Journal of Engineering Geology

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In this research, it is attempted to develop a new classification system for evaluating the rock sawability with respect to affective and major parameters. In this new classification system, four major characteristics of rock are selected for evaluating the rock sawability. In total, each rock takes a new score from 10 to 100 and classified into five classes: very poor, poor, fair, good and very good by new classification system. The new calculated rock sawability index (RSi) can be use as a useful index for evaluating the rock sawability. In the present paper, the relationship between ampere consumption, RSi and machine parameters are investigated by multiple regression. For this propose, 12 stones are tested by new sawing machine under different machining conditions (different depth of cut and feed rate). The results of this step are used as input data in SPSS software. Finally, two predicted models are presented with respect to machining parameters and RSi. These new models in stone factories can give a good viewpoint of energy consumption

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Abstract (Paper pages 1179-1194) The site under study is located in the south of municipality-13, east of Tehran. Numerous building construction activities and large investment have been done in this area. Hence, it is important to have a good knowledge of the site characteristics. Soil classification is a very effective tool for optimum engineering construction which may reduce the future earthquake hazards. Building codes such as standard No. 2800, UBC, IBC and Eurocode 8 were used for soil classification. Seismic and geotechnical data were collected. Based on the considered Building codes the average seismic velocity and SPT values were estimated. It was concluded that Piroozi Street can be grouped into II, SC, C and B classes.

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Marly rocks of Abtalkh formation were classified by Q, RMR, RSR and RQD rock mass classification systems using 222 meters logs from exploratory boreholes in Doosti dam site. The results show that the RMR is the most suitable method for classification of studied rock masses and has highest correlation coefficient with RQD. The validity of different Q-RMR equations was studied using error ratio (ER). Cameron et al. (1981) and Morno (1982) equations have lowest ER and highest validity for studied marlstones. Bieniawski (1989) and Cameron (1981) relationships are lower and higher limits of equations for marly rocks respectively. 

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Soil classification is one of the major parts of geotechnical studies. So assessment of existing methods for soil classification in different areas is important. For soil classification is used in situ and laboratory test results. Sampling and identification tests are laboratory methods for soil classification. CPTu test is in situ method for soil identification and classification, due to accuracy and speed, this test is used widely in geotechnical study today. Many researchers are proposed some charts for soil classifications based on the parameters measured in CPTu test. In this paper for evaluation the performance of these methods, 58 CPTu test results have been used. These tests are related to four areas in southern Iran. The soils are classified by CPTu methods and then they are compared with 372 laboratory soil classification. Research results show the chart proposed by Robertson (1990) which based on Qt, Ft and Bq variables has the best adaptation with the laboratory soil classification in these studied areas. Then according to data obtained from research, proposed a modified charts based on Rf, qt-u0/σ΄ v , that show 90% adaptation with laboratory soil classification.

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Introduction
Geotechnical investigations merely through boring and engineering experiments are considered a difficult task as they are highly costly and time-consuming. The identification of large areas initially requires geological studies followed by the inclusion of geotechnical information. Finally, a geological and geotechnical classification is prepared for the entire area. This type of classifications is employed in strategic urban planning and quick selection of geotechnical variables in small-scale projects. The present research performed the steps involved in these investigations and classifications for the city of Sanandaj, Iran. Hence, the geological-geotechnical classification of the city of Sanandaj was presented by integrating the geological information of this city with the geotechnical data obtained from drilled boreholes as well as multiple wells at different locations in this city.
Materials and Methods
This study was conducted on the city of Sanandaj in six steps. The steps involved and their respective objectives are given in summary in Table 1.
Discussion
This study is applicable to those regions with insufficient information on their boreholes. The present study used only 211 boreholes, the distance
Table1. Steps involved in this study

Objective or result	Title	Step
Identifying the general geological characteristics	General geological investigation of the considered region	1
Determining the rock units and soil layers as well as their outcrops and investigating their appearance	Determining the appearance of the layers through field investigations	2
Determining the layer types and drawing the longitudinal and lateral profiles	Identifying subsurface layers	3
Determining the characteristics of geological units and their origin of emergence	Geological classification based on the steps involved in formation of units	4
a)Collecting the available information, b) controlling the available information, c) completing the information	Determining the geotechnical attributes of geological units	5
a) Presenting geological-geotechnical classification, b) presenting geological identification criteria to determine the type of a given unit at the site of the project	Presenting a geological-geotechnical classification for the considered region	6

between which was greater than 5 km in some areas of the Sanandaj city. Hence, although no sufficient information was available on many areas of Sanandaj, the proposed method in this study was able to identify the geotechnical attributes of all soil layers and rock units. This study emphasizes on geological and geotechnical classification and presents a step-by-step method to systematically relate geological and geotechnical studies. By integrating these classifications, geotechnical identification of extensive regions such as urban areas can be facilitated even if the number of boreholes is insufficient. Moreover, simple identification criteria can be extracted from this method, through which the engineering attributes of the layers at each point can be determined. This method can be used as an optimal and economical method for geotechnical identification of extensive areas.
Conclusion
The following summaries can be concluded from this study:
-The step-by-step procedure of integrating geological and geotechnical information was described, through which the geological-geotechnical classification for this city was obtained.
-The geological units identified for Sanandaj were shale, limestone, andesite, and Quaternary, which includes layers of alluvial clay, residual clay, and sand and gravel. The extent and distribution of each of the aforementioned units in Sanandaj were identified and plotted. Moreover, the physical and mechanical characteristics of each of the units as well as their geotechnical hazards were determined and presented.
-In this study, simple geotechnical criteria such as faults, altitude level, and distance from river were identified. These parameters were effective in identification of geological units in Sanandaj../files/site1/files/131/5Extended_Abstract.pdf
 

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Introduction
Drill-bit selection is one of the most important aspects of well planning due to the bearing it can have on the overall cost of the well. Bit selection in conventional and slightly inclined wells is a very delicate and complex process. In high angle and horizontal wells it is even more difficult. Historically, drilling engineers have selected bits on the basis of what has been worked well in the area and what has been determined to have the lowest cost run from offset bit records. Often the best bit records were not available for evaluation, because the best bit may not yet have been run, may have been run by a competitor or the engineer was new to the area. As a result the bit program was generally developed by trial and error and at significant additional costs for a large number of wells. In most cases the optimum program was never reached because there was nothing to predict that a bit selection change could further reduce the cost of the well. In this study, an alternative solution approaches using the concept of the power of data mining algorithms to solve the optimum bit program for a given field is proposed.
Material and methods
It has been considered an offset well to be drilled outside the known boundaries of a known field. For this purpose, the seventh well (X-7) of the same field was used as a verification point. The data was trained using the well log and rock bit data of six wells located in the field and the real well log data of well 7 was input as unknown data. These depths are selected based on reported rock bit program. When compared to the real data, it could be observed that the models (adaptive neuro fuzzy inference system, K-nearest neighbors, decision tree, Bayesian classification theory and association rules) estimates the formation hardness accurately. This minor discrepancy was also present with the company’s suggested rock bit program, which was based on the previous wells’ rock bit data.
Results and discussion
In this paper, data mining algorithms for optimum rock bit program estimation is proposed. The accuracy and efficiency of the developed data mining algorithms (adaptive neuro fuzzy inference system, K-nearest neighbors, decision tree, Bayesian classification theory and association rules) that requires sonic and neutron log data input was tested for several real and synthetic cases. In the case of a development? well to be drilled outside the known boundaries of a field the model estimated rock bits with properties that consider the formation hardness correctly but slightly underestimated further rock bit details. The models also produced reasonable rock bit programs for an advance well to be drilled within the known boundaries of a field and a wildcat well drilled in a nearby field with similar rock properties to the training field. Thus it was concluded that the developed adaptive neuro fuzzy inference system is suitable as a front-end system for rock bit selection that could help engineers in decision-making analysis.
Conclusion
Optimum bit selection is one of the important issues in drilling engineering. Usually, optimum bit selection is determined by the lowest cost per foot and is a function of bit cost and performance as well as penetration rate. Conventional optimum rock bit selection program involves development of computer programs created from mathematical models along with information from previously drilled wells in the same area. Based on the data gathered on a daily basis for each well drilled, the optimum drilling program may be modified and revised as unexpected problems arose. The approaches in this study uses the power of data mining algorithms to solve the optimum bit selection problem. In order to achieve this goal, adaptive neuro fuzzy inference system, K-nearest neighbors, decision tree, Bayesian classification theory and association rules were developed by training the models using real rock bit data for several wells in a carbonated field. The training of the basic models involved use of both gamma ray and sonic log data. After that the models were tested using various drilling scenarios in different lithologic units. It was observed that the adaptive neuro fuzzy inference system model has provided satisfactory results.
 
 

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