INTERPRETASI AKUSTIK IMPEDANSI (AI) MENGGUNAKAN DATA SEISMIK DAN DATA SUMUR UNTUK MENENTUKAN ZONA PROSPEK HIDROKARBON

Research has been conducted to determine the prospect of hydrocarbon zones using the interpretation of acoustic impedance (AI) seismic method in Field X, South Sumatra Basin, Jambi Province. The purpose of this study was to determine the AI value on the distribution map of AI values for hydrocarbon prospect zones in Field X. In this study the data used were 3D seismic data with PSTM (Post-Stack Time Migration) type, and well data. Data processing using Hampson and Russell (HRS) software used in the mining and petroleum fields has a function for subsurface modeling below the ground surface of reservoir characterization. Well data will be linked to seismic data so that the well data will be in the actual position. The distribution of hydrocarbon prospect zones in PEV-1 well can be seen first in crossplot analysis at a depth of 1760-1798 m with AI cutoff value indicated as sandstone ranging from 8450 (m s)*(g/cc) and above, with a high correlation value 0.818 time shift 0 ms. Picking horizon is carried out to determine the target zone layer and its continuity laterally on seismic volume so that a model based can be done as an initial subsurface description below the soil surface at PEV-1 well. Then the next step is a post-stack analysis based model to find out how much the error value of the target zone prediction with certain parameters through the PEV-1 well data. So with a correlation value of 0.936429 and an error value of 0.35227 in the post-stack analysis model based, AI inversion in the PEV-1 well layer which is the target zone of the hydrocarbon prospect is indicated by the range of 8450 (m/s)*(g/cc) which is indicated as sandstone.

Wet stack – Analysis of condensation

The aim of the paper is to introduce the study of condensation processes in the wet stack. Because of the high cost of reheating (due to the loss of useful heat of flue gases), wet stacks are being considered for new or retrofit applications of wet flue gas desulfurization (FGD) systems around the word. Wet stacks, in contrast to classical chimneys where flue gases are heated up to avoid any condensation, works with wet flue gases and condensation is welcome. The study provides a quantitative analysis of the condensation.

An Overview of Memory Provisioning in Real-Time Wireless Sensor Networks

Wireless Sensor Networks and the smart applications designed to operate upon them have enjoyed a rapid increase in popularity over the last decade. The main challenge currently is the provision of real-time service delivery for wireless sensor networks to cater for new applications with guaranteed Quality of Service (QoS) requirements. However each application has a different service requirement. In order to deliver real-time services the dimensioning of such networks is important to service providers in order to meet these service requirements. If packets cannot be stored due to insufficient memory they are lost. Lost packets result in the resending of the packets and hence an increase in delay in delivery of the application traffic. It is this memory provisioning of these wireless sensor networks that is the focus of the work presented in this paper. More specifically the relationship between the application design, implementation and memory resources required to run the service are explored using a stack analysis tool. This stack analysis tool enables the stack footprint to be measured. Results of memory usage for two different WSN applications are presented. Recommendations based on this study for efficient memory provisioning and ultimately real-time service delivery are given.