| The
keystone to success in oil prospecting or in mineral exploration
is to keep cost on exploration and development in check. Today
there exist many different approaches to predict if oil or mineral
deposit is in the area or not. All these ways are directed on
diminution quantity of wildcat wells that should be drilled for
investigation of the area with oil or mineral deposit. Oil prospectors
use geologic and geophysical methods, subtle changes within the
Earth's magnetic and gravitational fields, stratigraphy, super-sensitive
optical nose, and others to increase the probability of exploration
success. Subsistence so many different ways that were used for
identification of oil or mineral deposits lead us to the conclusion
that among mentioned above ways of oil prospect there is no reliable
way to define deposit. Each of those ways have its strong and
weak sides. We have strong believe that combination of different
ways of oil and mineral prospecting can be quite efficient. However
it is quite difficult to implement this approach because of its
complexity and dimension.
We
propose way that overcomes dimension and complexity problem. It
is based on associative self-building neural network that can
effectively extract regularities from huge amount of data. Computer
processing power and available memory size are only two factors
that restrict network. That means user can train Artificial Foreteller
on a set of known oil deposit descriptions. Each description of
deposit can contain as many features as an oil prospector has
or want to use.
Features can be represented as a set of codes like
Feature
A - A1, A2, ... , An.,where Feature
A is a range of values, and A1, A2, ... , An
- set of sub ranges of values.
Feature
B - B1, B2, ... , Bm.
........
Feature
Z - Z1, Z2, ... , Zs.
Based
on this set of features user can describe each case of oil deposit
as a sequence of codes:
Case 1 - A1,B2, ... , Zm.
Case 2 - A9, Bw, ... , Zr.
........
Case
m - Ak, B6, ... , Zl If
there is history data about oil deposits and those areas where
oil was not found then it is possible to generate train set of
examples. A set of cases can be represented as following:
Train
Case 1 - A1, B2, ... , Zm. - positive
Train Case 2 - A9, Bw, ... , Zr.
- positive
........
Train Case m - Ak, B6, ... , Zl - negative,
- where "positive" means that the case describes area
where oil deposit was found, and "negative" means that
oil deposit was not found.
A set of cases is used for train and generating of associative
self-building neural network. During train Artificial Foreteller
processes the set and extracts combinations of values of different
features that are typical for positive or negative train cases.
Let's see how Artificial Foreteller processes a train set that
is shown below.
These
cases are represented in neural network with corresponding nodes
and connections. It will form four main nodes that represent two
positive, and two negative cases. At the same time it will generate
three new nodes that are common for more than one case. One of
them ( A1&B2&C5&G11&K13
) belongs to two positive cases, one (A10&B21)
- belongs to two negative cases, and one node (D11&E13&G11)
belongs both positive and negative cases (Case2, Case3). As a
whole, generated network can be represented like sum of products
and dispersions :
((A1
& B2 & C5 & G11 & K13)
& D1 & E23 & F43 &
L7 & M8 & N22 & O11
)OR((A1 & B2 & C5
& G11 & K13) &
(D11 & E13 & G11)
& F33 & L8 & M19
& N21 & O17)OR
NOT((A10 & B21)
& C15 & (D11 &
E13 & G11) &
F23 & K18 & L5 & M14
& N11 & O2)OR
NOT((A10 & B21)
& C14 & D8 & E11 &
F3 & G1 & K19 & L6
& M4 & N1 & O4)
Associative
self-building neural network is not a "black box". The
formula given above can be analyzed to separate most important
features from minor ones. It can be used as a powerful tool for
data analysis directed on selecting most representative features.
In
example shown above the combination of features A1&B2&C5&G11&K13
is common for two cases that describe deposits where oil was discovered.
Combination of features A10&B21 is common
for two cases that describe areas where oil was not found. And
combination of features D11&E13&G11
is common for two cases that belong both positive and negative
descriptions. When Artificial Foreteller predicts situation it
assesses activated nodes and if activated node represents combination
of features A1&B2&C5&G11&K13
then its prognosis is positive. If a node that represents combination
of feature's values A10&B21 is activated
then Artificial Foreteller predicts that the area has no oil.
In case when a node D11&E13&G11
is activated Artificial Foreteller can give only assessment of
the situation. It can predict that the area can contain or not
oil deposits with % probability.
User,
who trains the system, can analyze network and decide if it necessary
to keep features D, E, and G, or remove only
sub ranges D11, E13, and G11. After
that Artificial Foreteller can be trained again user can check
if its prediction feature becomes better or not. This way user
can select most important and representative features that describe
area with oil or mineral deposits and remove less important ones.
With help of generated neural network Artificial Foreteller will
predict or recognize area with oil deposits. Artificial Foreteller
can have as many network as many problems it should solved. That
means user can train Artificial Foreteller on separate criterion
like subtle changes within the Earth's magnetic and gravitational
fields or geophysical methods, or other.
Artificial Foreteller Structure
Initially
network structure consists of two layers. The lowest layer is
a set of sensors. The second layer consists of receptors connected
to the sensors.
Each
feature that describes condition of the area where might be
oil corresponds to a sensor. Sensors are divided into two types
- qualitative and quantitative. Qualitative sensor transfers
only signals "YES"/"NO" to the receptor
that is connected with the sensor. Signal "YES" activates
the receptor. Quantitative sensor is connected with a set of
receptors. Range of values that sensor receives is divided into
a set of sub ranges. Current version of preprocessor can form
from 3 up to 50 sub ranges. Accordingly, quantitative sensor
can combine up to 50 receptors. In that case receptor becomes
activated only if the sensor sends a signal that value corresponds
to a sub range of values that was confronted to the receptor.
Nodes of associative self-building neural network are formed
by network builder only during training sessions.
During
train neural network is formed. In its structure all derived
regularities (or combination of features) are represented with
nodes and their weights. In a mode of oil deposit prediction,
entered into Artificial Foreteller description of the area where
oil might be activates part of the neural network. Activated
part of the neural network sends signals about activation into
Artificial Foreteller of Oil or Mineral Deposit where they are
assessed. As a result, Artificial Foreteller of Oil or Mineral
Deposit issues prediction if the area contains oil or not.
 Associative
self-building neural network can be used not only for oil or
mineral prediction, but for classification and data analysis.
Analysis of network structure is quite important to derive most
significant features. Associative self-building neural network
works over and accumulates vast amount of information about
oil areas.
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Artificial
Foreteller is a new powerful system that demonstrates extraordinary
ease of use for prediction, and classification situations.
Artificial Foreteller analyses current input values and
predicts situation with oil or mineral deposits. As it well
known human being can operate with not more than 7 criteria
at the same time. As distinct from human being Artificial
Foreteller is able to operate as many different criteria
and features as it has been trained.
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Artificial
Foreteller can "remember" thousands of oil deposit
descriptions in its neural network and use them for analysis
of the current one. It forms prediction based on derived
from train data set of regularities and analogies among
analyzed and known situations.
Artificial
Foreteller was written in VC++ and works under MS Windows98/me/NT/2000/XP.
A virtual memory storage was developed to store neural network
and let the system to create network with size more than size
of PC central memory. With help of virtual memory storage
neural network became portable and can be transferred among
different PCs.
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Interested
parties can contact us at:e-mail
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