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PTE 588: SMART OILFIELD DATA MINING
FALL 2015
Topic: Oil and Gas Production Forecasting
Using Data Mining Techniques
Project Report
Team (4) Members:
Sultan Attiah
Long Vo
Walter Morales
Abhishek Ajith
Page 1 of 30
Contents
1. Objective And Solution Of The Project
2. Background On Ventura Oilfield
Ventura Oilfield
Geology
Production In Ventura County
Why There Are Less Injection Wells In Ventura County
3. Data Mining Process
Data Preparation
Data Surveying
Data Modeling
4. Conclusion
5. Implementation Of This Project
6. References
Page 2 of 30
Objective:
The objective of the project is to create a model of a reservoir using the data mining technique
by analyzing and interpreting field data. Moreover, characterize the reservoir after matching
the production from existing wells to find the best drilling spots, predict future production of
new wells and identify the best well type associated with the location.
Data Mining Process
For the purpose of this project we have selected the Ventura Oil Field; the first step in the data
mining process was the data preparation step, this consisted of gathering the data from the
DOGGR web page and cleaning and preprocessing the data. The data was plotted and mapped
to detect the relationship among attributes. Finally, the modeling step was performed. A detail
description of all these three steps will be described in this section.
1) Data Preparation Process:
Data gathering:
o Download the data
o Construct master database to link all downloaded databases together and
connect them by using an SQL script in order to query the data
o Eliminate all duplicate and “bad data”; replace all null data by zeros to prevent
problem when aggregating the data (for example when calculating cumulative
production)
o Review the structure of the data and make sure that “irrelevant” data is kept out
of the analysis
2) Data Surveying Process:
Calculate cumulative plots:
o Construct cumulative production plots for the entire field on well by well basis
o Calculate well counts based in oil active wells
Data Normalization:
o Clean all initial production recorded as zero from the production table for all
wells.
o Create a VBA macro in order to normalize the production time per month
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Data Classification:
o Calculate ratios (BOPD, BWPD, BGPD), peak oil (BOPD) and the time that the
well takes to reach its peak production or ramp time(Normalized time in
months)
o Generate statistics and distribution plots in order to classify all wells based in the
calculated properties.
o Classify the wells base in the calculated properties
Data Visualization:
o Use the previous classification to generate maps and normalized cumulative
production in order to investigate the relationships between several attributes
3) Data Modeling Process:
Data Preprocessing:
o Filter non-essential attributes.
o Identify nominal and numeric attribute types
o Identify the class attribute
o Normalize all numeric attribute to have the same scaling
Sampling:
o Divide dataset into training and testing set using Weka supervised resample filter
80% used for training set
20% used for testing set
Build cluster model from Simple K-Means:
o Create 3 sub model to test for overfitting:
3 cluster
5 cluster
10 cluster
Compare training set output cluster with testing set output cluster:
o Determine error
o Determine validity of sample set
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Evaluate and extract knowledge from cluster results:
o Location
o Production
o Ramp time
o Peak production
o Well type
Form conclusion and advise on well type, location, and productivity
Background on Ventura Oilfield
The Ventura Oil Field is a large and currently productive oil field in the hills immediately north
of the city of Ventura, California located within the United States. It was discovered in 1919,
and with a cumulative production of just under a billion barrels of oil as of 2008. It is the
seventh-largest oil field in California, retaining approximately 50 million barrels in reserve, and
has 423 wells still producing. The oil field is on and beneath the ranges of hills northwest, north
and northeast of the city of Ventura. The Ventura River, flowing down from Ojai, cuts through
the field, and empties into the Pacific Ocean at Ventura. Native vegetation in the hills is
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predominantly chaparral and coastal sage scrub, and riparian woodland is found along the
course of the Ventura River. The terrain on the hills is steep, and the roads to well pads, tanks,
and other infrastructures make numerous switchbacks. Most of the field is hidden from view
from the city because of the first steep range of hills. The oil field is one of several following the
east-west trend of the Transverse Ranges at this point: to the west are the San Miguelito Oil
Field and the Rincon Oil Field, and to the east, across the Santa Clara River valley, the large
South Mountain Oil Field adjacent to Santa Paula and the smaller Saticoy Oil Field along the
Santa Clara River east of Saticoy. To the north are the smaller Ojai and Santa Paula fields. Total
productive area of the field, projected to the surface, encompasses 3,410 acres (13.8 km2).
Geology:
In the Ventura field, the large-scale structural feature responsible for petroleum accumulation
is the Ventura Anticline, an east-west trending geologic structure 16 miles (26 km) long, visible
in the numerous rock outcrops in the rugged topography of the area. This anticline dips steeply
on both sides, with the dip angle ranging from 30 to 60 degrees, resulting in a series of rock
beds resembling a long house with a gabled roof, under which oil and gas collect in abundance.
The underlying Monterey Formation is presumed to be the source of the oil accumulations in
the Ventura field, as well as the other two fields in the same geologic trend. The Monterey
Formation is rich in organic matter, averaging 3-5 percent but reaching 23 percent in some
area. Oil gravity and sulfur content are similar in all pools in the field, with an API gravity of 29-
30 and approximately 1 percent sulfur by weight.
Production In Ventura County:
Occidental, which is one of the largest operators in Ventura County through Vintage Production
California LLC, stated it added wells in existing fields, explored in areas slightly outside of known
reserves and brought old wells back into use in California, resulting in 124 new wells and 388
wells being worked over. “Improved oil recovery increases the volume of oil pulled from a
reservoir, while enhanced oil recovery techniques help Occidental continue extracting oil from
a reservoir after primary and secondary recovery methods have been used” as stated by
Occidental spokesperson which outlines their objective. As of 2011 the number of active wells
in Ventura stands at 1708 and the oil produced in barrels is estimated to be around 8,308,059.
Page 6 of 30
Because of this production boom Vintage Petroleum, a subsidiary of Occidental Petroleum, has
entered into 192 lease agreements over the past six months in deals involving at least 9,000
acres. Since acquiring properties in Ventura County in 2006, Vintage Production California has
reworked field infrastructure to support and expand existing projects and develop new
opportunities for oil recovery. In the 1960's, the first serious exploration of submerged lands
was undertaken, which led to the discovery of two large oil fields off the Coast of Santa Barbara
County. Subsequently, Ventura County oil exploration efforts were then concentrated on
offshore fields and operators began to explore new, more efficient methods of oil production.
(e.g.: Fracking & EOR Techniques).
Why Are There Less Injection Wells in Ventura County:
Injection wells can be used for enhanced oil field recovery processes like a water flood, steam
flood, and cyclic steam injection, as well as oilfield wastewater disposal. These are called Class II
injection wells which are a special label for injection well sand they are regulated by DOGGR
through an agreement with the EPA. Ventura County doesn’t have many Injection Wells due to
the fact it has several environmental regulations and bans imposed over alleged use of harmful
fracking fluids which has seeped into nearby deep water aquifers and contaminated
groundwater. (The Anterra site – a commercial Class II Injection Well).
The real environmental issue surrounding hydraulic fracturing is not the loss of local vegetation,
but the effects this process has on local groundwater. So Ventura County updated its oil
application process to require new drilling proposals involving fracking or acidizing to provide
detailed information, including the source and amount of freshwater used, and disposal
methods for frac flow back and other wastewater.(which is a hindrance).
Another reason is after a CalTech Study linking induced seismicity with fracking gained traction,
many councils have blindly presumed the correlation between minor earthquakes and hydraulic
fracturing. The Environmental Protection Agency(EPA) has put the California State oil and gas
regulators: the Division of Oil, Gas and Geothermal Resources (DOGGR) on notice regarding
“serious deficiencies” in the way it has been overseeing the permitting of underground
injection wells, which may have compromised groundwater resources. Underground injection
Page 7 of 30
disposal wells are used by oil-field operators to dispose of all sorts of oil-field waste, from
drilling mud to flow back fluids from hydraulic fracturing — it all can be injected into these
wells. The rules about these injection wells are based on protections created by the Federal
Safe Drinking Water Act, which is meant to ensure that aquifers with the potential to be used
for drinking water or irrigation are protected. The EPA’s definition of an underground source of
drinking water is any aquifer with less than 10,000 parts per million (ppm) total dissolved solids
(TDS). The EPA has determined that injection wells in Ventura County have operated in aquifers
with less than 10,000 ppm, less than 3,000 ppm and aquifers with unknown amounts of TDS,
making further testing necessary. An aquifer can also be found exempt if it is determined that it
is likely to never be used as a drinking water source.
Data Mining Process (in details):
1. Data Preparation
This data was obtained from the DOGGR web page and downloaded from the following
webpage (FTP Server): ftp://ftp.consrv.ca.gov/pub/oil/new_database_format/
The following is a view of the FTP site that contains the data:
Figure 1: View of the production databases available in the DOGGR webpage.
Page 8 of 30
Please note the following regarding the data found in the DOGGR webpage.
a. We focused in all production and injection data available for the Ventura Oil Field. The
DOGGR only has electronic databases of oil production for all wells in California starting
in 1977.
b. The data is available in Zip format; therefore after downloading the data, all databases
were decompressed.
c. All databases containing production and completion data for all wells in California
were successfully downloaded.
A database linking of other databases was constructed using MS ACCESS and UNION SQL
statements were to link them together, then wells from the Ventura Oil Field were identified
and their associated oil, water, and gas production was queried. A total of 2475 oil producers
were identified since 1977 until August 2015. From the 2475 wells located in the field, a total
1063 wells have production information in the DOGGR databases.
All other data, from 1977 and older is kept in pdf files and it is available in the DOGGR files, we
didn’t considered this information and only used the information that was available in digital
format (From 1977 until 2015)
2. Data Structure
Structure of the well header data collected for all wells in the Ventura Oil Field:
Figure 2: All DOGGR databases after being downloaded and extracted
Page 9 of 30
Figure 3: View of the structure of the
well header data, unfortunately, most
of the fields were empty.
Figure 4: View of the structure of
the well header data,
unfortunately, most of the fields
were empty.
Geographical information was
also collected, latitude and
longitude.
Page 10 of 30
Structure of the production data collected from the DOGGR oil field
Before working with the data, the following was observed.
There was no injection data for any of the wells in the Ventura Oil Field;
therefore we did not include any analysis regarding this topic.
All null values in the oil production, water production, and gas production were
replaced with zeros, this is very important because adding null values to other
significant values will null the result (this is very important when calculating
gross production)
Many of the attributes were mostly empty in some cases, all empty attributes or
empty attributes were excluded from this study.
Outliers and “bad data” were eliminated.
Attributes that didn’t change among all wells and on time (Section, Township,
etc.) were not considered in the final data set used for the next step.
All relevant rates were calculated (BOPD, BOWD, BOGD) these rates were
calculated by dividing the monthly production by the number of days that a
given well was active.
Peak rates, initial production, and time to reach peak production were also
calculated.
Figure 5: View of the structure of the
well production data, no rates are
included and there were null values
assigned to production on individual
months that needed to be replaced
by zeros before calculating
cumulative production.
Page 11 of 30
3. Data Surveying
Many visualization and statistical steps were performing on the data, cumulative production
was plotted, normalizations were performed, and some data was mapped in order to
determine what data will be more relevant in this study.
Cumulative Production
A total of 1063 wells had oil production available in the DOGGR databases downloaded. After
all the data was collected, a cumulative production plot was put together, in this case, active oil
producers at each month were counted and plotted together with the total oil production. The
following is the graph obtained from this plot.
Note that the total cumulative production of the field in January 1977 was about 800 MBOE
from 700 active oil producers, and in January 2015 the production of the field was about 450
MBOE from about 420 active oil producers.
0
100
200
300
400
500
600
700
800
200,000
300,000
400,000
500,000
600,000
700,000
800,000
We
ll C
ou
nt
(Act
ive
Oil
Pro
du
cers
)
Pro
du
ctio
n (
BB
L)
Time (Months)
CUMULATIVE OIL PRODUCTION - VENTURA OIL FIELD
Total Oil Production (BBL) Well Count (Active Oil Producers)
Figure 6: Cumulative Production and Well Count per month for all wells in the
Ventura oil Filed
Page 12 of 30
There is an incremental production starting at the end of 2011, this is due to the massive
introduction of horizontal wells.
a. Normalization of Data
A macro in MS ACESS was constructed to generate normalized time. First, the data was cleaned
up so any zero production at the beginning of the life of the well was removed. The macro
iterates trough every element in the production table (About 500,000 rows) and calculates the
normalized data for all wells with recorded production (About 1060 wells)
The normalized time will start at 1 in the first month of production and will increase by one
each following month until the end of the production of the well or the last month recorded.
This is very useful to compare, for example, well lifetime and peak production time.
The following is an example of how the production of a normalized well was constructed.
Figure 7: The highlighted column represents the normalized time for the selected
well.
Page 13 of 30
b. Classification of Data
Some statistics was performed in order to further classify this data based on relevant
characteristics.
Total Cumulative Oil Production (BBL)
This is the total cumulative production of a well since the beginning of its production until the
well was abandoned or the last month with the record. The total cumulative production
indicates the production of the well in its entire life. Usually, it is important to identify what
drives high production wells.
The data was plotted and we clearly see that most of the wells have low cumulative
production. This happens because many of the new wells haven’t produced enough time to me,
therefore low cumulative production is expected.
Figure 8: Distribution of the cumulative oi production (BBL) of al wells in the Ventura Oil
Field
Page 14 of 30
The following are the statistics obtained after analyzing the cumulative production of al wells in
the Ventura Oil Field. Using the statistical properties extracted from the data, three classes
(Low Cumulative Production, Moderate Cumulative Production, and High Cumulative
Production) were assigned to every well. The 33 and 66 percentiles were used to uniformly
separate these groups as shown below.
After the classification was finished, this data was plotted using AcGIS and the maps were used
to visually evaluate the data.
From the graph, we can conclude that there is a higher concentration of high oil producers in
the in the West part of the field. Also, there is almost a uniform distribution of low, moderate
and high producers within the extension of the oil field.
MEAN 148,137
MEDIAN 122,097
MIN 2
MAX 2,028,092
WELLS 1,063
P33 46,282
P66 228,743
CUMULATIVE
PRODUCTION (BBL)
CLASS CONDITION COUNT
LOW < 46,282 351
MODERATE >= 46,282 AND < 228,743 350
HIGH >= 7668 362
Table 1: Tables showing the general statistics of the
cumulative production (BBL) of all wells together
with the given classification and respective counts
Figure 9: Map of all oil producers in the Ventura Oil field classified by cumulative oil Production
Page 15 of 30
For example, directionally drilled wells tend to have better cumulative production than no
directional drilled wells.
Peak Oil (BOPD)
This is the maximum production of the well within the first 8 months of production. Peak oil
production it usually used as the starting point when performing decline curve analysis (DCA)
The data was plotted and we clearly see that most of the wells have relatively low oil peak
production. This happens because some directionally drilled wells had high initial rates;
therefore most of the wells have relatively low peak oil production.
The following are the statistics obtained after analyzing the peak oil production of all wells in
the Ventura Oil Field. Using the statistical properties extracted from the data, three classes
(Low Peak Oil Production, Moderate Peak Oil Production, and High Peak oil Production) were
Figure 10: Distribution of peak oil production (BOPD) of al wells in the Ventura Oil Field
Page 16 of 30
assigned to every well. The 33 and 66 percentiles were used to uniformly separate these groups
as shown below.
After the classification was finished, this data was plotted using AcGIS and the maps were used
to visually evaluate the data.
Note that most of the high producers plotted in the map above have also high peak oil
production.
RAMP TIME
This is the time (Normalized Time) in which the well reach its peak production. This is usually
used as the initial time when calculating decline rates in decline curve analysis (DCA), usually, a
high producer with high peak oil times will have short ramp times.
The data was plotted and we clearly see that most of the wells have relatively low oil peak
production. This happens because most of the wells also had low peak oil production.
MEAN 845
MEDIAN 740
MIN 1
MAX 14,780
WELLS 1,107
p33 442
P66 1,308
PEAK OIL (POPD)
CLASS CONDITION COUNT
LOW <441.98 365
MODERATE >= 441.98 AND < 1307.88 365
HIGH >= 1307.88 377
Table 2: Tables showing the general statistics of the
peak oil production (BOPD) of all wells together
with the given classification and respective counts
Figure 11: Map of all oil producers in the Ventura Oil Field classified by peak oil production
Page 17 of 30
The following are the statistics obtained after analyzing the ramp time in normalized months of
all wells in the Ventura Oil Field. Using the statistical properties extracted from the data, three
classes (Low Ramp Time, Moderate Ramp Time, and High Ramp Time) were assigned to every
well. The 33 and 66 percentiles were used to uniformly separate these groups as shown below.
MEAN 4
MEDIAN 3
MIN 1
MAX 8
WELLS 1,107
p33 2
P66 5
RAMP TIME
(Months)
CLASS CONDITION COUNT
LOW < 2 258
MODERATE >= 2 AND < 5 442
HIGH >= 5 407
Table 3: Tables showing the general statistics of the
ramp time (Normalized time in months) of all wells
together with the given classification and
respective counts
Figure 12: Distribution of the ramp time (normalized data in months) of al wells in the Ventura Oil
Field
Page 18 of 30
Finally, using the classification described above, geographical locations, well type, and
normalized time, normalized production graphs were generated.
Data Modeling
Data Preprocessing
Data collected from the data preparation stage was used as inputs to build the unsupervised
descriptive model. Attributes with non-changing instances were removed while attributes with
varied instances were kept to reduce data dimension and irrelevant attributes. Nominal and
Numeric attribute types were defined and separated. Well identification, well status, and
descriptive attributes were defined as nominal. Production, time, depth, pressure, and
hydrocarbon properties were considered as numeric attributes. Figure 14 below shows the 50
input attributes.
0
10
20
30
40
50
60
70
80
90
100
1 6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
10
1
10
6
11
1
11
6
Oil
Pro
du
ctio
n (
BO
PD
)
Nomralized Time (Months)
Normalized Oil Produciton
Figure 13: Normalized production for the first 10 years of all oil producers with low Ramp Time, low
Class peak oil, and Low Total production.
Page 19 of 30
Figure 14: Input Attributes
The class attribute was chosen to be the total oil production descriptive class of low, moderate,
and high. Because the numeric attributes vary based on its measured units, normalization was
necessary for data mining comparison between attributes. Normalization with a scale from zero
to one was implemented.
Sampling and Modeling
To test the quality of the data, 80 percent of the entire data set was taken for training and 20
percent was taken for testing. A comparison of the output by the trained model was done on
both the training data set and the testing data set. The sampling was done with Weka
supervised resample filter. To create a descriptive model, Simple K-means was considered, the
model consists of 3 sub-models with 3 clusters, 5 clusters, and 10 clusters. Overfitting was
tested with the 3 sub model in ascending order for consistency of the data. At high number of
clusters, the data set can confuse noisy data for real data or missing data. Therefore, a creation
of 3 sub-models is necessary.
Page 20 of 30
Result
A comparison between the 80 percent cluster output and the 20 percent cluster output
resulted in a perfect match. The training set contains the same percentage of instances
clustered as the testing set. This indicates that the sampled data sets were representative of
one another. Figure 15, 16, and 17 below shows the results from cluster 3, 5, and 10 between
the evaluated training set and testing set.
Figure 15: 3 Cluster Model
Figure 16: 5 Cluster Model
Figure 17:10 Cluster Model
3 Cluster Model
The 3 cluster model shows areas of moderate production and areas of high production from
horizontal and vertical wells. It takes 11 iterations and a cluster sum of squared errors of
1,969,764 to build the model. The result can be seen below in Figure 18 and the normalized
well locations can be seen in Figure 19.
Training Set Testing Set
Training Set Testing Set
Training Set Testing Set
Page 22 of 30
Figure 19: Normalized Well Locations
The blue represent cluster 0, red (cluster 1), and green (cluster 2). The clustering shows the
wells on the west side of the field exhibit high oil peak and production from horizontally drilled
wells (cluster 2). Cluster 2 also extends to the south-central of the field; however it is also mixed
together with vertical well clusters. The wells on the east and central of the field exhibit
moderate oil peak and production and are vertically drilled (cluster 0 and cluster 1
respectively).
5 Cluster Model
The 5 cluster model is similar to the 3 cluster model, however directionally drilled wells are split
into two categories; one with a high-class ramp time and one with a low-class ramp time. The
vertically drilled wells are separated into moderate oil peak and production and low oil peak
and production. It takes 32 iterations and a cluster sum of squared errors of 1,830,613 to build
the model. The result can be seen below in Figure 20 and the normalized well locations can be
seen in Figure 21.
Page 24 of 30
The blue represent cluster 0, red (cluster 1), green (cluster 2), teal (cluster 3), and pink (cluster
4). The two horizontally drilled well clusters exhibit high oil peak and production, wells on the
west side (cluster 2) exhibit high ramp time while south central (cluster 3) wells have moderate
ramp time. The vertically drilled wells on the east side (cluster 4) of the field have low oil peak
and production while the wells at the center (cluster 0 and cluster 1) of the field have moderate
oil peak and production. All of the vertical wells have moderate ramp time.
10 Cluster Model
The 10 cluster model clusters the horizontal and vertical wells into clusters with low, moderate,
and high production scattered across the field. Some clusters show signs of over fitting and can
be disregarded from the data set. It takes 29 iterations and a cluster sum of squared errors of
1,681,103 to build the model. The result can be seen below in Figure 22 and the normalized
well locations can be seen in Figure 23.
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Figure 23: Normalized Well Location
The blue represent cluster 0, red (cluster 1), green (cluster 2), teal (cluster 3), pink (cluster 4),
purple (cluster 5), orange (cluster 6), dark red (cluster 7), yellow (cluster 8), and black (cluster
9). The result shows 3 different clusters of horizontally drilled wells with high oil peak and
production, the west side of the field have high ramp time (cluster 2) or moderate ramp time
(cluster 8) and wells in south-central (cluster 3) with moderate ramp time. There is a cluster of
horizontal wells scattered around the east and central of the field that shows low peak and
production (cluster 9). They accumulate mainly in the east-south central of the field and
account for 8 percent of the testing instances. This inconsistency in the data set might be an
indication of overfitting due to its small size. This could also be an indication of noise.
Vertical wells in the east side (cluster 4) of the field have low oil peak and production with
moderate ramp time. While the southwest central (cluster 0) and southeast (cluster 6) wells
have moderate oil peak and production with high ramp time. Vertical wells in the north-east
central (cluster 1) have moderate oil peak and production while wells in the direct center
(cluster 5) have high oil production with moderate oil peak. There is a cluster of vertical wells
scattered around the west and central of the field that have high oil peak and production with
Page 27 of 30
low ramp time. They accumulate mainly in the northwest of the field and account for 7 percent
of the test instances. This inconsistency in the data set might also be an indication of overfitting
due to its small size, and could be an indication of noise.
Conclusion:
The field currently has mostly vertical wells that cover around 60% of the total number of wells
drilled. If new horizontal wells were drilled or over existing vertical wells in the west or south-
central of the field, according to the results extracted from the clustering model, the wells will
likely exhibit high oil peak and production with either high or moderate ramp time. If vertical
wells were drilled in the east side of the field, it is most likely going to have low oil peak and
production. However, there is not enough information on horizontal wells in this area;
therefore production of horizontal wells drilled in this area cannot be evaluated. This might be
due to the restraint of obtaining horizontal drilling permits or that the low production may
dissuade horizontal drilling.
If vertical wells were drilled in the center of the field, it is most likely going to have moderate oil
peak and production with moderate ramp time, however, there is also a chance that the field
will output high oil production with moderate peak and ramp time.
Implementation of This Project:
Since the recommendation of our study is encouraging the use of successful horizontal wells,
below is a real life example of horizontal well successfully being used in Ventura County:
Tri-Valley Corp. has drilled seven horizontal wells in its Oxnard fields to get at the heavy oil
there. These wells are drilled down vertically and over horizontally. It then pumps steam into
those wells and pumps oil out. Using SAGD means it will drill another horizontal well above the
existing well. This "injection well" will be used to pump in steam. The steam reduces the
viscosity of the oil so it becomes thinner and moves into the lower well. The heated oil and
water is then pumped to the surface and separated, with the water being cleaned and reused
for a new steam generation. These types of wells can get up to 60 percent of oil from a deposit.
Tri-Valley Corp. is using a steam injection process to wring thick, tar-like oil out of the sands of
Page 28 of 30
its Pleasant Valley field. The Oxnard property yielded 333 barrels of oil a day in May, up from
both March and April, (with March being the previous peak production at 256 barrels).
For successful implementation of this project in real life scenario, it will require an adequate
amount of data for accurate production forecasting so that the training model can be made
more efficient. Also, the field personnel needs to be trained in using software such as WEKA ,
VBA etc. Also, data with less error and noise is preferred. The main advantage of this project
methodology is that it can be cost effective for small fields too. This is because it requires less
development time and the data requirements are flexible as it can be continuously improved as
we accumulate data.
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References:
1. California Oil and Gas Fields, Volumes I, II and III. Vol. I (1998), Vol. II (1992), Vol. III
(1982). California Department of Conservation, Division of Oil, Gas, and Geothermal
Resources (DOGGR). 1,472 pp. Ventura Oil Field information pp. 572-574. PDF file
available on CD from www.consrv.ca.gov.
2. California Department of Conservation, Oil and Gas Statistics, Annual Report, December
31, 2006.
3. DOGGR Website