Well abandonment has been associated and considered since in the Field Development Plan stage. Worldwide, government and legislative authorities are having specific requirement and regulation in ensuring the oil and gas industry to seal and permanently take offline unproductive wells to prevent them from impacting the environment and safety. When all feasible opportunity is exhausted and no remaining economic potential is proven in a well or field, it will proceed to abandonment, saving money spent on well liability cost. In effort to reduce the P&A cost which has no financial return, operators and regulators strive to improve P&A method to increase efficiency without compromising safety. The production of oil and gas, whether or not enhanced by the injection of water or gas, will cause a change of pressure, stress and temperature in the reservoir and its surrounding formations. Additionally, chemical characteristics of the injectant, may reactive gases for storage or production enhancement, may lead to changes in petrophysical, geomechanical and chemical properties of subsurface formations, faults and wells equipment. These changes may or may not have a detrimental effect on the containment of toxic or otherwise harmful fluids and gasses in the subsurface. The comprehensive P&A analysis and program is vital to ensure the security of well containment. Loss of containment may lead to potential loss of life, assets, environment and reputation. This paper will discuss the analysis done by Petrophysicist in supporting the decision and design of well P&A design, either isolation at reservoir level or caprock level. After no remaining potential and shallow hydrocarbon is verified, the well will be conditioned for pressure analysis and caprock assessment, by formulating well dynamic strength parameters, namely Young modulus and UCS and establishing pressure column. The competent caprock at the proposed barrier depth will be assessed, benchmarked and inventoried for regional caprock understanding, taking account input from multidiscipline. In addition to additional assessment on rock strength in well P&A design, this paper also recommend the multidiscipline future collaboration assessment technique for better regional caprock understanding. When possible, this method is able to provide feasible P&A design with some confidence level at the competency of the withholding caprock.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractRapid petrophysical evaluation is critical in such expensive venues as the deepwater Gulf of Mexico. Recently, a realtime petrophysical analysis program was introduced based on the combination of elemental concentrations from spectroscopy and standard triple-combo logging 1 .The application consists of a spectroscopy-based quantitative lithology consisting of total clay, sand, carbonate and pyrite 2 . Elemental concentrations are used to correct neutron and density for matrix effects 3 to directly compute total porosity and to correct for lithology and thereby enhance neutrondensity crossover in the case of gas. Intrinsic permeability is computed from the lithology and total porosity using the k-Lambda approach 4 . Water salinity is input and water saturation is computed using the Waxman-Smits-Thomas equations and porosity and lithology 5,6 . Finally, irreducible water saturation is computed from the Coates-Timur 7,8 equation using the calculated porosity and permeability.

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.

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