SPE 98010 New Method for Production Data Analysis to ... · PDF fileposition of the Society of Petroleum Engineers, ... One of these methods is “flowing material balance”

Copyright 2005, Society of Petroleum Engineers This paper was prepared for presentation at the 2005 SPE Eastern Regional Meeting held in Morgantown, W.V., 14–16 September 2005. This paper was selected for presentation by an SPE Program Committee following review of information contained in a proposal 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 a proposal of not more than 300 words; illustrations may not be copied. The proposal 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 Most of the mature fields in the United States have been producing for many years. Production in these fields started at a time when reservoir characterization was not a priority; therefore they lack data that can help in reservoir characterization. On the other hand to re-vitalize these fields in a time that price of hydrocarbon is high, requires certain degree of reservoir characterization in order to identify locations with potentials of economical production. The most common type of data that may be found in many of the mature fields is production data. This is due to the fact that usually production data is recorded as a regulatory obligation or simply because it was needed to perform economic analysis. Using production data as a source for making decisions have been on the petroleum engineer’s agenda for many years and several methods have been developed for accomplishing this task. There are three major shortcomings related to the efforts that focus on production data analysis. The first one has to do with the fact that due to the nature of production data its analysis is quite subjective. Even when certain techniques show promise in deducing valuable information from production data, the issue of subjectivity remains intact. Furthermore, as the second shortcoming, existing production data analysis techniques usually address individual wells and therefore do not undertake the entire field or the reservoir as a coherent system. The third short-coming is the lack of a user friendly software product that can perform production data analysis with minimum subjectivity and reasonable repeatability while addressing the entire field (reservoir) instead of autonomous, disjointed wells. It is a well known fact that techniques such as decline curve analysis and type curve matching address individual wells (or sometime groups of wells without

geographic resolution) and are highly subjective. In this paper a new methodology is introduced that attempts to address the first and the second, i.e. unify a comprehensive production data analysis with reduced subjectivity while addressing the entire reservoir with reasonable geographic resolution. The geographic mapping of the depletion or remaining reserves can assists engineers in making informed decision on where to drill or which well to remediate. The third shortcoming will be addressed in a separate paper where a software product is introduced that would perform the analysis with minimum user interaction. The techniques introduced here are statistical in nature and focuses on intelligent systems to analyze production data. This methodology integrates conventional production data analysis techniques such as decline curve analysis, type curve matching and single well radial simulation model, with new techniques developed based on intelligent systems (one or more of techniques such as neural networks, genetic algorithms and fuzzy logic) in order to map fluid flow in the reservoir as a function of time. A set of two dimensional maps are generated to identify the relative reservoir quality and three dimensional maps that track the sweet spots in the field with time in order to identify the most appropriate locations that may still have reserves to be produced. This methodology can play an important role in identifying new opportunities in mature fields. In this paper the methodology is introduced and its application to a field in the mid-continent is demonstrated. INTRODUCTION Techniques of production data analysis (PDA) have improved significantly over the past several years. These techniques are used to provide information on reservoir permeability, fracture length, fracture conductivity, well drainage area, original gas in place (OGIP), estimated ultimate recovery (EUR) and skin. Although there are many available methods identified, there is no one clear method that always yields the most reliable answer1. Furthermore, tools that make these techniques available to the engineers are not readily available. The goal of this study is to develop a comprehensive tool for production data analysis and make it available for use by industry. Production data analysis techniques started systematically

SPE 98010

New Method for Production Data Analysis to Identify New Opportunities in Mature Fields: Methodology and Application Mohaghegh, S. D., Gaskari, R. and Jalali, J., West Virginia University

2 New Method for Production Data Analysis to Identify New Opportunities in Mature Fields SPE 98010

with a method presented by Arps in the 1950s1. Arps decline analysis is still being used because of its simplicity, and since it’s an empirical method, it doesn’t require any reservoir or well parameters. Fetkovich proposed a set of equations described by an exponent, b, having a range between 0 and 12. Arps’ equation is based on empirical relationships of rate vs. time for oil wells and is shown below3:

( )( )bi

i

tbD

qtq 11+

= (1)

In this relationship, b = 0 and b = 1 represent exponential and harmonic decline, respectively. Any value of b between 0 and 1 represents a hyperbolic decline. Although Arps equation is only for pseudo-steady state conditions, it has been often misused for oil and gas wells whose flow regimes are in a transient state. The Fetkovich methodology analyzes oil wells producing at a constant pressure. He combined early time, analytical transient solutions with Arps’ equations for the later time, pseudo-steady state solutions. The Fetkovich method like Arps equation, calculates expected ultimate recovery. Carter’s gas system type curves were published in 19853. Carter used a variable λ identifying the magnitude of the pressure drawdown in gas wells. A curve with a λ value of 1 corresponds to b=0 in Fetkovich liquid decline curves and represents a liquid system curve with an exponential decline. Curves with λ=0.5 and 0.75 are for gas wells with an increasing magnitude of pressure drawdown. Agarwal-Gardner also introduced a method for production data analysis in 1998. This technique combines decline curve and type curve concepts for estimating reserves and other reservoir parameters for oil and gas wells using production data. 3 Other methods were introduced by Palacio & Blasingame, and Agarwal-Gardner, which provide information on gas in place, permeability, and skin. There are also modern analytical methods that do not use type curves. One of these methods is “flowing material balance”. This technique provides the hydrocarbons in place using production rate and flowing pressure data from a reservoir under volumetric depletion. 3 METHODOLOGY In this section a step by step process for Intelligent Production Data Analysis (IPDA) is introduced. Several items must be mentioned in order to put the degree of reliability of these techniques in perspective. Please keep in mind that IPDA is developed for situations that only production data is available. Therefore, in situations that one has access to other data such as logs, core analysis, pressure tests, geologic models, etc. one might choose to use other well established techniques.

Nevertheless, it will be demonstrated that as more data such as those mentioned above are available, one may use them to increase the accuracy and the reliability of the methodology being introduced here. Furthermore, as it will be explained in this paper, IPDA, at this time, may come across as a lengthy procedure. This is due to the fact that IPDA is an iterative and essentially an optimization technique. The next step in our efforts is the development of an automatic or semi-automatic procedure that will make this process much faster and easier to implement. In the new development the goal is to reduce the user interaction to a minimum. Figure 1 is a flow chart that describes the methodology used in this process. As shown in the chart, there are four distinct and inter-related parts to this process, namely decline curve analysis, type curve matching, single-well reservoir simulation (history matching) and finally relative reservoir quality indexing and mapping that includes three dimensional mapping of remaining reserves. In a nutshell, IPDA is a two step process. In the first step the idea is to simultaneously, interactively and iteratively perform decline curve analysis, type curve matching and history matching on the production data of a particular well in the field until convergence is achieved to a unified set of reservoir characterizations. Given the fact that each of these techniques are quite subjective by nature, by letting each one technique to guide and keep an eye on the other two during the analysis, the degree of confidence and reliability on the results as well as repeatability of the analysis will increase. The second step is to use the well-based results from the first step and super impose them on the field as a whole and develop two and three dimensional maps of reservoir quality and remaining reserves. In the following sections each part of this process will be explained. Decline Curve Analysis Decline curve analysis is the first step in the process. The production data is plotted on semi-log scale and decline curve is fitted. If one uses hyperbolic decline, then the outcome of the decline curve analysis would be “qi” (initial flow rate), “Di” (initial decline rate) and “b” (hyperbolic exponent). Figure 2 is an example of decline curve analysis performed on a well located in the Golden Trend Fields of Oklahoma. It is a good idea to plot both production rate and cumulative production versus time simultaneously and try to match the production rate while keeping an eye on the cumulative production. This usually helps in getting a reasonably good match. Once the match is completed the result would be a set of decline curve characteristics as mentioned above. Based on the decline curve characteristics that you have identified in this step you can easily calculate Estimated Ultimate Recovery (EUR) for a certain number of years, say 30 years. The 30 year EUR for the well in Figure 2 is calculated to be 1,795 MMSCF. Type Curve Matching Two things are important in selecting the type curves for this

SPE 98011 Mohaghegh, Gaskari and Jalali 3

step of the analysis. First, the type curves must be for rate and not pressure, and second, it would be great if it could provide some commonality with the decline curve results, such that it would use some of the results of the decline curve. Something that would weave the two (the decline curve analysis and the type curve matching) together. This is essential since the procedure would call for an iterative process. In this case a set of type curves for low permeability reservoirs with hydraulically fractured wells4 were used. Figure 3 shows the type curve match achieved for the same well shown in Figure 2. The link between this set of type curves and the decline curve analysis are the hyperbolic exponent “b”. In Figure 3 it is shown that the type curves for a particular “b” value are generated and used for matching the production rates from this well. The “b” value used for this well (b=0.9) was the same that was calculated during the decline curve analysis. This is important since a new set of decline curves can be developed for any values of “b”. Figure 4 shows the production data from this well plotted against type curves that have been generated for “b” values 0.1 (on the left) and 1.5 (on the right). Once the match is accomplished, the results from type curve matching is a set of parameters such as permeability, fracture half length and drainage area. Results of these parameters appear on the top left corner of the type curve screen as show in Figures 3 and 4. On the top right corner of the screen the EUR (again for 30 years) is calculated and shown. The EUR from the decline curve analysis was calculated to be 1,795 MMSCF while the EUR from the type curve matching is calculate to be 1,800 MMSCF as shown in Figure 3. The EUR calculated from the decline curve analysis is used as a controlling factor for the match of the type curve. This allows us to develop more confidence on the type curve matching and have a bit more faith on the values calculated for permeability, fracture half-length and the drainage area. The EUR values from the two procedures (type curve and decline curve) are used in order to finalize the match in both cases. As we mentioned before this is an iterative procedure and sometimes it may take several iterations before the decline curve analysis and the type curve match would agree with one another. One important issue that needs to be mentioned at this point is that the type curve matching process requires knowledge about a set of parameters. These parameters are used during the calculation of permeability, fracture half length, drainage area and EUR. These parameters are listed below:

Initial reservoir pressure; Average reservoir temperature; Gas specific gravity; Isotropicity (kx/ky ratio); Drainage shape factor (L/W ratio); Average porosity; Average pay thickness; Average gas saturation; Average flowing bottom-hole pressure.

Most of the parameters above can be (and usually are) guessed

within a particular range that is acceptable for a particular field. Usually the initial reservoir pressure for a field or formation is known with reasonable range or it can be assumed based on formation depth. Formation depth can also be a good indication of average reservoir temperature. Gas specific gravity can be easily calculated based on the assumed average initial pressure and reservoir temperature. In most of our calculations we assume that the reservoir is isotropic, meaning that the kx/ky ratio is equal to 1. The drainage shape factor is also assumed to be 1 meaning that we are assuming a square drainage area. Average porosity, thickness and gas saturation can be calculated for each well from logs, if they are available. If they are not, then an average value for the entire field can be assumed. This method allows for better matches and results in higher confidence level if wells in the field have logs. By having access to logs; porosity, thickness and saturation can be calculated and used individually for each well during the analysis. If and when such logs are not available or prove to be too expensive to analyze (which seems to be the case in many fields in the United States) then the procedure allows the user to input an average value (as the best guess) for all wells. In the bottom right corner of Figure 3 you can see two buttons. The button on the top lets the user input values for the above parameters that will be used as default for the entire field and the second button will let the user to input and over-write the default values for any specific well. Single-Well Reservoir Simulation This step of the analysis calls for history matching the production data using a single-well, radial reservoir simulator. Reservoir characterization data that is the result of type curve matching is used as the starting point for the history matching process and the objective is to match the production data of a particular well. History matching is not a simple and straight forward procedure. Care must be taken in order to preserve the integrity of the match, in other words, the match that results from this procedure must make physical sense. One of the main reasons for performing the history matching in conjunction with the type curve matching and the decline curve analysis as an iterative process is to preserve the integrity of the entire process. Many times the results of the history matching will force us to go back and re-evaluate our decline curve results and the type curve match. This rigorous iterative process is the key to the successful completion of this process. This is performed for all the wells in the field. In most cases a match should be possible within reasonable ranges of the reservoir characteristics that were used as the starting point (result of iterative type curve matching and decline curve analysis). This may prove to be a long process. The goal is to automate this process with minimal user interaction. Once a match is accomplished within a reasonable range of all the reservoir characteristics, the ground work has been established for another important step in the analysis, namely Monte Carlo Simulation. Since now we have (most probably) diverged from the reservoir characteristics that we started with, it is reasonable to expect that we have converged on a


range of values for each of the parameters rather than a single value. In order to get a more realistic look at the capabilities of a well and its potential future production a Monte Carlo simulation is performed at this point. During the Monte Carlo simulation each of the parameters that play a role during the simulation process is represented by a probability distribution function (instead of a crisp, certain number) with characteristics that have been identified during the entire integrated analysis, i.e. decline curve analysis, type curve matching and history matching using reservoir simulator. The result of the Monte Carlo simulation is a probability distribution of the 30 year EUR. If we have done everything properly, it is expected that the EUR that was calculated during the decline curve analysis and the type curve matching process for a given well would fall within the probability distribution that has been calculated as the result of the Monte Carlo simulation. As the values of these EURs get closer to higher probability values, our confidence on the accuracy of the ranges (of the reservoir characteristics) that were used in the Monte Carlo simulation will increase. Mapping of the Reservoir Quality Once the integrated production data matching is completed and a set of reservoir characteristics are identified, it is time to produce some maps that would ultimately help operators in identifying the sweet spots in the field. The two and three dimensional maps include relative reservoir quality indices as a function of time as well as some reservoir characteristics and map of the remaining reserve as a function of time. Let us look at an example to help demonstrate the process. First step is to identify the parameter that would be used as the key for the reservoir partitioning. Figure 5 shows that “Gas” has been selected as the fluid of choice and from among the Production Indicators (PIs) that have been calculated “First 3 Months of Production” is selected as the output as well as an attribute. The PI identified as the output (you may only have one output but multiple attributes) is used as the key to partitioning the field while attributes are those PIs that their average values are calculated for each partition and are used as a guide to fine tune the partitioning process. Once these selections are made, the next step is to partition the field into different reservoir qualities based on the average values of the output for each partition. This is shown in Figure 6. As displayed in this figure, 85 wells in this field have been analyzed for this study. Fuzzy pattern recognition5 is performed on the latitude and longitude as a function of the selected output and then super imposed on one another to create the partitions as shown in Figure 6. The numbers on the map indicate the relative reservoir quality with 1 being the highest quality. In this representation, the relative quality of the reservoir rock in terms of hydrocarbon productivity decreases with higher numbers of RRQI. The values corresponding to the partitions in Figure 6 are shown in Table 1. In this table the 85 wells in the field are divided into five different categories identified as Relative Reservoir Quality Indices of 1 through 5. The average value of

the PI (First 3 Months of Production) decreases as the index of the relative reservoir quality increases. This confirms our partitioning practices. Figure 7 displays a three dimensional view of the reservoir quality with smooth transitions from one reservoir quality to the next. If the output of the problem is changed from a PI representing early life of the field to one representing a later time in the life of the field, then the difference in these figures may represent depletion in the reservoir or fluid movement. Furthermore, each of the reservoir characteristics that are generated as the result of the analysis that was mentioned in the previous sections can be mapped throughout the field. This will provide a good visual of the status of the field at different times in its life and can provide insight on the locations that have high production potentials for future development. More details on these mapping procedures and how remaining reserve are calculated on a field-wide basis is provided in the next section. RESULTS & DISCUSSIONS The methodology described in this paper was applied to production data from 85 wells producing in the Golden Trend fields of Oklahoma. The only data used to perform the analysis shown here are the production data that are publicly available; therefore, all these analyses can be performed on any field throughout the United States and Canada. This may prove to be a valuable tool for independent asset valuation prior to any acquisition. The first step in the process is performing three different kinds of analysis techniques on each well simultaneously in an iterative manner in order to converge to one unified set of matches and EURs. The three analysis techniques are decline curve analysis, type curve matching and history matching using a single-well radial reservoir simulation. Figures 8 through 12 show results of two analyses, reached simultaneously (iteratively) on five different wells in the filed. The graph on the left in each figure is the decline curve analysis and the graph on the right is the type curve match. The EURs are quite comparable in each of the figures. Table 3 shows the parameters that were calculated in this analysis for the five wells shown in Figures 8 through 12. As mentioned before, the hyperbolic exponent found in the decline curve analysis is used to identify the set of type curves that should be used during the matching process. Then the EUR from the type curve matching is compared with the EUR calculated from the decline curve analysis. If the difference is greater than a certain threshold, then the characteristics of the decline curve is modified and the new b value is used in the type curve matching resulting in a new EUR. This process is continued until the hyperbolic exponent provides the closest EUR between type curve matching and the decline curve analysis. Results shown in Table 3 are the final results that have been achieved upon convergence when all three methods (decline curve analysis, type curve matching and history matching using a single-well radial simulation model) are performed simultaneously and convergence is achieved. Figures 13 and 14 show the history match results achieved for two of the


wells in the database. Figures 15 an 16 are results of Monte Carlo simulation performed on the two wells shown in Figures 13 and 14. During the Monte Carlo simulation each of the parameters that were used and calculated during the type curve matching and history matching process is assigned a probability distribution function and the history matched model is run for hundreds of times each time calculating the 30 year EUR. The 30 year EUR calculated during the decline curve analysis and the type curve matching are also shown in Figures 15 and 16. As expected (since this was an iterative procedure to converge to a common set of characteristics that would provide a common set of results) the 30 year EUR values from the other two techniques fall in the high frequency areas of the probability distribution function of the 30 year EUR obtained from Monte Carlo simulation. Once the iterative process of matching the production data of each well individually using three simultaneous process of decline curve analysis, type curve matching and history matching is completed, several production indicators (that are simply statistical measures of a well’s production) are calculated such as:

Best 3 Months of production Best 6 Months of production Best 9 Months of production Best 12 Months of production First 3 Months of production First 6 Months of production First 9 Months of production First year cumulative production Three year cumulative production Five year cumulative production Ten year cumulative production

Other indicators are calculated as a result of decline curve analysis and type curve matching such as:

Decline curve related indicators: o Initial flow rate o Initial decline rate o Hyperbolic exponent o Estimated Ultimate Recover

Type curve matching indicators

o Permeability o Fracture half length o Drainage area o Permeability * Thickness (kh) o Estimated Ultimate Recovery

All the above characteristics are now available for all the wells in the field, in our case for 85 wells. Figures 17 through 20 show the change of the relative reservoir quality index with time. In Figure 17 the Relative Reservoir Quality Index (RRQI) is shown as a result of First 3 months of production. As mentioned before RRQI 1 indicates the best location in the field followed by numbers from 2 to 5. Please keep in mind that all these numbers and quality indicators are relative as the name points out.

The RRQIs are identified based on partitioning the fuzzy pattern recognition performed on latitude and longitude as shown in the figure. The red and blue lines in the fuzzy pattern recognition part of the figure are the partitioning agents that can be moved up and down. Each time the partition agents are moved the partitioning of the field is renewed and the average Production Indicator (in the case of Figure 17, the First 3 Months cumulative production) for each of the partitions are recalculated. This process can be used as a guide for identification of the correct partitions in the field. Comparing Figures 17 and 18 one can see the migration of several wells from RRQI “1” to RRQI “2” and from RRQI “2” to RRQI “3”. Based on the reservoir characteristics (and sometimes well related issues such as damage) several wells do not produce as well as others and exhibit different behaviors. If it is a well related problem, these would be isolated wells and in a long term analysis can be identified by other means such as a process called “Intelligent Candidate Selection Analysis6-8” (ICSA). Integration of ICSA with IPDA is the topic of a future paper. This integration allows the identification of wells that are in need of remedial processes and those that cannot be rescued. This integration would unify the field development strategies as a combination of well remedial operations and new drilling opportunities. Table 3 shows the statistical change of the RRQIs as the analysis move from first 3 month of production to first 9 months of production. Following items are notable in this table. The value of average cumulative production in both cases (3 and 9 months of production) decreases with an increase in RRQI (please remember that increase in RRQI indicates a relative decrease in the reservoir quality). Furthermore, the number of wells that are located in RRQI 1, 2, and 3 are different in two cases, identifying the migration of wells from one RRQI to another which can be an indication of depletion. In Figure 19, First 3 years of production is selected as the output for partitioning. Changes in this figure as compared with Figure 18 show the movement of RRQIs in the field that is a clear indication of depletion and fluid movement in the reservoir. Furthermore, Figure 20 shows the 30 year EUR forecast for this field if no new wells are drilled. This can help operators to identify the locations in the field that would present good candidate for infill drilling due to the remaining gas in place. Similar maps can be generated for other parameters that have been calculated as a result of decline curve and type curve matching analyses. Figure 21 shows the two dimensional map based on reservoir permeability and Figure 22 is the three dimensional version of permeability distribution in the filed. The next step is to track the fluid movement in the reservoir. In case of primary recovery this can simply mean depletion or detection of remaining hydrocarbon in place. Wells are drilled at different times and therefore may be in production for different length of time. This is shown in Figure 23 where example for several wells is presented. What we are interested


in is the map of the remaining fluid in the reservoir. This can help operators (in conjunction with other maps such as those presented in Figure 17 through 24) to identify the best location for drilling new wells or to make decision on the wells that need to be stimulated or re-stimulated. As shown in Figure 23 we are going to identify the remaining gas in place by calculating the 30 Year EUR for each well and then subtracting the amount of gas that they have produced up to a certain point in time. This way we can identify the remaining gas in place as of any date of interest. Figure 23 shows the dates at which we would perform the above calculation. Identification of remaining gas in place based on the current cumulative production and decline curve forecast is not new. New items that are introduced here are the identification of fuzzy patterns that evolves in the field and three dimensional mapping of the remaining hydrocarbon in place. Such mapping is shown in Figures 24 and 25. In Figure 24 the remaining gas in place as of year 2005 is shown and Figure 25 is the same map after 15 more years of production. The difference between these two figures shows the depletion in the reservoir and identifies the parts of the field that still have potential for more recovery. Figure 26 shows the reservoir depletion in 5 year intervals by mapping remaining gas in place as of January of 2005, 2010, 2015 and 2020. An important note is that using this technique, new wells can be virtually drilled in a particular location and their effects (production) on the remaining reserves can be observed. In other words many “What If” scenarios can be played out in order to make the best possible decision on the location of the next wells. Application of these techniques to water flooding (and other enhanced recovery) operation may prove to be very useful in identification of flood fronts as a function of injection and production data when reservoir characteristics of the formation is complex and not very well understood. CONCLUSIONS A new technique for field-wide production data analysis has been introduced in this paper. This technique takes advantage of an iterative procedure to unify the matching of production data for each well using three independent techniques, namely decline curve analysis, type curve matching and history matching using a single-well radial reservoir simulation model. Working with the above three techniques simultaneously and iteratively a set of reservoir characteristics emerge that approximately satisfies all three production matches. In absence of any reservoir characterization studies, this technique resolves the subjectivity associated with each of these techniques by using each one of them as a controlling factor for the other two in an iterative fashion. The techniques presented in this paper also provide means for mapping reservoir quality throughout the field using the results of the production data matching mentioned above. The

result is a set of two and three dimensional maps that identifies the potential places for drilling new wells. This technique uses state of the art in intelligent systems in order to discover patterns in the production data using all the wells in the field simultaneously. This is an important recognizing factor for this technique as compared to others in the literature that allows mapping of the reservoir characterization and remaining reserves in the field in two and three dimensional graphs. Implementation of this technique in its present form for a field with tens or hundreds of wells is a relatively lengthy process. The research and development team is currently working to automate this process that would require minimum user interaction during the iterative process. A new paper introducing the automated procedure will be presented as soon as the development process is completed. REFERENCES

1. “A Systematic and Comprehensive Methodology for Advanced Analysis of Production Data”, L. Matter, D. M. Anderson, Fekete Associates Inc., SPE 84472, 2003.

2. “Useful Concepts for Decline-Curve Forecasting, Reserve Estimation, and Analysis”, M.J. Fetkovich, E.J. Fetkovich, and M.D. Fetkovich, Phillips Petroleum Co., SPE Reservoir Engineering, February 1996.

3. “Analyzing Well Production Data Using Combined Type Curve and Decline Curve Analysis Concepts”, Ram G. Agarwal, David C. Gardner, Stanley W. Kleinsteiber, and Del D. Fussell, Amoco Exploration and Production Co., SPE 49222, 1998

4. “Advanced Type Curve Analysis for Low Permeability Gas Reservoirs”, Cox, Kuuskraa and Hansen. SPE 35595, 1998.

5. Fuzzy Clustering Models and Applications, Studies in Fuzziness and Soft Computing, Volume 9. M. Sato, Y. Sato, L.C. Jain. Physica-Verlag, Heidelberg, New York.

6. "Benchmarking of Restimulation Candidate Selection Techniques in Layered, Tight Gas Sand Formations Using Reservoir Simulation", Reeves, Bastian, Spivey, Flumerfelt, Mohaghegh, and Koperna, SPE 63096, 2000.

7. "Development of an Intelligent Systems Approach to Restimulation Candidate Selection", Mohaghegh, Reeves, and Hill, SPE 59767, 2000.

8. "Restimulation Technology for Tight Gas Sand Wells", Reeves, Hill, Hopkins, Conway, Tiner, and Mohaghegh, SPE 56482, 1999.


Table 1. Average value of First 3 Months PI for each of the partitions.

RRQI No. Wells % of Wells First 3 Months of Production (MSCF)

1 15 17.65 89,684.5 2 22 25.88 74,832.9 3 9 10.59 51,179.0 4 22 25.88 30,801.4 5 17 20.00 22,141.6

TOTAL 85 100

Figure 1. Flow chart of Intelligent Production Data Analysis – IPDA.


Figure 2. Decline curve analysis of well “C-ANY #1-4”

Figure 3. Type curve matching of well “C-ANY #1-4”


Figure 4. Type curve matching of well “C-ANY #1-4” with “b” values 0.1 and 1.5.

Figure 5. Selecting output and attributes for partitioning the field into zones with different quality.


Figure 6. Partitioning the field into zones with different quality based on First 3 Months PI.

Table 2. Results of field partitioning for 3 and 9 months production.

Number of Wells Average Cumulative Production RRQI 3 Months 9 Months 3 Months 9 Months

1 15 9 89,684 270,355 2 22 21 74,832 198,686 3 9 16 51,179 152,250 4 22 22 30,801 100,389 5 17 17 22,141 67,964


Figure 7. Three dimensional view of the relative reservoir quality partitioning of the field based on First 3 Months PI.


Figure 8. Simultaneous decline curve analysis and type curve matching of well C-LL#1-28.

Figure 9. Simultaneous decline curve analysis and type curve matching of well C-WBY #1-1.

Figure 10. Simultaneous decline curve analysis and type curve matching of well C-AN #2-27.


Figure 11. Simultaneous decline curve analysis and type curve matching of well N-CERO 3-3.

Figure 12. Simultaneous decline curve analysis and type curve matching of well T-dle A#1.

Table 3. Results of simultaneous decline curve analysis and type curve matching shown for five wells in the field.

Decline Curve Analysis Type Curve Matching

Well Name Qi (MSCF) Di b EUR

(MMSCF) K

(md) Xf (ft)

A (ac)

EUR (MMSCF)

C-LL#1-28 40,500 0.051 0.51 1,449.3 3.45 35.8 73.4 1,455.5 C-WBY #1-1 28,130 0.057 1.15 1,709.6 2.09 28.3 48.1 1,671.6 C-AN #2-27 47,830 0.248 1.30 1,314.8 3.78 29.3 17.7 1,328.3 N-CERO 3-3 6,333 0.026 1.80 800.8 0.18 27.2 6.8 809.6 T-DLE A#1 10,894 0.268 1.42 318.9 0.75 8.6 4.2 318.5


Figure 13. History match results for Well C-ER #1-16.

Figure 14. History match results for Well C-VA #1-34.


Figure 15. Results of Monte Carlo Simulation study for Well C-ER #1-16.

Figure 16. Results of Monte Carlo Simulation study for Well C-VA #1-34.


Figure 17. Relative Reservoir Quality Index based on first three months of cumulative production.

Figure 18. Relative Reservoir Quality Index based on first nine months of cumulative production.

Fuzzy Pattern Recognition

Fuzzy Pattern Recognition


Figure 19. Relative Reservoir Quality Index based on first three years of cumulative production.

Figure 20. Relative Reservoir Quality Index based on 30 year EUR forecast.


Figure 21. Distribution of reservoir permeability in the field based on IPDA’s integrated technique.


Figure 22. Three dimensional distribution of reservoir permeability in the field based on IPDA’s integrated technique.


Figure 23. Production schedule for different wells, corresponding production forecast and dates for calculating remaining gas in place.

Figure 24. Remaining gas in place as of January 2005.


Figure 25. Remaining gas in place as of January 2020.


Figure 26. Remaining gas in place as a function of time.

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SPE 98010 New Method for Production Data Analysis to ... · PDF fileposition of the Society of Petroleum Engineers, ... One of these methods is “flowing material balance”