Vertical seismic profile (VSP) data is a powerful calibration tool that helps to link the time (surface seismic) & depth domains (well logs). Conventional rig source VSP data is not recommended to be used in a highly deviated well for seismic calibration. Excessive ray bending from the source-receiver offset will cause errors in the vertical time calculations, and this data will not be reliable to calibrate the surface seismic. This paper will discuss a case-study for a highly deviated offshore well using a vertical incident VSP (VIVSP) source geometry. A VIVSP is a unique type of geometry that places the source above the receiver array. This is usually done using a supply vessel for an offshore environment. Placing the air gun source above the receiver ensures vertical raypaths, and this gives the most accurate TWT result. Planning is crucial for this type of VSP to calculate the number of receiver arrays & source positions. 2D ray trace modelling using the surface seismic interval velocity & VIVSP geometry was used to verify the data would be within tolerance for vertical raypaths, and to optimize the acquisition time. VSP data was acquired using the optimized vertical incidence geometry. Data quality was excellent which gave a reliable time-depth profile for most of the well interval. VSP data was processed to corridor stack. and CDP transform was used to create an image below the well trajectory. This data was used to calibrate seismic velocity during seismic processing, as well to tie surface seismic to well.

Vertical seismic profile (VSP) data is a powerful calibration tool that helps to link the time (surface seismic) & depth domains (well logs). Conventional rig source VSP data is not recommended to be used in a highly deviated well for seismic calibration. Excessive ray bending from the source-receiver offset will cause errors in the vertical time calculations, and this data will not be reliable to calibrate the surface seismic. This paper will discuss a case-study for a highly deviated offshore well using a vertical incident VSP (VIVSP) source geometry. A VIVSP is a unique type of geometry that places the source above the receiver array. This is usually done using a supply vessel for an offshore environment. Placing the air gun source above the receiver ensures vertical raypaths, and this gives the most accurate TWT result. Planning is crucial for this type of VSP to calculate the number of receiver arrays & source positions. 2D ray trace modelling using the surface seismic interval velocity & VIVSP geometry was used to verify the data would be within tolerance for vertical raypaths, and to optimize the acquisition time. VSP data was acquired using the optimized vertical incidence geometry. Data quality was excellent which gave a reliable time-depth profile for most of the well interval. VSP data was processed to corridor stack. and CDP transform was used to create an image below the well trajectory. This data was used to calibrate seismic velocity during seismic processing, as well to tie surface seismic to well.

Core & Log Neural Network Modeling (CLONNE) has been initiated to utilize an ANN to optimize usage of available data to generate synthetic logs and core data which enable user to eliminate any special logs and core data acquisition in the future. This will reduce the well cost and time required for data acquisition and data analysis. CLONNE process starts with data gathering of the available core and log data which then QC'ed and conditioned for bad hole, light hydrocarbon, thin lamination and normalized. Then pair of core and log data are combined as dummy well to generate the first CLONNE model that can be used to predict for the whole fields. Conventional data including density, neutron, sonic, GR logs and other parameters are used to generate output. A random well from the field is selected to test the predictability matching of CLONNE versus the real data acquired. Several calibration performed to provide the best predictability. Currently a number of CLONNE models have been created for offshore fields in Malaysia. For CLONNE Synthetic logs, 4 models have been created to predict Porosity, Bulk Density, Neutron and Shear Slowness. For CLONNE Synthetic core, 3 models have been created to predict Grain Size, Permeability and Porosity. All of this models have managed to predict quite well in both thick sand and laminated sand. More models will come to predict other log curves and core parameters. The models established has been tested in one field, where a synthetic sonic log has been created. After the drilling and subsequent logging run, an actual sonic log has been deployed and compared which yield to 96% comparable. The data predicted from CLONNE can greatly save almost 15 months spend to acquire and analyze core data and also almost RM 6 Million total expenditure to acquire and analyze core data. In 2018, CLONNE has achieved RM 6 Million cost avoidance from application in 3 fields in Malaysia. The CLONNE model generated can be implement to Basin wide prediction thus enable the sharing use of data. This will help to integrate the data available instead of data being utilize in the specific field only.

Low Resistivity low contrast (LRLC) reservoirs were normally disregarded due to high water saturation and classified as tight sand. LRLC reservoir defined as Pay that has low resistivity contrast between sand and adjacent shale due to presence of conductive mineral or fresh water. Hence, this paper will transform the standpoint by demonstrating values and potential reserve addition underneath LRLC reservoir which proves that it could contribute equally as the conventional reservoir and realizing potential reserve growth. HY field located in Baram Delta Basin East Malaysia has been producing for more than 40 years and classified as lower coastal plain to coastal environment. The reservoir is loosely consolidated, fine to very fine sandstone and interbedded with shale. Z reservoir (Low Resistivity contrast reservoir) initially identified as gas-bearing reservoir with fresh water salinity of 2k-4kppm. Plus, difference in resistivity values between sand and adjacent shale only separated by ~3ohmm .Due to these claims, there is no Oil interpreted below the gas level and been neglected for years. A robust water salinity investigation supported with the geological point of view and water sample taken at the wellhead, Project Team proposed the water salinity should be 10k-15k ppm which is more saline than previously assumed. Revision in water salinity value has led to pinpoint Z reservoir as Oil bearing reservoir and recover estimated ~200 ft Pay of Oil column in Z reservoir. An appraisal well was drilled for data gathering and exploring potential in the deeper sections, hence serve as a platform for further petrophysical evaluation in the Z reservoir. As a result, Project team managed to take Oil sample and Oil gradient for Z reservoir. In addition, PVT lab result showed the oil sample taken having similar fluid property as the produced oil in the major reservoir. Based from the existing static model, potential additional of recoverable reserves was calculated around 20 MMstb for the Z reservoir. This has been an eye opener for the team to give an extra attention and emphasis on the true potential beneath the LRLC reservoir.

Brownfield in Balingian and Baram Delta have handful of idle wells and well to be abandoned in their inventories. The project aims to reduce the idle well inventories and support production gain through monetizing behind casing opportunities. The target is to appraise and develop LRLC potentials with lower cost of appraisals. This will maximize full field potentials before abandonment and leads to future development of LRLC opportunities as conventional reservoir becomes more difficult to develop. The idle well inventory has grew up due to problem in production (increase water cut, HGOR) and well problems (sand, fish). An order has been introduced to reduce the idle well list up to 50%. Additionally, in the past, the LRLC intervals were often ignored and considered as water-wet sands due to high water saturation or as tight sands. These intervals, that contain significant reserves, are recognized in many technical papers explaining its identification and evaluation techniques from well-data (logs and samples/cores). The scope of the project is to rejuvenate the idle wells by add-perf LRLC reservoirs. It is impossible to achieve the target without the presence of proper and improved LRLC BCO evaluation process, thus an integrated workflow approach (between Petrophysicist, Reservoir Engineer, Production Technologist, Asset manager & Well Intervention group) has been developed and applied in the project. A new evaluation tools had also been developed called REM (Resolution Enhanced Modelling) in order to improve the log properties of LRLC reservoirs so that the data obtained from old conventional tools can still be used to evaluate LRLC reservoir. Although LRLC is termed UNSEEN, the risk is reduced by proper understanding of hydrocarbon column and sand development. To date, 7 fields are already benefitted from this approach. Field A LRLC reservoir for example has tripled the hydrocarbon saturation, and net to gross has improved to 20% using REM compare to 5% without REM. The other 6 fields are also gaining the same increase in the properties. This has resulted in a cumulative potential of 4.4 MMstb of reserves addition and ~11 KBopd potential gain. As a result, a better and attractive BCO proposals can be generated from LRLC opportunities. The exercise will provide the company with cheaper options of appraising and developing LRLC reservoir while reducing the idle wells. There is no better way of understanding LRLC reservoir; as no tools can identify & quantify it yet, rather from the actual production.