This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in San Antoni, Texas, USA, October 2017.

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1 SPE MS Mapping the Natural Fracture Network in Marcellus Shale using Artificial Intelligence Mohaghegh, S. D.; Intelligent Solutions, Inc. & West Virginia University Copyright 2017, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in San Antoni, Texas, USA, October 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 have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper 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 SPE copyright. Abstract Contribution of Natural Fracture Network (NFN) to the productivity of shale wells is a well-established fact. So much so that it is widely believed throughout the industry that all other things being equal, the presence and the extent of Natural Fracture Network in shale determines the productivity of a shale well. While deterministic mapping of the NFN in shale through direct measurements is not an option, stochastic techniques are used to generate NFN to be used in numerical simulation. Among tools used to provide some indication of NFN in shale are wellbore image logs and seismic surveys. However, limitations of these tools for this purpose is well documented. In this paper, we present an alternative technique that makes use of a large number of field measurements and incorporates artificial intelligence in order to generate NFN map in shale. In this technique Artificial intelligence, infers the presence and the extent of the NFN from well productivity. Parameters that impact the productivity of shale wells are: well construction & trajectory, reservoir characteristics, completion, stimulation/hydraulic fracturing, and operational conditions (wellhead pressure, choke size). Natural Fracture Network falls under the category of reservoir characteristics. In this approach Artificial Intelligence uses 12 measured parameters representing the volume of fluid available for production and operational conditions as well as completion and hydraulic fracturing practices to infer the presence and the extent of NFN. These field measurements are analyzed on a well by well basis to generate a dimensionless score for the presence and the intensity of the NFN that are then mapped throughout the reservoir using geo-statistics. Through two case studies, we demonstrate that the discrepancies in correlation between reservoir, completion and frac job parameters and the well productivity can be explained through the presence and the extent of the Natural Fracture Network. The results of this process is (a) a distribution map of the presence and the extent of the Natural Fracture Network throughout the reservoir and (b) a large number of examples with detail comparisons between wells that justify the generated map.

2 2 SPE MS Introduction It is a well-known fact that the Natural Fracture Network (NFN) is a significant contributor to well productivity in shale wells. It is widely accepted in the industry that the most significant contribution of the induced hydraulic fractures is the opening of (activating) the existing network of natural fractures before (or along with) creating new fractures in shale. Since it is impossible to map the Natural Fracture Network in shale (or any other formation, for that matter) through direct measurements, engineers and scientist have used seismic surveys and stochastic modeling as inspiration to generate Natural Fracture Network. In numerical reservoir simulation, the Natural Fracture Network is modeled using a stochastic approach that is referred to as Discrete Fracture Network (DFN). Using the information that sometimes may be available from wellbore image logs (which happens rarely), or using information from outcrops, important parameters that are needed to randomly generate Natural Fracture Network models are assumed such as fracture density, size, and orientation, etc. Core analysis and seismic survey have also been used to define natural fracture distribution in formations and to help model the geometry of their network in shale [1], [2], [3], [4], [5]. While the mapping of the Natural Fracture Network through direct measurements is not an option, the contribution of the NFN to the productivity of the shale wells is obvious. Any advancement that can contribute to a better understanding of the NFN, its presence, and its density is of immense interest to the oil and gas community that is producing hydrocarbon from shale. It is widely believed throughout the industry that all other things being equal, the presence and the extent of the Natural Fracture Network determines the productivity of a shale well. Shale wells productivity that is often represented by the production (volume) profile and/or cumulative production is directly impacted by the following parameters: Well Construction & Trajectory; represented by well geometry, percent in-formation, inclination, azimuth, up-dip/down-dip, fault intersection, etc. Reservoir Characteristics; represented by well location (latitude and longitude), well logs, porosity, thickness, water saturation, TOC, geo-mechanical properties, etc., and of course by the presence and the density of the Natural Fracture Network (NFN) Completion; represented by lateral length, number of stages, stage length, distance between stages, number of clusters per stage, distance between clusters, shot density, etc. Stimulation/Hydraulic Fracturing; represented by: type and amount of fluid, type and amount of proppant, proppant size, and concentration, injection rate and injection pressure, etc. Operational Constraints; represented by choke size, well-head (casing and tubing) pressure, etc. The approach that is introduced here uses Artificial Intelligence (AI) to assist engineers and scientists in creating a map of the Natural Fracture Network for Marcellus shale by making the most use of the field measurements that have been numerated above. Motivation After analyzing more than 3000 wells in many shale plays in the United States, we continuously encountered discrepancies in well productivities that were unexplainable by any (or the collection) of the field measurements. Every time we approached the reservoir engineers, completion engineers, and geoscientists from the operating companies or the service companies that were directly involved in the process, we heard all sorts of reasoning, none of which were supported by numbers, field measurements, and/or facts.

3 SPE MS 3 While some of the responses for explanations included reasonable logic, others were simply: Not sure. Issues such as lack of good cementing behind the casing, presence of faults, and the presence (or the absence of) Natural Fracture Network were among the reasons that would come up quite often in such discussions. These inconsistencies of well productivity that seemed to be quite prevalent throughout the United States in almost every shale play, was the main motivation behind the efforts to see if there are information in the collected data (field measurements) that can add some consistency to the explanation of why production from shale wells differs so much, even when all the parameters that are involved (and that can be measured) are so similar. The work presented in this paper demonstrate these efforts and the results achieved in Marcellus shale in the United States. Assumptions Like every other technique that includes assumptions, this AI-based technique includes one assumption. As long as the assumption that is articulated below is accepted, a reasonable map of the Natural Fracture Network can be generated using a large number of actual field measurements. The assumption for this technology that is demonstrated in Figure 1, simply states that wells with similar construction and geometry, similar reservoir characteristics, similar completions, similar hydraulic fractures, and similar operational constraints, are expected to display similar productivity. This is the main and the only assumption that is made in this study. Figure 1. Wells "A" and "B" have similar attributes, therefore, their productivity is expected to be similar. The horizontal bar charts represent similarity in magnitude Figure 2. Higher productivity of Well "B" can be attributed to Reservoir Characteristics since all other characteristics are similar. The horizontal bar charts represent similarity in magnitude. However, for clarity purposes, let us repeat the same assumption, but under different circumstances to make our point. For example the following statements is actually the repeat of the above assumption, but expressed in a slightly different manner. If wells with similar construction and geometry, similar

4 4 SPE MS completions, similar hydraulic fractures, and similar operational constraints, exhibit different productivity, then it must be the reservoir characteristics that is responsible for the differences in the well productivity. This is shown in Figure 2. Reservoir characteristics can be represented by multiple measured parameters. Most of these parameters are calculated through well logs that represent the most common measurements that are available from shale wells. It must be noted, that the technique that is being presented here is not dependent on any particular parameter or measurement and can use any available reservoir characteristics. During the development of this technique measured parameters that represent reservoir characteristics in several shale plays were used. These parameters are shown in Figure 3. In this figure the reservoir characteristic that is not measured is the Natural Fracture Network. Therefore, to articulate the assumption mentioned in the last paragraph more clearly, one can say, If directly measured reservoir characteristics such as well logs, TOC, thickness, etc. do not present the required distinction to justify the differences in well productivity, then the Natural Fracture Network, remains as the only possible explanation for the difference in well productivity. This is shown in Figure 3. Figure 3. Measured parameters that represent reservoir characteristics in a shale wells. Natural Fracture Network is not a measured parameter. The assumption that has been mentioned here can be even more pronounced with the example shown in Figure 4. In this example job size (a hydraulic fracturing attribute) is smaller for well A and therefore increases the expectation that this well (well A ) should be less productive than well C. However, in reality, the observed productivity of the wells are different. In light of what was shown in Figure 3, in this

5 SPE MS 5 study we may assume that the Natural Fracture Network can explain the discrepancy in the well productivity shown in Figure 4. One more item needs to be mentioned here. Although we have tried to be comprehensive in our identification of parameters that impact well productivity, we may still have overlooked some possible parameters. If this is indeed the case, then what we are calling Natural Fracture Network in this paper, can be extended to include the impact or impacts of the potentially missed parameters. Figure 4. The attributes of Wells "A" and "C" are such that Well "C" is expected to have higher productivity, however, Well "A" is more productive than Well "C". Reservoir Characteristics The idea is that characteristics of the reservoir rock (Marcellus shale in this case) determines the degree at which the combination of the completion characteristics and stimulation implementation would result in a Marcellus shale well productivity. Furthermore, the characteristics of the reservoir rock (shale) includes both the parameters that can be, and are measured, such as well logs, porosity, initial water saturation, formation thickness, TOC, etc. and another important characteristics that cannot be directly measured, such as the presence and the extent of the Natural Fracture Network (Figure 3 and Figure 5). Figure 5. Parameters that determine the characteristics of the shale as a reservoir rock. Cross plots shown in Figure 6 through Figure 8 clearly show that there seem not to be any visible trends or patterns between measured parameters that represent reservoir characteristics and 30 days cumulative gas production (MMscf) in this particular asset in the Marcellus shale. These plots clearly show that the quality of the reservoir (shale) cannot be distinguished using these measured characteristics. Nevertheless, if all parameters (well construction and trajectory, completion characteristics, Hydraulic Fracturing practices, and operational constraints) for two wells seem to be similar, yet these wells display different productivity, and the distinction cannot be identified with all measured reservoir characteristics (Porosity, Gas Saturation, Formation Thickness, TOC, and Initial Gas In Place), then the only parameter left that still can impact well productivity, would be what we have chosen to call in this article the Natural Fracture Network or NFN.

6 6 SPE MS Figure 6. Cross plot of 30 days cumulative gas production versus Net Thickness and TOC, in a Marcellus shale asset. Figure 7. Cross plot of 30 days cumulative gas production versus Gas Saturation and Porosity, in a Marcellus shale asset Figure 8. Cross plot of 30 days cumulative gas production versus Initial Gas in Place (IGIP), in a Marcellus shale asset.

7 SPE MS 7 Well Productivity Comparison To clarify the point made in the previous section, an example is presented in Figure 9 (several more examples such as this one is presented in the later sections of this article). This figure shows the comparison between two shale wells in a Marcellus shale asset. They are Well#B282 and Well#B383. These two wells are not far from one another. The primary fluid for both wells is Gas and they both are producing fluid within a similar Condensate to Gas Ratio (CGR) window. These wells have similar porosity (7% for B282 and 7% for B383) and initial gas saturations (67% for B282 and 66% for B383). Other parameters such as initial pressure, formation thickness, IGIP, Breakdown Pressure, ISIP, and TOC are also reasonably close as shown in Figure 9. Furthermore, the stage length and spacing for the two wells are 337 and 206 ft. for Well #B282 and 336 and 197 ft. for Well #B383. Figure 9. Comparing two wells when the well productivity does not match the reservoir characteristics, completion, and stimulation characteristics. The completed lateral length for both wells are quite similar (3,653 ft. for B282 and 3,659 ft. for B383). These wells have been stimulated with hydraulic frac jobs that are slightly different. Well#B282 has a smaller fluid volume while having a smaller amount of proppant injected. While the pas volume is quite similar, and both have no fine proppant the average treatment pressure of B282 is about 500 psi higher and the average treatment rate also higher by 12 (bpm). What is most interesting is that while almost everything about these two wells is similar, their productivity is significantly different from one another. Well#B282 presents a much higher 30 days productivity (156.4 Gas Volume/psi) than Well#B383 (27.4 Gas Volume/psi). We propose to attribute the discrepancy between well productivities to the presence and the extent of the Natural Fracture Network in this shale formation. One may use the term Effective Natural Fracture Network in order to account for other parameters that may be contributing to this discrepancy, and are not accounted for in the measurements shown here. If this was an anecdotal event between two wells, then one should not have made such conclusions and dig deeper in finding other reasons that might explain this inconsistency in well productivity. However, after looking at more than 3000 shale wells in several different shale plays in the United States (Marcellus,

8 8 SPE MS Marcellus, Niobrara, Eagle Ford, Bakken, etc.), we have observed again and again (more examples will be presented in this article) that such discrepancies in well productivity are hard to explain using facts and field measurements. Proposed Solution The proposed solution is to use field measurements in order to generate a map of Effective Natural Fracture Network that could help explain the observed discrepancies in well productivity. In this analysis, it make sense to only use the parameters that are available for all wells. For example, while we are all but certain that geo-mechanical rock properties play a role in the propagation of the induced fracture, if such parameters are not available for all wells in our analysis, it does not make sense to include them in the analysis. It must be mentioned that this technology is so flexible that any parameter that is available can be somehow incorporated in the analysis. Figure 10. List of twelve parameters used in the generation of the Natural Fracture Network map for this particular asset in Marcellus Shale. Figure 10 shows the list of twelve measured parameters that were used in the example that is being presented in this paper. The parameters are divided into four categories: The first category is Well Productivity. Well-head pressure, initial reservoir pressure (may be presented by TVD as a proxy) and 30 days cumulative gas production (MMscf) are used to generate a pressure corrected production indicator. The second category is Stimulation. In the example shown above, amount of proppant (lbs. /stage) and amount of fluid (bbls/stage) are used to represent the size of the stimulation (Hydraulic Fracturing). Other parameters can also be included in this category to better represent the quality of the hydraulic fracturing job. The third category is being called Volume/Completion. The overall purpose of this category is to represent the quality and the size of the reservoir that is being produced from along with the completion

9 SPE MS 9 practices incorporated. For this category, well spacing along with the lateral length and formation thickness are used to calculate bulk volume and porosity and initial gas saturation are used to calculate Hydrocarbon Pore Volume (HCPV). Other parameters included in this category are initial pressure, TOC and total number of Stages. The fourth and the final category is Natural Fracture Network. This category is defined as the presence and the extent of the natural fracture network in the reservoir volume that has been defined in Category #3, above. One may conclude that any and all other parameters that may be contributing to the well productivity may not be included in the parameters that have been taken into account (for this example those shown in Figure 10) are included in this category. Fuzzy Cluster Analysis Once the parameters are divided into categories as explained in the previous section, each category is divided into classes/clusters. The clusters used in this analysis are fuzzy in nature. In other words, they do not have crisp boundaries and there are overlaps between clusters. Each category is divided into three fuzzy clusters. As mentioned above, in this analyses well productivity is represented by three parameters namely, 30 days of cumulative MMscf, initial pressure, and well-head pressure. Well productivity is calculated based on these three parameters as an estimate of the 30 days of cumulative gas production (MMscf) per psi. The well productivity is then divided into three fuzzy clusters of wells with poor, average and good productivity. Fuzzy clustering of the well productivity is shown in Figure 11. Please note that the range and values that distinguishes between fuzzy clusters are project dependent. Figure 11. Well productivity is divided into three fuzzy clusters of poor, average, and good wells The next category is Stimulation. In this example Stimulation is represented by two parameters, namely, amount of fluid in barrels per foot of lateral length and amount of proppant in pounds per foot of lateral length. A supervised fuzzy cluster analysis is used to divide Stimulation into three overlapping clusters of small, average, and large frac jobs. In this approach the cluster centers for small, average, and large frac job are determined and imposed on the algorithm (i.e. supervised). Location of the cluster centers are shown in cross plot in Figure 12, and the values for ach cluster center is shown in Figure 13. Each point in Figure 12 represents the size of the frac job for each well. Euclidian distance of each point (well) to each cluster center determines the membership of the stimulation for that

10 10 SPE MS well in each of the three clusters. For example, well #324 has much larger membership in the cluster of average frac jobs (due to its smaller Euclidian distance to the average frac job cluster center) than it has in large and small frac jobs. Well #096 has almost similar memberships in the clusters of small and average frac jobs and somewhat smaller membership in the cluster of large frac jobs. Figure 12. Location of the small, average, and large frac jobs shown on a cross plot of proppant vs. fluid per ft. The next category is Volume/Completion. In this example Volume/Completion is represented by four parameters, namely, Completed lateral length, TOC, Initial pressure, and Number of stages. Using supervised fuzzy cluster analysis the volume/completion is divided into three overlapping clusters of small, medium, and large volume/completions. This is similar to the approach used for clustering the stimulation. The main difference is that in clustering the stimulation only two parameters were used while for clustering the volume/completion eight parameters are involved five of which have been integrated to form hydrocarbon pore volume. The cluster centers for small, medium, and large volume/completions are determined using a process which includes cross plotting all the involved parameters against one another and identifying the location of each cluster center. This process is shown in Figure 14 through Figure 16.

11 SPE MS 11 Figure 13. Values of cluster centers small, average, and large frac jobs shown for fluid and proppant per ft. Figure 14. Location of the small, medium, and large volume/completions shown on two cross plots. Figure 15. Location of the small, medium, and large volume/completions shown on two cross plots. Once the process of determining the location of cluster centers is completed, values for each cluster center is plotted in bar chart to demonstrate that the multi-dimension attributed to each cluster center actually makes sense. Figure 17 shows the values of hydrocarbon pore volume, initial pressure, TOC and number of stages for each of the small, medium, and large volume/completion cluster centers. Now, available hydrocarbon volume, initial pressure, TOC, and number of stages for each well is measured against these cluster centers and each well is tagged with a series of membership functions determining the degree of their membership in each of these clusters (closeness to the cluster center), in terms of available

12 12 SPE MS hydrocarbon volume and how they have been completed, to each of the cluster centers that have been defined. Figure 16. Location of the small, medium, and large volume/completions shown on two cross plots. Figure 17. Values of cluster centers small, medium, and large volume/completions shown for hydrocarbon pore volume, initial pressure, TOC and number of stages. Logical Inferences Natural Fracture Network (NFN) is divided into three overlapping categories (clusters) of Minimal, Average and Extensive. However, unlike other three categories that we had actual field data from hundreds of wells to help us determine the location of each cluster center, in the case of NFN we do not have such a luxury. Here, we classify the presence and the extent of the NFN into three clusters and then we qualitatively correlate the classes of the NFN to classes of well productivity when all other parameters are

13 SPE MS 13 similar in magnitude. This is where the main assumption that was covered in the previous section is incorporated in the analysis. For example, we state that all other parameters being equal, higher well productivity is expected in the presence of extensive NFN, or lower well productivity is expected in the presence of minimal NFN. If we assume a qualitatively linear relationship between presence and the extent of the Natural Fracture Network and well productivity (all other parameters being similar), then the qualitative plot shown in Figure 18 can be inferred. Following items are included in this figure: o Assuming all other parameters (hydrocarbon volume available to the well, how the well is completed, and hydraulically fractured) being similar; o There is a qualitatively linear relationship between the presence and the extent of the Natural Fracture Network and well productivity, When the presence and the extent of the NFN is minimal, the expected well productivity is poor, When the presence and the extent of the NFN is average, the expected well productivity is also average, and When the presence and the extent of the NFN is extensive, the expected well productivity is good. Each of the above statements regarding the relationship between well productivity and NFN is further qualified (granulized) with a truth value. Since the relationships that we have established are qualitative in nature, and the term similar is used for hydrocarbon volume, completion, and hydraulic fracturing, the truth value can further qualify the statements and the rules that are made during the next step. The truth value includes Very True, that has a higher truth value than True, and it has a higher truth value than Fairly True. Fuzzy Rules Once the variables involved are identified, the fuzzy clusters of each variable is determined and the NFN and well productivity relationship model is decided, then it is time to build the set of rules that would govern the interaction between all the involved parameters. Since in this version of NFN mapping Fuzzy Set Theory [6] is being used as the technology behind this development, the rules that will be introduced, will be fuzzy rules. Fuzzy rules have two distinct characteristics that serves the AI-based development being introduced in this paper. These distinct characteristics are: Fuzzy rules incorporates natural language semantics in order to build a set of instructions on how the involved variables interact. This characteristic of fuzzy set theory removes restrictions that are usually associated with crisp classification of variables. When fuzzy rules are used in fuzzy inference engines, to make conclusions based on these rules, several of them are fired simultaneously and in parallel. The final decision is then made based on the conflict resolution or aggregation of similarity between multiple rules. This allows simultaneous investigation of a vast number of possible scenarios to be considered at the same time and the final inference to be made in an intelligent fashion.

14 14 SPE MS Figure 18. High level modeling of presence and extent of NFN and well productivity using a linear model. Figure 19. Different models for presence and extent of NFN and well productivity. The model can be Linear, Logarithmic (early impact), or Exponential (late impact).

15 Production (BOE/psi) Poor Average Good SPE MS 15 Relationship between the presence and the extent of the NFN and well productivity can be either linear or nonlinear. The nonlinear relationship maybe modeled in many different ways. Two examples of such nonlinearities are logarithmic, or exponential as shown in Figure 19. Small Medium Large Stimulation Large Average Small Minimal Minimal Average 3 TRUE 2 Fairly True 1 Fairly True Minimal Minimal Minimal 6 TRUE 5 TRUE 4 Fairly True Minimal Minimal Minimal 9 Very True 8 TRUE 7 TRUE Volume/Completion Small Medium Large Small Medium Large 12 Very True 11 Fairly True 10 Fairly True 15 Fairly True 14 Very True 13 Fairly True Minimal Stimulation Large Average Small Average Average Extensive Average Average Average Average 18 Fairly True 17 Fairly True 16 Very True Stimulation Average Large Average Small Extensive Extensive Extensive 21 Fairly True 20 TRUE 19 Very True Extensive Extensive Extensive 24 Fairly True 23 Fairly True 22 TRUE Average Extensive Extensive 27 Fairly True 26 Fairly True 25 Fairly True Figure 20. The fuzzy rules that govern the distribution of Natural Fracture Network in shale formations (the linear model). Figure 20 displays a collection of 27 rules that are used for mapping of Natural Fracture Network in the example that is presented in this paper. Each of the rules in this figure is qualified with a specific Truth Value that distinguishes similar rules from one another. Let us demonstrate how the rules shown in Figure 20 are expressed in natural language. For example Rule #19 states: When a Small Volume/Completion is available to a shale well, and the well is Stimulated with a Small frac job, and the well demonstrates Good Productivity, then the Natural Fracture Network around this well must be Extensive.

16 16 SPE MS Furthermore, compare to other rules that conclude the Natural Fracture Network around a well is Extensive, this rule is Very True. For the purposes of being complete, let us provide another example as well. Rule #18 states: When a Large Volume/Completion is available to a shale well, and the well is Stimulated with a Large frac job, and the well demonstrates Average Productivity, then the Natural Fracture Network around this well must be Minimal. Furthermore, compare to other rules that conclude the Natural Fracture Network around a well is Minimal, this rule is Fairly True. It should also be mentioned that as little as 9 rules and as many as 18 rules are fired simultaneously (in parallel) and then resolved using a fuzzy inference engine in order to generate a conclusion about a single well, in this operation. The parallel execution of a large number of rules helps the robustness of the results generated by this technology. Generating NFN Distribution Map Once the fuzzy sets for the Volume/Completion, Stimulation, Well Productivity, and Natural Fracture Network have been established and the fuzzy rules to combine these fuzzy sets have been defined, the combination of the fuzzy rules are execute in parallel for every well through incorporation of a fuzzy inference engine in order to generate a value (number) for the Natural Fracture Network Density. The process of generating the NFN distribution map for a shale asset follows the following four steps: For each given well (its location in the field), the fuzzy membership values for all three variables (Volume/Completion, Stimulation, Productivity) are identified, The collection of rules needed to engage all the combinations of the fuzzy membership values of all variables are selected from the list of rules shown in Figure 20, The fuzzy membership values, and the corresponding fuzzy rules are used in a fuzzy inference engine in order to generate the set of fuzzy membership values for the NFN, Using the Center of Mass technique the fuzzy membership values of the NFN are defuzzified. This determines the NFN Density of the given well location. Figure 21. Fuzzy membership values for the volume/completion, stimulation, and productivity variable at Well #23 1. Let us demonstrate this process through a simplified example. This example does not apply to any of the wells in the asset in Marcellus shale that is the subject of this paper and has been made up in order to demonstrate the details of this methodology. This technology is implemented comprehensively using a 1 In this example, for the sake of simplicity, we are assuming that each variable is represented by one or two fuzzy sets, and thus it is represented by one or two fuzzy membership value.

17 SPE MS 17 Shale Analytics software application 2. Using the Well #23 in a shale asset, we identify that the fuzzy membership values for all three variables (Volume/Completion, Stimulation, and Productivity) for this well. As shown in Figure 21 the Volume/Completion variable includes two fuzzy membership values (medium and large), the Stimulation variable includes one fuzzy membership value (small), and the Productivity variable includes two fuzzy membership values (average and good). Figure 22. Set of four rules representing all the fuzzy membership values for the three variables. The set of four fuzzy rules shown in Figure 22 are extracted from the table of rules (Figure 20) and cover all the fuzzy membership values for the three variables shown in Figure 21. Once the fuzzy membership values and the corresponding fuzzy rules are identified, a fuzzy inference engine is used in order to generate the resulting fuzzy membership values for the NFN. A graphical representation of such a fuzzy inference engine that combines the fuzzy membership value of each of the variables using a given fuzzy rule, and fires them in parallel is shown in Figure 23. Figure 23. Example of the fuzzy inference engine used to generate NFN fuzzy membership values from the parallel execution of the fuzzy 2 The Shale Analytics software application used to perform tee analyses that are presented in this paper is called IMprove by Intelligent Solutions, Inc. (

18 18 SPE MS rules. Figure 24. Using the Center of Mass technique the fuzzy membership values of the NFN defuzzified. This is the NFN Density of the given well location. The results of firing all the applicable rules in parallel generates a set of fuzzy membership values for the NFN. These fuzzy membership values of the NFN are aggregated to represent the fuzzy nature of the NFN for the location of the well being analyzed, in this case Well #23. Since a crisp number is needed to be used to map the distribution of the NFN throughout the field, the fuzzy membership values of the NFN are defuzzified using the center of the mass defuzzification technique. This process if demonstrated in Figure 24. Once a NFN Density value is generated for each well location in the field, these numbers are used as anchor points for a geo-statistical routine to generate the NFN distribution map for the entire field. Results and Conclusions Using Artificial Intelligence, a data-driven solution for the distribution of the Natural Fracture Network in a shale asset was mapped in Marcellus Shale. Similar analyses has been performed and have resulted in the generation of maps of the NFN in Marcellus, Bakken, Niobrara, and Eagle Ford shales using data from thousands of wells. Figure 25 shows the Natural Fracture Network distribution generated for a specific location in the Marcellus shale. The results that are generated based on the 27 rules displayed in Figure 20 are combined with geo-statistics in order to generate the NFN distribution map shown in Figure 25. The outcome of the Artificial Intelligence system for the distribution of the Natural Fracture Network are scaled using an arbitrary range for the demonstration purposes. The range used for this article has a minimum value of 10 to a maximum value of 100 (dimensionless score only indicating intensity of the NFN) in order to show the relative distribution of the density of the Natural Fracture Network in this asset. The heterogeneous nature of the distribution of the Natural Fracture Network that is shown in Figure 25 demonstrate the complexities associated with the completion, stimulation and production operations of shale wells. Once the distribution of the NFN is mapped, the results of this exercise can be put to engineering and geosciences evaluation for future decision making. Also this map can be used as an indication of the sweet spots in the asset. To demonstrate the accuracy of the results generated by this Artificial Intelligence technology, we present several examples. First, let us revisit the example that was presented earlier in this document (Figure 9) in order to justify the necessity of performing such analysis. In that example we presented two wells with

19 SPE MS 19 similar volume/completion and similar lateral length. These wells were stimulated with similar frac jobs. However, the productivity for these two wells were very different (well #B282 is producing about 6 times as much as well #B383). Figure 26 shows that using the technology presented in this document, this discrepancy can be explained by attributing the difference in the well productivity to the presence and the extent of the Natural Fracture Network throughout this asset. Figure 25. Map of the Natural Fracture Network for an asset in Eagle Ford, generated using the AI-Based process presented in this document. This figure (Figure 26) shows that the well with better productivity is located in a part of the field with higher NFN density (NFN Density score of 93) while the well with lower productivity, although it has been completed similarly and has been stimulated with a similar frac job is located in the part of the filed that has lower NFN density (NFN Density score of 38). In other words, when field measurements that are related to how the well is drilled, how much hydrocarbon volume is accessible to it, how it was completed, how big of a frac job was performed and what choke sizes were used to produce it, cannot explain the well productivity (or actually provide opposite result as we expect), then the only characteristics that is left for the well productivity to be attributed to, is the presence and the extent of the Natural Fracture Network. The map shown in Figure 25 is a compilation of this line of reasoning that is applied to every square foot of this asset using all the facts (field measurements) that can be accessed.

20 20 SPE MS Figure 26. Impact of Natural Fracture Network on productivity of wells with similar volume/completion characteristics but different frac job sizes. Figure 27. Distribution of the Natural Fracture Network can explain the discrepancies associate with well productivity in wells when volume/completion characteristics, lateral length, frac job sizes, and operational conditions cannot justify the displayed well productivity. Two pairs of wells are shown.

21 SPE MS 21 Figure 28.Distribution of the Natural Fracture Network can explain the discrepancies associate with well productivity in wells when volume/completion characteristics, lateral length, frac job sizes, and operational conditions cannot justify the displayed well pro The asset in the Marcellus shale that was the subject of this study includes a large number of wells that can be used to demonstrate the consistency by which this artificially intelligent system has made the conclusions that are mapped in Figure 25. The AI system is capable to learn from and then simultaneously process the large amount of historical data from this asset in order to generate the comprehensive and cohesive map of the presence and the extent of the Natural Fracture Network in this asset. To further demonstrate the consistency of this technology Figure 27 and Figure 28 demonstrate two more pairs of examples on how the discrepancies regarding the well productivity when compared with all other parameters can be explained using this technology. Extension to the Rest of the Field (Play) As it can be noted, since this technology uses well, reservoir characteristics, completion parameters, stimulation practices, and well productivity in order to infer Natural Fracture Network density, it is only applicable to the parts of a shale play that has already been developed (drilling and production is already underway). There will be immense interest in the industry to be able to map the Natural Fracture Network in parts of the fields that is yet to be developed. The technology presented in this paper can be used to accomplish this task as long as there exist a seismic survey for the area that is of interest to the operator. This section of the paper is a brief demonstration on how this technology can be used in order to extend the learning from the developed portion of the asset to the rest (yet to be developed) part of the play. Also, it must be noted, since uncertainties are associated with the reservoir characteristics of the yet to be developed portions of the shale play, the process presented in this paper can be performed in a fashion in order to quantify the uncertainties that are associated with such characteristics. Once the Natural Fracture Network is mapped throughout the part of the field where some wells have been completed and are under production (as described in this document), the map can be extended to the rest of the shale play where there may not be any wells drilled yet, as long as there are seismic surveys are available for the play. In order to accomplish this task following steps need to be followed: Using the developed portion of the field, perform the NFN mapping that was presented in this paper and

22 22 SPE MS generate NFN Density for the developed portion of the filed. Impose a reasonably fine Cartesian grid on the NFN distribution map generated in the previous step. Extend the grid to the entire play, including the yet to be developed portions of the shale play. Use the seismic survey for the entire shale play and select the seismic attributes (as many as one can generate, no limitations) for the entire shale play including the developed and the yet to be developed portions of the play, Using the seismic attributes for the developed portion of the field and the NFN Density map for the same portion of the field, design, train, calibrate, and validate a robust set of neural networks that together can correlate the seismic attributes to the NFN Density (one of such networks is shown in Figure 29), Deploy the trained neural network in predictive (forecast) mode. Use the seismic attributes (those that were eventually used to train the set of neural networks in the previous step) for each Cartesian grid of the yet to be developed portion of the field and feed it to the trained set of neural networks. The output of the neural network will be the NFN Density for the given Cartesian grid. Repeat the above step for every Cartesian grid in the yet to be developed portion of the field and generate a map of NFN Density for the entire play including the yet to be developed potion of the shale play. Figure 29 shows the schematic diagram of one of the multiple neural networks that collectively correlate the collection of the seismic attributes to the natural fracture network Density. Once the training, calibration, and validation of the neural networks are completed, the model is capable of generating a reliable value for the NFN Density as a function of the seismic attributes. Technologies presented in this paper are patent pending. Figure 29. Data driven model that correlates the seismic attributes to the Natural Fracture Network.

23 SPE MS 23 References [1] Gale, J.F.W., Reed, R.M., and Holder J., Natural fractures in the Barnett Shale and their importance for hydraulic fracture treatments, AAPG Bulletin, v. 91, no. 4 (April 2007), pp [2] Khair, H. A., Cooke, D., Backé, G., King, R., Hand, M., Tingay, M., and Holford, S., Subsurface Mapping of natural Fracture Networs; A Major Challenge to be Solved. Case Study from the Shale Intervals in the Cooper Basin, South Australia, Thirty-Seventh Workshop on Geothermal Reservoir Engineering, Stanford University, Stanford, California, January 30 - February 1, [3] Fisher, M.K., Wright, C.A., Davidson, B.M., Goodwin, A.K., Fielder, E.O., Buckler, W.S., Steinsberger, N.P., Integrating Fracture Mapping Technologies to Optimize Stimulations in the Barnet Shale, SPE 77441, SPE Annual Technical Conference & Exhibition, 2002, San Antonio, Texas, 29 Sept. - 2 Oct [4] Fisher, M.K., Heinze, J.R., Harris, C.D., Davidson, B.M., Wright, C.A., Dunn, K.P., Optimizing Horizontal Completion Techniques in Barnet Shale Using Microseismic Fracture Mapping, SPE 90051, SPE Annual Technical Conference & Exhibition, 2004, Houston, Texas, Sept [5] Gong, B., Guan, Q., Towler, B., Wang, H., Discrete Modeling of Natural and Hydraulic Fractures in Shale-Gas Reservoirs, SPE , SPE Annual Technical Conference & Exhibition, 2011, Denver, Colorado, 30 Oct. - 2 Nov [6] Mohaghegh, S.D., Virtual Intelligence and its Applications in Petroleum Engineering; Part 3. Fuzzy Logic, Distinguished Author Series. Journal of Petroleum Technology (JPT), November 2000.