Coupling pinch analysis and rigorous process simulation for hydrogen networks with light hydrocarbon recovery

In refineries,some hydrogen-rich streams contain considerable light hydrocarbons that are important raw materials for the chemical industry.Integrating hydrogen networks with light hydrocarbon recovery can enhance the reuse of both hydrogen and light hydrocarbons.This work proposes an automated method for targeting hydrogen networks with light hydrocarbon recovery.A pinch-based algebraic method is improved to determine the minimum fresh hydrogen consumption and hydrogen sources fed into the light hydrocarbon recovery unit automatically.Rigorous process simulation is conducted to determine the mass and energy balances of the light hydrocarbon recovery process.The targeting procedures are developed through combination of the improved pinch method and rigorous process simulation.This hybrid method is realized by coupling the Matlab and Aspen HYSYS platforms.A refinery hydrogen network is analyzed to illustrate application of the proposed method.The integration of hydrogen network with light hydrocarbon recovery further reduces fresh hydrogen requirement by463.0 m^(3)·h^(-1) and recovers liquefied petroleum gas and gasoline of 1711.5 kg·h^(-1) and 643 kg·h^(-1),respectively.A payback period of 9.2 months indicates that investment in light hydrocarbon recovery is economically attractive.

Hydrocarbon indication in Rio Bonito Formation sandstone:Implication for CO_(2)storage in São Paulo,Brazil

São Paulo State has witnessed CO_(2)storage-based investigations considering the availability of suitable geologic structures and proximity to primary CO_(2)source sinks related to bioenergy and carbon capture and storage(BECCS)activities.The current study presents the hydrocarbon viability evaluations and CO_(2)storage prospects,focusing on the sandstone units of the Rio Bonito Formation.The objective is to apply petrophysical evaluations with geochemical inputs in predicting future hydrocarbon(gas)production to boost CO_(2)storage within the study location.The study used data from eleven wells with associated wireline logs(gamma ray,resistivity,density,neutron,and sonic)to predict potential hydrocarbon accumulation and fluid mobility in the investigated area.Rock samples(shale and carbonate)obtained from depths>200 m within the study location have shown bitumen presence.Organic geochemistry data of the Rio Bonito Formation shale beds suggest they are potential hydrocarbon source rocks and could have contributed to the gas accumulations within the sandstone units.Some drilled well data,e.g.,CB-1-SP and TI-1-SP,show hydrocarbon(gas)presence based on the typical resistivity and the combined neutron-density responses at depths up to 3400 m,indicating the possibility of other hydrocarbon members apart from the heavy oil(bitumen)observed from the near-surface rocks samples.From the three-dimensional(3-D)model,the free fluid indicator(FFI)is more significant towards the southwest and southeast of the area with deeper depths of occurrence,indicating portions with reasonable hydrocarbon recovery rates and good prospects for CO_(2)injection,circulation and permanent storage.However,future studies based on contemporary datasets are required to establish the hydrocarbon viability further,foster gas production events,and enhance CO_(2)storage possibilities within the region.

Regression analysis and its application to oil and gas exploration:A case study of hydrocarbon loss recovery and porosity prediction,China

In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery.

The key parameter of shale oil resource evaluation: Oil content

The United States has become the world's largest oil producer of shale oil. China has abundant shale oil resources, but its resource potential has not yet been exploited. The core of the evaluation is the selection of parameters and their reliability. By combining the parameters of the shale oil resource evaluation, we investigated the key parameters in the evaluation model and reviewed the research results. The adsorption and retention of heavy hydrocarbons, loss of light hydrocarbons, and original oil saturation are key in the evaluation of shale oil resources. The adsorption and retention of heavy hydrocarbons can be determined by the pyrolysis, FID curve, and hydrocarbon generation kinetics of shale before and after extraction. The loss of light hydrocarbons mainly occurs in coring(change in temperature and pressure),sample treatment, which can be evaluated using the GC spectrum, rock pyrolysis, crude oil volume coefficient, mass balance, component hydrocarbon generation kinetics, and other methods. The original oil saturation evaluation includes indirect, direct, logging, and simulation methods. The most reliable parameters can be obtained by using the sealed or pressure-maintained coring immediately after thawing(without crushing), and the recovery of light hydrocarbon loss is critical for the resource evaluation of medium to high mature shale. Therefore, the experimental determination of shale oil content and the study of the influencing factors of the parameters should be strengthened.

Fuzzy optimization design of multicomponent refinery hydrogen network

Hydrogen and light hydrocarbon components are essential resources of the refinery.The optimization of the refinery hydrogen system and recovery of the light hydrocarbon components contained in the gas streams are key strategies to reduce the operating costs for sustainable development.Many research efforts have been focused on the optimization of single impurity hydrogen network,and the flowrates of the hydrogen sources and sinks are assumed to be constant.However,their flowrates vary along with the quality of crude oil and refinery processing plans.A general superstructure of multicomponent refinery hydrogen network is proposed,which considers four components,namely H_(2),H_(2)S,CH_(4) and C_(2+),as well as the flowrate variations of hydrogen source and hydrogen sink.The mathematical model based on the superstructure is developed with objective functions,including the minimization of total annualized cost and the maximization of overall satisfaction of the hydrogen network.Moreover,the model considers the removal of hydrogen sulfide and the recovery of light hydrocarbon components(i.e.,C_(2+))in the optimization.To verify the applicability of the proposed mathematical model,a simplified industrial case study with four scenarios is solved.The optimization results show that the economic benefit can be maximized by considering both the direct reuse of gas streams from high-pressure separator(HP gas stream)and from low-pressure separator(LP gas stream)and the recovery of the light hydrocarbon streams.The fuzzy optimization method can be used to guide the optimal design of the refinery hydrogen system with multi-period variable flowrates.