# Estimation Methodology for Natural Gas Production In the Gulf of Mexico

# 

# ASA Committee on Energy Statistics

**Energy Information Administration**

**October 16, 17, 2003**

**Washington, DC**

# 

John Wood, EIA; Gary Long, EIA; and Lucy Luo, Z, Inc.

#   {#section-2 .unnumbered}

# Estimation Methodology for Natural Gas Production

#  In the Gulf of Mexico

## Introduction

The Gulf of Mexico (GOM) accounts for about 25% of the nation's total
natural gas production. The EIA needs good GOM production estimates for
several of its programs. However, the first release of Minerals
Management Service (MMS) accepted preliminary data (95 percent of wells
reported) lags by about 1.5 years. Reliable, final (essentially
complete) production data from (MMS) can add another 6 months to a year
to the lag. Because of these large lag times, the Reserves and
Production Division (RPD) in EIA's Office of Oil and Gas (OOG) began
developing and using various methodologies to create GOM natural gas
monthly production estimates based on data supplied by the MMS. This
document focuses on the methodology currently being used.[^1]

The MMS began releasing their raw (unedited, uncertified) well
production data in February 2003. After some editing, this data allows
RPD/OOG to construct well production distributions of the early but
incomplete well production data. The current method uses the early
reported well data distribution as a sample of the final complete
distribution to produce estimates of final monthly production data.
These estimates for a given month become more reliable over time as the
well data approaches completeness. A monthly estimate can be verified at
the well level by EIA when the well data are complete. As in any
real-world estimation process, all of the methods may require expert
judgment or analyst override, especially when unanticipated phenomena
such as hurricanes occur in the GOM.

## Data Description and Preparation

The process naturally starts with the MMS data and is dependent on their
ability to collect and report these data. Starting in February the MMS
made its new (suspended) well/completion production data available to
EIA and the public on its Website,
[http://www.gomr.mms.gov/homepg/pubinfo/freeasci/product/freeprod.html.]{.underline}
In part, this was in response to EIA's request for these data. The MMS
well/completion data is now available in three kinds of files:

1)  Accepted or verified data that has 95% or more of well/completions
    reported (latest month meeting this condition is March 2002),

2)  Accepted data that have less than 95% of wells reported (subsequent
    data), and

3)  Suspended data (newly available un-accepted, un-verified, un-edited
    data).

Data edited by RPD from these three files are combined to yield the
total reported production. Having access to these data has permitted the
development of the methodologies to estimate final production described
here. All these data are downloaded from the MMS Website in 8 separate
files.

Historical data on the MMS Website go back to 1996 and are updated
occasionally (1996 through 2003 last updated September 2003). Current
accepted data are updated monthly. Suspended data are currently updated
approximately twice per month. Accepted data are in zipped delimited
text files, and the suspended data are in zipped Excel files. This
detailed production data are by well completion by month.

SAS programs have been written to convert the downloaded delimited data
into two SAS data sets of summarized monthly production. One data set
contains all accepted data and the other contains the suspended data. Of
the roughly 64,530 records in the suspended data (released date:
09-15-2003), about 3,700 were duplicates in the data processed in
September 2003. Also, there are about 800 duplicates between the
suspended and accepted data. SAS programs are used to identify and
delete the duplicate records. There are approximately 6,500 records
(completions) each month with gas production greater than zero (these
include associated gas from oil completions).

## Gas Production Estimation Methodology

The reported data is essentially complete in mid 2001 and progressively
less complete closer to current months. Figure 1 shows reported well
completions dropping from about 6,500 in September 2001 to 5,900 in June
2003. A requirement of the methodology is to have the expected well
completions for each incomplete month. Then the expected completions and
average production per completion can be used to estimate production in
each month. Along with the reported well completions shown in Figure 1,
two other estimates of expected well completions are shown. These will
be discussed later.

![](media/image1.wmf){width="5.996527777777778in"
height="4.090277777777778in"}

## Simple Model

For any distribution the mean or average production per completion is:

$$M_{i}\  = \ \frac{P_{i}}{W_{i}}$$

Where:

M~i~ = Mean production per completion for month i

P~i~ = Total production for month i

W~i~ = Total producing completions for month i

The simplest model for the production is

$$P'_{i}\  = \ W'_{i} \ast M'_{i}$$

Where:

P'~i~ = Modeled production for month i

W'~i~ = Modeled or expected number of completions for month i

M'~i~ = Modeled mean production per completion for month i

However, the mean production per completion is skewed, so an estimation
based solely on the mean may not be a good estimate.

***Figure 2. Variable: Well_Rate (Month: May 2001)***

  -----------------------------------------------------------------------------------------------------------------------------------------------------------------
  **Moments**                                                                                                                                                    
  ------------------------ ---------- ------------ --------------- ----------------- ---------- ---------------- --------- ---------- ------------ ---------- -- --
  **N**                               6574                                                      **Sum Weights**                       6574                       

  **Mean**                            2.16640601                                                **Sum                                 14241.9531                 
                                                                                                Observations**                                                   

  **Std Deviation**                   5.91859516                                                **Variance**                          35.0297687                 

  **Skewness**                        6.87008015                                                **Kurtosis**                          63.735333                  

  **Uncorrected SS**                  261104.523                                                **Corrected SS**                      230250.67                  

  **Coeff Variation**                 273.198797                                                **Std Error                           0.07299679                 
                                                                                                Mean**                                                           

    **Basic Statistical                                                                                                                                          
  Measures**                                                                                                                                                     

  **Location**                                                     **Variability**                                                                               

  **Mean**                 2.166406                                **Std Deviation**                                       5.91860                               

  **Median**               0.318597                                **Variance**                                            35.02977                              

  **Mode**                 0.000290                                **Range**                                               91.78294                              

  **Tests for Normality**                                                                                                                                        

  **Test**                                         **Statistic**                                                 **p                                             
                                                                                                                 Value**                                         

  **Kolmogorov-Smirnov**                           **D**                             0.357172                    **Pr \>                           \<0.0100      
                                                                                                                 D**                                             

  **Cramer-von Mises**                             **W-Sq**                          261.0979                    **Pr \>                           \<0.0050      
                                                                                                                 W-Sq**                                          

  **Anderson-Darling**                             **A-Sq**                          1284.144                    **Pr \>                           \<0.0050      
                                                                                                                 A-Sq**                                          
  -----------------------------------------------------------------------------------------------------------------------------------------------------------------

  -----------------------------------------------------------------------
  **Quantiles (Definition 5)**      
  --------------------------------- -------------------------------------
  **Quantile**                      **Estimate**

  **100% Max**                      91.7830

  **99%**                           2.84755

  **95%**                           9.70106

  **90%**                           5.45597

  **75% Q3**                        1.71177

  **50% Median**                    0.318597

  **25% Q1**                        0.077419

  **10%**                           0.0234194

  **5%**                            0.0103548

  **1%**                            0.00135484

  **0% Min**                        0.000032258
  -----------------------------------------------------------------------

![](media/image2.jpeg){width="4.877777777777778in"
height="2.7631944444444443in"}

## 12 Classes Model

The data is divided into 12 classes and the latest six months of
complete production data (from April 2001 to September 2001) were used
to create the expected distribution for each month (Table 1). If
production per completion is less than 1.0 MMCF (most of these are oil
well completions) or over 100 MMCF (rare but highly productive gas well
completions), then they are defined as classes 1 and 12 respectively. As
for other classes, the well completion is an exponential distribution
(Figure 3.) with the formula of Wij=690(A)\^(J-2). EXCEL solver is used
to determine the coefficient A. Then based on the calculated well
completions in the six-month calibration or expected data distribution,
the class boundaries are determined for each class (Table 1).

![](media/image3.wmf)

Table1. Class Determination Method

  ---------- ---------------- ------------------------- ---------------------
  Class(J)   Well             Formula                   Well Production\
             Completions\                               Rate (MMCF/Day)
             Number                                     

  1          4402                                       0\<P\<1.0

  2          690              Wij=690(0.68192)\^(j-2)   1.0\<=P\<2.078

  3          471              Wij=690(0.68192)\^(j-2)   2.078\<=P\<3.601

  4          321              Wij=690(0.68192)\^(j-2)   3.601\<=P\<5.491

  5          219              Wij=690(0.68192)\^(j-2)   5.491\<=P\<7.951

  6          149              Wij=690(0.68192)\^(j-2)   7.951\<=P\<10.945

  7          102              Wij=690(0.68192)\^(j-2)   10.945\<=P\<15.15

  8          69               Wij=690(0.68192)\^(j-2)   15.15\<=P\<20.995

  9          47               Wij=690(0.68192)\^(j-2)   20.995\<=P\<31

  10         32               Wij=690(0.68192)\^(j-2)   31\<=P\<50

  11         22               Wij=690(0.68192)\^(j-2)   50\<=P\<100

  12         Uncertain                                  100\<=P
  ---------- ---------------- ------------------------- ---------------------

The basic concept assumes that incomplete data in recent months is a
sample distribution of what will ultimately be reported as the final
distribution. The expected distribution determines the expected
completions for each incomplete class. If the wells for a given month
equal or exceed the expected number of wells, then that month is
accepted as essentially complete. For months with fewer wells than
expected, any class with completions numbering more than the expected
distribution is considered complete and accepted as is. For all
incomplete classes, the total number of missing completions is allocated
to the incomplete classes proportional to the expected number of
completions of all classes that are not full. Then, for each class, the
product of the number of completions and the reported average production
per completion is the estimate of production for that class.

Classes 11 and 12 (production rate greater than 50 MMcf/d) are treated
differently. For class 11 we examine each well's historical production
record and expert judgment is used to determine where monthly production
is missing. For class 12, where each well can change the GOM monthly
production by about 1 percent, we use class 12 well completions as
reported.

The following chart shows a comparison of two incomplete months to the
expected distribution. Note that while not complete the January 2002
distribution is more complete than the January 2003 distribution.

![](media/image4.wmf)

![](media/image5.wmf){width="5.995138888888889in"
height="3.7090277777777776in"}

![](media/image6.wmf){width="5.999305555555556in"
height="3.4965277777777777in"}

## Estimates for Class 11

Since class 11 (\> 50 MMcf/day) shows significant growth from 1996 to
2002 it requires careful consideration. In 1996 this class had only 3 or
4 wells representing roughly 2 percent of GOM production. In the
calibration period, April through September 2001, the class holds about
22 wells with about 10 percent of the production (Figure 5). Table 2
shows the historical production of some of these wells with several
months of missing production. As an example, beginning with the August
2003 data, a petroleum engineer estimates four more wells will likely be
reported for Class 11 in June 2003, three in May 2003, one each in
February through April 2003,two for January 2003, and one in March 2002
(cells highlighted in yellow). Buy the second update in September 2003,
all but one well in January 2003 have been reported.

![](media/image7.wmf)

Table 2. Well Records Showing missing reported Value

![](media/image8.wmf){width="5.996527777777778in" height="5.13125in"}

The following are the formulas for the twelve class model.

For a distribution with 12 classes:

$$P_{i}\  = \ \sum_{j = 1}^{\text{12}}P_{i,j}$$

Where j is a class from 1 to 12.

The mean production per completion for any class is given by the
following:

$$M_{i,j}\  = \ \frac{P_{i,j}}{W_{i,j}}$$

Where

M~i,j~ = Mean production per completion of class j for month i

W~i,j~ = Number of completions of class j for month i

Therefore, the production model for a class, and total are:

$$P'_{i,j}\  = \ W'_{i,j} \ast M_{i,j}$$

$$P'_{i}\  = \ \sum_{j = 1}^{\text{12}}{P'_{i,j}}$$

Where M~i,j~ is the actual reported mean production per completion of
the sample distribution.

Production is then estimated by the following equation.

$$P'_{i}\  = \ \sum_{j = 1}^{\text{10}}\left\lbrack M_{i,j} \ast \text{WE}'_{i,j} \right\rbrack + M_{i,\text{11}} \ast \text{WE}'_{i,\text{11}} + M_{i,\text{12}} \ast W_{i,\text{12}}$$

Where:

P'~i~ = Modeled production for month i

M~i,j~ = Reported mean production per completion in class j for month i

WE'~i,j~ = Modeled or expected number of completions in class j for
month i

WE'~i,11~ = Professional Expected number of completions in class 11 for
month i

W~i,12~ = Reported number of completions in class 12 for month i

We have the following options for estimating production:

1)  Use the Expected/standard mean production per completion for each
    class.

2)  Use the Reported mean production per completion for each class.

3)  Use a Smoothed mean production per completion for each class.

4)  Use a Flat Expected well completion count.

5)  Use a Modeled Expected well completion count.

## Modeled Well Completions from Rig counts

For "normal" months a model based on the rig counts in the GOM can be
used to estimate the expected number of wells. The model is calibrated
to the six-month reference period (April to September 2001) and supplies
an expected number of well completions for each month. The expected
completion model is as follows.

$$\text{WE}_{i}\  = \ A \ast e^{\left\lbrack \left( \frac{B}{A} \right)\  \ast \ t \right\rbrack} + C \ast \text{SmRigs}_{i}$$

Where:

WE~i~ = Modeled or expected number of completions for month i

SmRigs~i~ = Smooth GOM rig count (6 month exponentially smoothed) for
month i

A, B, C = Fit parameters

The first half of the equation is a decline function that reduces the
number of completions each month. The second half of the equation adds
completions based on the smooth rig count. The resulting expected
completions are shown in Figure 1.

Following is a plot (Figure 6.) of the reported production and estimated
final production. Reported production is essentially complete through
September 2001 (accepted and edited data 99.5 percent complete plus RPD
edited suspended data). Major hurricanes or storms occurred in the fall
of 1998 and 2002. The estimated production shown here includes an
empirical adjustment to the number of completions during the storms of
2002.

Figure 6 shows 4 estimates using the modeled well completions and flat
well completions for both a 12 class distribution and a single class
distribution. All four cases include an empirical hurricane adjustment.
Reported production is essentially complete through September 2001.

![](media/image9.wmf){width="5.996527777777778in"
height="4.090277777777778in"}

## Well Distribution Stability Test

A key part of the procedure is the determination of the
expected/standard well distribution based on six months of essentially
complete data. We examined the stability of the well distributions using
a Chi-Square goodness of fit test. After applying this test to the
distribution in the 12 months prior to the 6 standard months and later
incomplete months, it was determined that all of the months have a
similar distribution (an exception will be discussed later). As an
example the following tables show the Chi-Square test for March 2001 and
April 2003.

CHI-SQUARE Goodness of fit Test

+---------------+--------------------+---------------+---------------+
| ***ProdD      |                    |               |               |
| ate=200103*** |                    |               |               |
+===============+====================+===============+===============+
| **Frequency   |                    |               |               |
| Count**       |                    |               |               |
+---------------+--------------------+---------------+---------------+
| **COUNT**     | **Frequency**      | **Percent**   | **Test\       |
|               |                    |               | Percent**     |
+---------------+--------------------+---------------+---------------+
| **4429**      | 4429               | 67.64         | 67.70         |
+---------------+--------------------+---------------+---------------+
| **701**       | 701                | 10.71         | 10.61         |
+---------------+--------------------+---------------+---------------+
| **472**       | 472                | 7.21          | 7.24          |
+---------------+--------------------+---------------+---------------+
| **329**       | 329                | 5.02          | 4.94          |
+---------------+--------------------+---------------+---------------+
| **213**       | 213                | 3.25          | 3.37          |
+---------------+--------------------+---------------+---------------+
| **131**       | 131                | 2.00          | 2.29          |
+---------------+--------------------+---------------+---------------+
| **121**       | 121                | 1.85          | 1.57          |
+---------------+--------------------+---------------+---------------+
| **74**        | 74                 | 1.13          | 1.06          |
+---------------+--------------------+---------------+---------------+
| **45**        | 45                 | 0.69          | 0.72          |
+---------------+--------------------+---------------+---------------+
| **33**        | 33                 | 0.50          | 0.49          |
+---------------+--------------------+---------------+---------------+
| **Chi-Square  |                    |               |               |
| Test\         |                    |               |               |
| for Specified |                    |               |               |
| Proportions** |                    |               |               |
+---------------+--------------------+---------------+---------------+
| # Chi-Square  |                    | 6.5276        |               |
+---------------+--------------------+---------------+---------------+
| **DF**        |                    | 9             |               |
+---------------+--------------------+---------------+---------------+
| **Pr \>       |                    | 0.6862        |               |
| ChiSq**       |                    |               |               |
+---------------+--------------------+---------------+---------------+
| ***Sample     |                    |               |               |
| Size =        |                    |               |               |
| 6548***       |                    |               |               |
+---------------+--------------------+---------------+---------------+

+---------------+--------------------+---------------+---------------+
| ***ProdD      |                    |               |               |
| ate=200304*** |                    |               |               |
+===============+====================+===============+===============+
| **Frequency   |                    |               |               |
| Count**       |                    |               |               |
+---------------+--------------------+---------------+---------------+
| **COUNT**     | **Frequency**      | **Percent**   | **Test\       |
|               |                    |               | Percent**     |
+---------------+--------------------+---------------+---------------+
| **4175**      | 4175               | 69.53         | 67.70         |
+---------------+--------------------+---------------+---------------+
| **631**       | 631                | 10.51         | 10.61         |
+---------------+--------------------+---------------+---------------+
| **405**       | 405                | 6.74          | 7.24          |
+---------------+--------------------+---------------+---------------+
| **273**       | 273                | 4.55          | 4.94          |
+---------------+--------------------+---------------+---------------+
| **170**       | 170                | 2.83          | 3.37          |
+---------------+--------------------+---------------+---------------+
| **129**       | 129                | 2.15          | 2.29          |
+---------------+--------------------+---------------+---------------+
| **90**        | 90                 | 1.50          | 1.57          |
+---------------+--------------------+---------------+---------------+
| **57**        | 57                 | 0.95          | 1.06          |
+---------------+--------------------+---------------+---------------+
| **48**        | 48                 | 0.80          | 0.72          |
+---------------+--------------------+---------------+---------------+
| **27**        | 27                 | 0.45          | 0.49          |
+---------------+--------------------+---------------+---------------+
| **Chi-Square  |                    |               |               |
| Test\         |                    |               |               |
| for Specified |                    |               |               |
| Proportions** |                    |               |               |
+---------------+--------------------+---------------+---------------+
| *             |                    | 14.2235       |               |
| *Chi-Square** |                    |               |               |
+---------------+--------------------+---------------+---------------+
| **DF**        |                    | 9             |               |
+---------------+--------------------+---------------+---------------+
| **Pr \>       |                    | 0.1146        |               |
| ChiSq**       |                    |               |               |
+---------------+--------------------+---------------+---------------+
| ##            |                    |               |               |
| ### **Sample  |                    |               |               |
| Size = 6005** |                    |               |               |
+---------------+--------------------+---------------+---------------+

## Hurricane Exceptions

For the months of September, October, November, and December 2002, the
Chi-Square test indicated that these distributions are different from
the standard 6-month distribution. Tropical storm Isidore and Hurricane
Lili in September and October affected production in these four months.
When wells are shut in for a partial month or even several months, the
distribution changes. An empirical downward adjustment to the number of
expected completions is necessary for months with a major storm. The
Chi-Square test for October 2002 indicates that the distribution is
different.

+---------------+--------------------+---------------+---------------+
| ***ProdD      |                    |               |               |
| ate=200210*** |                    |               |               |
+===============+====================+===============+===============+
| **Frequency   |                    |               |               |
| Count**       |                    |               |               |
+---------------+--------------------+---------------+---------------+
| **COUNT**     | **Frequency**      | **Percent**   | **Test\       |
|               |                    |               | Percent**     |
+---------------+--------------------+---------------+---------------+
| **4304**      | 4304               | 73.25         | 67.70         |
+---------------+--------------------+---------------+---------------+
| **541**       | 541                | 9.21          | 10.61         |
+---------------+--------------------+---------------+---------------+
| **380**       | 380                | 6.47          | 7.24          |
+---------------+--------------------+---------------+---------------+
| **232**       | 232                | 3.95          | 4.94          |
+---------------+--------------------+---------------+---------------+
| **136**       | 136                | 2.31          | 3.37          |
+---------------+--------------------+---------------+---------------+
| **98**        | 98                 | 1.67          | 2.29          |
+---------------+--------------------+---------------+---------------+
| **75**        | 75                 | 1.28          | 1.57          |
+---------------+--------------------+---------------+---------------+
| **46**        | 46                 | 0.78          | 1.06          |
+---------------+--------------------+---------------+---------------+
| **33**        | 33                 | 0.56          | 0.72          |
+---------------+--------------------+---------------+---------------+
| **31**        | 31                 | 0.53          | 0.49          |
+---------------+--------------------+---------------+---------------+
| **Chi-Square  |                    |               |               |
| Test\         |                    |               |               |
| for Specified |                    |               |               |
| Proportions** |                    |               |               |
+---------------+--------------------+---------------+---------------+
| *             |                    | 93.2510       |               |
| *Chi-Square** |                    |               |               |
+---------------+--------------------+---------------+---------------+
| **DF**        |                    | 9             |               |
+---------------+--------------------+---------------+---------------+
| **Pr \>       |                    | \<.0001       |               |
| ChiSq**       |                    |               |               |
+---------------+--------------------+---------------+---------------+
| ##            |                    |               |               |
| ### **Sample  |                    |               |               |
| Size = 5876** |                    |               |               |
+---------------+--------------------+---------------+---------------+

[^1]: Previous methodologies have been based on average month-to-month
    changes in historical GOM production data, average month-to-month
    changes in Texas production data, a simple linear model based on
    Texas production data, and the smoothed means of individual well
    production data.