Get documents about "Interactive Information System For Online Processing Geo-Technological Data (GTD) Sinking Wells
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 5, May 2011
Interactive Information System for online processing geo-
technological data (GTD) sinking wells
Information Systems
Safarini Osama
IT Department
University of Tabuk,
Tabuk, KSA
usama.safarini@gmail.com
osafarini@ut.edu.sa
Abstract—: Online management of drilling requires the choice of - Obtaining of preliminary “integral” well logging
an informed decision of many possible because of the volume of curve and its segmentation.
incoming and processed GTD, problem arising in the functioning
through management situations. The importance here is the From measures of similarity (see Table 1) is selected “distance
information management process to enable effective man- indices” similar to a distance by Hamming and Euclid as the
machine decision. So the purpose of work is to Develop a
most widespread [2]. The features that describe distance
methodology, algorithm and program for processing
(compression and classification) GTD sinking wells, confirming indices in this case will be an amplitude and depth, while
the geological GTD, for example, marks mining drill bits; measures of similarity – their functions or as analogs of a
distance by Hamming or Euclid:
- A product of a module of difference of
Keywords- Man-machine decision, Geo-technological data, amplitudes;
classification, compression, correlation, measures of similarity, - A product of a module of amplitude difference
marks mining drill bits, data mining geology, Information by a difference of depths;
Component, Euclidean and Hamming Distance. - A product of a module of amplitude difference
by a square of differences of depths.
I. INTRODUCTION
In these segmentation methods a number of segments,
The Work describes methods and means of information measures and functions applied here using the program shown
system software for decision-making by the results of geo- in (Fig .1) can be varied with a possibility to present areas of
technological data (GTD) on bore-hole drilling, compression segments, their models specifying borders, intersections, etc.
of GTD on a drilling regime, improvement of interactivity of very close to that which is now assumed for processing of
GTD processing and online management process of drilling, vague sets as the measures of similarity of objects, classes are
algorithms of GTD segmentation, a program product, results the values of the function that belongs to [3].
of GTD processing.
II. DISCUSSION
While classifying GTD, the process of segmentation aimed at
taking On-line decisions in drilling, a forecast of the beginning
or end of an interval, following and prediction of a working
period of a drilling bit, evaluation of wear of drilling tools,
prevention of emergency situations, breaking of equipment
and others [1].
The results of the proposed segmentation provide us a
geological situation through a well depth. The proposed
methods assume interactive interpretation of segmentation and
compression of GTD and a possibility of additional verifying
repetitions and variations. This is connected with division of
GTD into segments, their verification by the identified models
applying two, essentially different methods:
- Separately for each well logging curve with their Fig.1 Program Interface for classification into classes
subsequent superposing for final segmentation;
106 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 5, May 2011
presented as a much simpler function with the same
Classification by various measures of similarity Table 1 characteristics as the original sample.
Formula of a similarity
measure Division of information components
Class №1 Class №2 Class №3
• Classification of each geo-measured data properties.
1) RIJ
,
1,3,4,5,6,7,8 11 2,9,10,12,13,14
RI + RJ − RIJ
1 Rij 5,3,4,6,7,8,9,11 14,13 2,1,10,12 1.000
2) RIJ +
r
r + 1 − ( RIJ + Rij )
-0.514 1.000
0.315
3)
RIJ 1,3,4,5,6,7,8 11 2,9,10,12,13,14 -0.336 -0.135 1.000
RIJ + RIj + RiJ -0.498 -0.483
-0.573 -0.117 0.651 1.000
4)
2 RIJ 1,3,4,5,6,7,8 11 2,9,10,12,13,14 -0.554 -0.593 0.789
2 RIJ + RIj + RiJ
0.435 0.288 -0.716 -0.924 1.000
0.504 0.608 -0.746 -0.847
RIJ + Rij 5,3,4,6,7,8 1 14,2,9,10,11,12
5) 0.627 0.175 -0.575 -0.805 0.848 1.000
r + RIj + RiJ
,13 0.524 0.517 -0.473 -0.558 0.543
1
5,3,4,6 14,9,10,11,13 1,2,7,8,12
S
h
6) dij = ∑Ck (xki − xkj )h
k=1 Table 2.Correlation of parameters with / without separating into
1 layers
Cij =
1 + d ij
7) ∑x
k
ik − x jk 3,1,4,5 14,2,10,12 13,6,7,8,9,11
Average Test 1 Test 2 Test 3 Test 4 Test 5 Test 6
8) ∑k
xik − x jk 3,1,2,4,5 14,9,10,11,12 13,6,7,8 Layer 1 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Layer 2 0.289 -0.027 -0.585 -0.210 0.425 -0.243 -0.246
∑ (x
k
ik − x i )(x jk − x j ) 5,4,6,7,8,9,11 14,2,10,12,13 3,1 Layer 3 0.169 -0.037 0.171 0.093 -0.131 -0.195 -0.386
9) Layer 4 0.193 -0.030 -0.302 0.621 0.007 -0.045 0.152
σ iσ j
Layer 5 0.274 -0.139 0.928 -0.109 0.416 0.000 -0.053
r−s s~ 5,3,4,6,7,8 14,2,9,10,11,12, 1
10) C ij + Cij Layer 6 0.233 0.217 -0.911 0.015 -0.253 0.000 0.001
r r 13 Layer 7 0.283 -0.348 0.911 -0.015 0.253 -0.165 -0.006
Layer 8 0.284 0.286 -0.827 -0.123 -0.374 -0.026 -0.066
Layer 9 0.262 -0.110 0.056 0.518 0.571 -0.313 -0.006
Layer 10 0.073 -0.043 -0.003 -0.043 0.017 0.075 0.256
Table 1.Classification by various measures of similarity Layer 11 0.123 0.036 -0.227 -0.362 0.005 -0.087 0.024
Layer 12 0.326 -0.806 0.791 0.069 0.064 0.071 0.158
GTD Processing in two stages
- In the first stage compression and classification of GTD for Table 3.Correlation with the marks of mining bits
each of the measurements. The results are a set of features for
the second phase.
- At the second stage, the final classification on the full range of
GTD, this allows assessing the correlation with marks of bits, III. CONCLUSION
or data mining geology.
The developed information System is an instrument for
Data Compression decision-making in complicated multi-factor non-formalized
cybernetic systems with a feedback, i.e.:
Stage data compression involves the following steps:
• in interactive assessment of informational significance
• Calculate the autocorrelation function Kxx for every of drilling factors provided by readings of on-land
geo-measured properties curve. facilities, telemetric and feedback data [4];
• Determine the Tk - correlation interval for each • support of processing (compression and
sample. It is determined based on type of classification) of well sinking results verifying
autocorrelation function. geological prospecting data, for instance, on a mark
of drilling bit run;
• Approximation of each sample geo-measured
properties sampling interval in depth equal to the Tk. • Developed are algorithms and programs for
In this case geo-measured data properties are segmentation of GTD with a possibility of an
interactive assessment of a segmentation quality,
107 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 5, May 2011
variation of a number of segments,
representativeness, correlation to a geological profile,
borders of formations, wear of drilling bits, and
prevention of emergency situations.
AUTHOR’S PROFILE
• Application of MS Excel for estimation of segments
of GTD on a drilling regime; Dr. Safarini Osama Ahmad Salim had finished
his PhD. from The Russian State University of
• As seen from Table 2, the correlation shows better Oil and Gaz Named after J. M. Gudkin,
Moscow, 2000, at Computerized-Control
results when separating the well profile into layers; Systems Department. He was awarded by his
this reflects the fact of geology changing properties. participation in Interpretation of measurement
data in gas wells, Abstracts of paper presented to
• As seen from Table 3, Correlation with the marks of the third All- Russia Conference of young
scientists, specialists and Students on the
mining bit confirms the changes in geo-measured problems in gas industry in Russia “New technologies in the gas
data properties or as different layers. industry”, Moscow 1999, 28-30 September.
He obtained his BSC and MSC in Engineering and Computing Science from
Odessa Polytechnic National State University in Ukraine 1996. He worked in
REFERENCES different countries and universities. His research is concentrated on
Automation in different branches Specially Oil and Gaz.
[1] Levitzky A.Z., Komandrovsky V. G., Safarini Osama
Methods and Means to Develop an Information System for On-Line
Control of Drilling, Scientific-Technical Research Journal, “Automation
Telemetry and Communication in the Oil Industry”, N 3 2000, PP 7-11.
[2] Safarini Osama
"Enhanced Decision-Making Computer-Aided Methods for On-Line
Control of Well Drilling", Abstracts of paper of the IPSI Conference Held
in Carcassonne, France, UNESCO Heritage, April 27 to 30, 2006.
[3] Levitzky A.Z., Komandrovsky V. G., Safarini Osama
On Automation for On-Line Control of Well Drilling, Scientific-Technical
Research Journal, “Automation Telemetry and Communication in the Oil
Industry”, N 3-4 1999, PP 2-8.
[4] Komandrovsky V. G., Safarini Osama
On classification of information components of On-Line control of a
drilling Process, Abstract of paper of the Third Scientific Technical
Conference, “Urgent Issues of the Condition and Development of the Oil
and Gas Complex in Russia”, Moscow, 1999 27-29 Jan.
108 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
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