<em>Permeability is one of the important of reservoir characteristics, but is difficult to predict it. The accurate permeability values can be obtained from core data analysis, but it is not possible to do at all of the well intervals in the field. This study used 191 sandstone core samples from the Upper Cibulakan Formation in the North West Java Basin. The concept of HFU (Hydraulic Flow Unit) developed by Kozeny-Carman is used to generate the relationship between porosity and permeability for each rock type. Afterward, to estimate the permeability value at uncored intervals, the statistical methods of artificial neural network based on log data are used on G-19 Well, G Field which is located in the North West Java Basin. Based on core data analysis from this research, the reservoir consists of eight HFU with different equations to estimate permeability for each HFU. From this reserarch, the results of permeability calculations at uncored intervals are not much different from the core data at the same depth. Therefore the approach of permeability prediction can be used to determine the value of permeability without performing core data analysis so that it can save the company expenses.</em>

Permeability is an important reservoir property but it is difficult to predict. An accurate measurement of permeability values can be obtained from core data analysis. However, this analysis is not possible to do at all interval wells in the field, so that permeability information becomes incomplete. Then, the use of artificial neural network method can be an alternative to predict the incomplete permeability values. This study used 191 of sandstone core samples from Upper Cibulakan Formation in the North West Java Basin. These core data were used to determine hydraulic flow unit (HFU) from the reservoir, and to obtain a relationship between porosity and permeability for each HFU. The application of artificial neural network method is done by building a database of flow zone indicator (FZI) based on its relationship with log data. From this FZI value, the HFU class can be known. Afterward, the permeability value can be obtained according to the equation of the relationship between porosity and permeability at each HFU that had been generated. Artificial neural network was applied on G-19 and G-11 Well that had 51 of core data. Based on this study, the result of permeability value is not much different from core data at the same depth, so that this method can be applied to obtain the permeability in uncored intervals.

<p>Lost Circulation Materials (LCM) are specially designed not to damage the penetrating formation during handling of loss circulation problems and are very effective for drilling operations worldwide. Optimization of LCM composition may stop loss circulation effectively and protect the production zone from the invasion of mud filtrate. The concentration of lost circulation  materials (LCM) is a key parameter to determine the effectiveness of LCM. In this study, laboratory equipment such as the Hamilton beech mixer, Fann VG meter and API filter press are used to evaluate the effectiveness of various LCMs in dealing with loss circulation. In this research, coconut fibre, banana tree skin, and bagasse are used as LCM in various concentrations. The mud losses were simulated using an 80 mesh shaker. The quality of the muddy rheological properties was<br />the basic parameters to be evaluated. The test was carried out at 80oF and 200oF. The experimental results show that bagasse has the best performance both at 80oF and 200oF as LCM compared withcoconut fibres and banana trunk. The lost circulation of  mud filtrate at 80oF and 200oF due to the addition of 2 gram bagasse is 34 ml and 40 ml, respectively.</p>

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<p>Efforts are made to find the remaining hydrocarbons in the reservoir, requiring several methods to calculate the parameters of reservoir rock characteristics. For this reason, logging and core data are required. The purpose of this research is to estimate the Remaining Hydrocarbon Saturation that can be obtained from log data and core data. With several methods used, can determine petrophysical parameters such as rock resistivity, shale volume, effective porosity, formation water resistivity, mudfiltrate resistivity and rock resistivity in the flushed zone (Rxo) and rock resistivity in the Uninvaded Zone which will then be used to calculate the Water Saturation value Formation (Sw) and Mudfiltrat Saturation. (Sxo) In this study four exploratory wells were analyzed. Shale volume is calculated using data from Gamma Ray Log while effective Porosity is corrected for shale volume. Rw value obtained from the Pickett Plot Method is 0.5 μm. The average water saturation by Simandoux Method were 33.6%, 43.4%, 67.0% and 39.7% respectively in GW-1, GW-2, GW-3 and GW-4 wells. While the average water saturation value by the Indonesian Method were 43.9%, 48.8%, 72.3% and 44% respectively in GW-1, GW-2, GW-3 and GW-4 wells. From comparison with Sw Core, the Simandoux Method looks more appropriate. Average mudfiltrate (Sxo) saturation by Simandoux Method were 65.5%, 68.2%, 77.0% and 64.6% respectively in GW-1, GW-2, GW-3 and GW wells -4. Remaining Hydrocarbon Saturation (Shr) was obtained by 34.5%, 31.8, 23%, 35.4% of the results of parameters measured in the flushed zone namely Rxo, Rmf and Sxo data. For the price of Moving Hydrocarbons Saturation or production (Shm) is 31.9%, 24.8%, 10%, 24.9% in wells GW-1, GW-2, GW-3 and GW-4.</p>