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Artificial Foreteller of Oil and Mineral Deposits


 

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 A
1&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.

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.

 

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.

 

 

Interested parties can contact us at:e-mail

 

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