Vibrating-sample magnetometer (VSM) is a magnetic measurement method by observing magnetic moment (M) which is a response of applying ascending and descending magnetic field (H) to the material. The data of this ascending and descending magnetic field will form a kind of loop called hysteresis loop. The hysteresis curve of each material will be different for each kind, so that this curve can be used to evaluate type and domain of magnetic mineral. This paper introduces HYSITS, a MATLAB code for analysing the magnetic hysteresis curve. We aim to provide an easy-use program, such as the feature to adjust smoothing span and increment parameter. With that the hysteresis curve analysis can be done effectively. The optimal result of the parameters adjustment can be seen from the smoothing span 10 for increment values 0,001 and 0,002 on the graph. This MATLAB code will generate 3 plots, which are hysteresis curve (magnetic moment vs. magnetic field), difference of ascending and descending magnetic moment (ΔM vs. H), and the 1st derivative of ΔM vs. H. Although HYSITS has several features that distinguishes it from its non-MATLAB predecessors, HYSITS still needs improvements so that it can be more reliable for research about magnetic hysteresis

The Northern West Java Basin is one of the basins which has been proven to have oil content. In the exploration stage, we need an effective method in providing a description of seismic cross section model of the subsurface conditions of a study area. This study aims to map the thickness of the subsurface structure of the Parigi formation and understand the interpretation of seismic data in the time and depth domain. Seismic cross section has interpreted to determine the location of the alleged potential for hydrocarbon accumulation. In this study we focus on the top and base Parigi formation and obtain subsurface structure map in the time and depth domain. We use time to depth conversion (velocity interval method) to map the subsurface structure thickness. The results of the study show that there is anomaly built up which has potential as a gathering place for hydrocarbon on Parigi formation. The difference in thickness between the top and base Parigi is indicated by the differences in appearance of the structure map. The depth range shown on this map is around 1850ft-2135ft. So that the thickness of the Parigi formation in the study area is 285ft.

Converting seismic data or structural maps from time to depth domain is very important in the stage of oil and gas exploration. Interpretation in time domain often produce inaccurate interpretation especially in zones under high velocity such as sub-salt or sub-carbonate. Under this zone, there are pull up velocity anomalies or pseudo-anticline. Otherwise in zones under low velocity such as water bottoms with sharp or fluctuating slopes (canyons), loose material overburden or rapid sedimentation, under detached electric normal faults and shale flaps, there are push down velocity anomaly or pseudo-syncline. The study area is in the Main and Massive Formation of ITEUNG Field, Northern West Java Basin where is proven to have petroleum potential. In the study area there are pull-up velocity anomaly or pseudo-anticline even though in reality they are just flat or even syncline due to the location in the zone under high velocity. This study aims to correct velocity anomaly by doing time correction and convert the time domain structure map into depth domain structure map with the time-depth curve method to eliminate structural ambiguity on velocity anomaly. The result shows that the map is obtained in the form of a depth domain structure map which is more accurate than the time domain structure map. Depth structure map make the interpretation more accurate because the structures in depth structure map is more similar to the original sovereignty after correlating with well data. In this present study shows that pull up velocity anomaly can be avoided so that interpretation can be done more accurately.

The industrial revolution 4.0 has required collaboration on the oil and gas industry sector. One of the problems is the technology transfer and the increasing of oil reserves supply. It needed to productivity analysis for reservoir rock characterization and subsurface profile that will determine technology transfer. This study was aimed to obtain the hydrocarbon reservoirs quality based on petrophysical parameters. The reservoir zone of JEONA_35 well in 1527.77 - 2067.21 ft. From NPHI and RHOB logs, there is a cross over in 1990-2020 ft that interpreted as oil fluid. The PHIE is 30 %, Sw is 20 % and Vclay is 10 %. Electrofacies analysis shows that the reservoir zone was located in Bekasap and Menggala Formations (1527.77 - 1889.25 ft). In Bekasap Formation, the gamma ray log pattern is a cylinder block shape and thick from the top until base formation, it deposited in the channel fill distribution with thin sandstones lithology. In Menggala Formation, gamma ray log pattern shows the uptrend bell shape that was deposited in the fluvial - delta distribution with thick sandstone. So, it can be concluded that JEONA_35 reservoir has a good quality with the water saturation is low relatively, so the oil saturation is high relatively.

Reservoir’s quality is very important in exploration of oil and gas. This quality can be used to determine the productive field including the reserve itself. To analyse the reservoir quality, net pay thickness needs to be determined. However, different quality of logging tools will affect the log reading and thickness interval. Therefore, it needs to be normalized in order to equalize the reading of minimum and maximum log values in interest zone. This study is intended to interpret the impact of gamma ray normalization on net calculation. The net pay calculation is carried out with and without normalization. To do normalization, cumulative percentile used are 3% and 93%. For instance, before normalization, the highest net pay in PE_35 is 12ft. After P93 and P3 normalization, the highest net pay value become 13.5ft and 51.5ft respectively. If the net pay value of PE_35 using P3 normalization is compared to the net pay value before normalized, then the normalized value is optimized more than 5%, which is a good amount of optimization. Thus, to obtain an optimistic net pay value, normalization must be done. To conclude this study, 3% cumulative percentile normalization method has shown the most optimistic net pay value increase.