Summary The Eagle Ford shale (EFS) is the largest single economic development in the history of the state of Texas and ranks as the largest oil and gas development in the world on the basis of capital invested. Between 2008 and the present, the EFS has become one of the most heavily drilled rock units in the US and is the most-active shale play in the world. This paper presents a completion-optimization framework for unconventional plays. The framework uses well-production performance analysis to estimate the fracture characteristics, and assists in diagnosing potential low-productivity issues. The framework enables precompletion planning, real-time completion operations monitoring, and post-completion evaluation of the design effectiveness, and it optimizes future designs. The key components of the framework are prospectivity analysis, completions optimization, and well-performance analysis. Prospectivity analysis provides the map of reservoir quality and rock quality across the play. Precompletion planning, a component of completions optimization, is driven by prospectivity analysis with the goal to design the best completion on the basis of the rock-quality data available. During completions monitoring, the designs are updated on the basis of the actual field pump rates and quantities of proppant pumped to estimate actual hydraulic- and propped-fracture characteristics. The effective fracture geometry is determined on the basis of well-production calibration. Wellbore and completion problems could be diagnosed in this analysis, including damage of fractures, fluid behavior, and well interference. We applied this framework to wells completed in the EFS. The reservoir-quality variability is based on petrophysical evaluation of logs acquired on all study wells. The characteristics of propped fractures were estimated on the basis of geomechanical modeling of actual field pumping measurements. Even though the fractures extended above and below the target interval of the EFS, they were successful in creating the desired half-length from a design point of view. It was also observed that not all the clusters matured, because of the stress-shadow effect. The produced fluids in these wells ranged from black oil to gas condensate. Well interference was observed as a production penalty factor in well-performance analysis. The behavior of current wells was used to design optimal completions for wells that were planned to be completed. This framework uses common data sets collected by a majority of operators. It provides intelligence for completion optimization of future wells after thorough investigation into fracture design, completion operation, and effective fracture characteristics. It is a systematic approach to optimized single-well design and field development (multiple wells, pad drilling).

The Eagle Ford Shale (EFS) is the largest single economic development in the history of the state of Texas and ranks as the largest oil & gas development in the world based on capital invested. Between 2008 and the present, the EFS has become one of the most heavily drilled rock units in the United States and is the most active shale play in the world. This paper presents a completion optimization framework for unconventional plays. The framework utilizes well production performance analysis to estimate the fracture characteristics and assists in diagnosing potential low productivity issues. The framework enables pre-completion planning, real time completion operations monitoring and post-completion evaluation to evaluate design effectiveness and optimize future design. The key components of the framework are Prospectivity Analysis, Completions Optimization and Well Performance Analysis. Prospectivity analysis provides the map of Reservoir Quality (RQ) and Rock Quality (RkQ) across the play. Pre-completion planning, a component of Completions Optimization, is driven by Prospectivity Analysis with the goal to design the best completion based on the Rock Quality data available. During completions monitoring, the designs are updated based on actual field pump rates and quantities of proppant pumped to estimate actual hydraulic and propped fracture characteristics. The effective fracture geometry is determined based on well production calibration. Wellbore and completion problems could be diagnosed in this analysis, including damage of fractures, fluid behavior, and well interference. We applied this framework to wells completed in the Eagle Ford Shale. The Reservoir Quality variability is based on petrophysical evaluation of logs acquired on all study wells. The characteristics of propped fractures were estimated based on geomechanical modeling of actual field pumping measurements. Even though the fractures extended above and below the target interval of Eagle Ford Shale, they were successful in creating the desired half-length from a design aspect. It was also observed that not all the clusters matured because of the stress shadow effect. The produced fluids in these wells ranged from black oil to gas condensate. Well interference was observed as a production penalty factor in well performance analysis. The behavior of current wells was used to design optimal completion for wells planned to be completed. This framework utilizes common data sets collected by majority of operators. It provides intelligence for completion optimization of future wells after thorough investigation into fracture design, completion operation, and effective fracture characteristics. It is a systematic approach to optimized single well design as well as field development (multiple wells, pad drilling).

Ghee (milk fat) due to its high price and demand is highly susceptible to adulteration for economic gains. Further, due to advanced practices of adulteration, its detection is becoming difficult by any single method. In the present study, methods based upon physico-chemical (BR reading and RM value) and chromatography (fatty acid, triglycerides, and plant sterols profiling) were evaluated to detect adulteration. Pure ghee was adulterated @ 1%, 2.5%, 5%, and 10% with coconut, soya bean, groundnut, and sunflower oil. Results of physico-chemical methods indicated that BR reading crossed regulatory limit only @ 10% of sunflower and soya bean oil adulteration while for the other two oils it was within the acceptable range (FSSAI standards for Gujarat i.e. 40 to 43.5). In the case of RM value, adulteration was not detected with any oil up to 10% level of adulteration with respect to FSSAI regulation (i.e. minimum 24) for Gujarat. Among chromatographic methods, fatty acids marker molecules like lauric for coconut and linoleic for soya bean, groundnut, and sunflower oil showed an increase upon adulteration even @ 1%. Marker fatty acids % values were falling outside the range for lauric (i.e.2.3 to 3.2%) and linoleic (i.e.1 to 2%) as specified by FSSAI in the manual of analysis for milk and milk products 2016 however, natural variations might affect the results. Triglyceride profiling was found to be capable of detecting adulteration @ 5% for all the oils except groundnut which was detected @ 10%. Plant sterols (β-sitosterol and stigmasterol) based method was found suitable to detect adulteration @ 1% with sunflower and soya bean oil, @ 2.5% with groundnut oil, and @ 5% with coconut oil. All the methods appear to have their strengths and limitations; hence laboratories may apply a combination of techniques to detect advanced adulteration.