rules defined with the subject matter experts (reservoir engineers and earth scientists) were moreconservative in some cases. In terms of the rules, in a few cases it was not possible to extract all the rules sincenot enough cases representing that rule were available. This was solved by generating expert rules with theengineers. Additionally, it was also observed that a few rules initially derived by the experts seemed to leanmore conservative whereby the data showed that high production could still be achieved in those cases.Modification to rules (i.e., from High to Medium and from Medium to Low) was done when warranted toconserve the experts knowledge.

The fuzzy models were used to generate confidence maps by applying the system for each grid block ofeach layer. Since most Lateral Re-Entry wells have maximum efficiency targeting no more than three sands ata time the need for a composite map was noted. Thus the fuzzy confidence maps for each of the bottom threetarget sands were aggregated into one confidence map. In this new format this map shows the areas across thefields where all three sands would contribute in somewhat equal capabilities to the Re-entry well output. Thismap was very revealing, as it defined the sweet spots around the field driven mainly by the main reservoirattributes provided by the 3D earth model. Figure 9 shows an example of the aggregated fuzzy confidence mapfor the deepest three sands. The map clearly outlines high potential areas across the reservoir (red and orange)for Lateral Re-Entry target. Additionally, a high contrast color (red) reveals high potential in all three sandswhereby a lighter color (orange/yellow) might very well have a strong sand and the other twomedium to highpotential. It should be noted that before using the confidence map the area identify by the black rectangle onthe map was not a primary Lateral Re-Entry target, mainly due to relatively low offset production. Wellscompleted in that area realized significant production output.

Figure 9Aggregated Confidence Index Map

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The fourth step of the methodology was to generate the Voronoi diagram for all producing and shut-inwells in the field. With almost no pressure existing in the reservoir the recovery mechanism is gravitydrainage. Therefore, the polygons generated and assigned to active and shut-in producers using Voronoialgorithm are a good approximation of the wells drainage area. The remaining oil reserves in eachdraining cell as well as normalized remaining oil were calculated using information from the full field 3Dearth model and polygons areas. Voronoi diagrams were computed for all nine layers as well as totalaggregated sum. The aggregated diagram represents the remaining oil reserve for each well. However, ofmost interest are the remaining reserves in the two or three sands the Lateral Re-entry would intersect, thusthese aggregated maps are generated as well. Moreover, a Lateral Re-entry could intercept two or threeVoronoi cells; therefore the sum of the polygons needs to be considered. These computations are part ofthe candidate selection criteria since the search for the best candidates should ensure sufficient reservesavailabe to drain.

Field Application and ResultsBuilding upon the successful field Pilot in 2012, the new methodology was used for candidate wellidentification and selection. The screening process starts with identification of poor performance wells,generally lower than 3 bopd. Once a potential well was identified the aggregate fuzzy and Voronoiremaining reserves maps are analyzed. The process is illustrated in Figures 10 and 11 respectively. Theblack dot indicated on both maps represents the original vertical completion while the sloped black linerepresents the new completion after Later Re-entry section was drilled. The aggregated confidence mapfor three deeper layers in the reservoir is shown in Figure 10. The highest red contrast grid blocksrepresent the higher reservoir capabilities. Similarly, the aggregated Voronoi polygons map for the samethree layers is shown in Figure 11. The highest red contrast grid cells represent the larger remainingreserves. The integration of the two approaches showed the Lateral Re-drilled well (black line on both)being placed such as intersects as many grid blocks exhibiting the higher confidence and also beingconfined within high reserves Voronoi polygons.

The production profile realized by this well is presented in Figure 12. The plot shows that initial wellproduction was about 25 bopd in 2002, and declined to below 3 bopd by 2006. Between 2008 and 2013

Figure 10Aggregated fuzzy confidence map Figure 11Aggregated Voronoi map

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the well was barely making any oil (less than 1 bopd) until was selected for Lateral Re-entry. Theproduction realized after Lateral Re-entry is significant, the well reaching a peak of 51 bopd and stillmaintaining above 20 bopd after one year.

It should be noted that, once a well is identified additional engineering is required to be included in thefinal drilling program. For example the individual fuzzy confidence maps at layer level are used forsensitivity analysis and also to as a tool for placing the toe of the Lateral Re-entry section. The goal is totarget the longest high angle section in the sand with the greatest potential. Additionaly, the final welltrajectory is validated in the full field 3D earth model and is vetted with the drilling and completion groupfor final validation.

The new workflow has been used by the asset development team since early 2013 as part of the mainLateral Re-entry program. Since its deployment multiple development packages were executed. Theproduction response of the initial Pilot as well as the main program is presented in Figure 13, 14, 15 and16 respectively. In contrast to the original Pilot which resulted in an average 9 bopd per well (Figure 13)the main program using the intelligent approach realized an average of 18-25 bopd per well. A fewexamples of development packages executed throughout 2013 and 2014 are shown in Figures 14, 15 and16. This program delivered significant business impact and demonstrated the efficiency of a data drivenanalytics approach.

Figure 12Lateral Re-entry well Production Profile example

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The 2014 program is using updated fuzzy maps accounting for the changes in temperature, saturationand liquid thickness. The future looks bright with a significant queue of opportunities awaiting executionover the next couple of years. Best practices developed during execution have led to additional costreduction which makes these Lateral Re-entries even more attractive.

ConclusionsThe strengths and capabilities of data-driven analytics were introduced and demonstrated in this study.The intelligent approach integrates technologies such as WM rules extraction, fuzzy logic modeling andVoronoi diagram for optimization of Lateral Re-entry identification, selection and placement. The modelsuccessfully learns from high-dimensional data, effectively identifies prime productive areas and guidesoptimum directional paths as demostrated in the realized production increase.

We applied the model to a large field with thousands of wells and multiple production layers. Weproved that aggregated confidence maps identified new target areas in the reservoir which were previouslyor not even considered. Furthermore, we demonstrated that the intelligent model significantly outperformsthe prior approaches by returning a two fold production increase (18 25 bopd well average) in 2013 and2014, as compared to 9 bopd average from prior year.

The inclusion of new artificial intelligence technologies such as fuzzy logic in day to day engineeringworkflows not only demonstrates the commitment of leadership to encourage ingenuity but also thebusiness impact and value creation achieved. The Lateral Re-entry project clearly validates that the

Figure 13Pilot Program 2012 Figure 14Package 1 2014 Intelligent Workflow

Figure 15Package 2 2012 Intelligent Workflow Figure 16Package 1 2013 Intelligent Workflow

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intelligent approach is more realistic, flexible and focused on the high potential areas, maximizingproduction outcome of the field.

AcknowledgementsThe author would like to thank Chevron North America Exploration and Production for allow thepublication of this work. Additional gratitude is expressed to Eli Grijalva, Carlos Matheus, Juan Medel,Rob Jaecks, and Brian Scott for their assistance and input.

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Swartz, J., Knauer, L., Eacmen, J., Hunter, A., and McNaboe, J., 2008, Kern River Field: Frameworkand Future of an Old Giant: AAPG Search and Discovery Article #90076, AAPG Pacific Section,Bakersfield, California, 2008.

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Optimizing the Selection of Lateral Re-Entry Wells through Data-Driven AnalyticsIntroductionObservation and Project OpportunityTechnology BackgroundWang-Mendel (WM) MethodFuzzy Logic SystemsVoronoi Diagrams

History of the Lateral Re-Reentry WellsCandidates Selection processs Conventional Approach

Data-Driven Methodology - Intelligent ApproachField Application and ResultsConclusions

AcknowledgementsReferences