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Production forecasting using numerical simulation has become a standard in the oil and gas industry. The model construction process requires an explicit definition of multiple uncertain parameters; thus, the outcome of the modelling is also uncertain. For the reservoirs with production data, the uncertainty can be reduced by history-matching. However, the manual matching procedure is time-consuming and usually generates one deterministic realization. Due to the ill-posed nature of the calibration process, the uncertainty cannot be captured sufficiently with only one simulation model. In this paper, the uncertainty quantification process carried out for a gas-condensate reservoir is described. The ensemble-based uncertainty approach was used with the ES-MDA algorithm, conditioning the models to the observed data. Along with the results, the author described the solutions proposed to improve the algorithm's efficiency and to analyze the factors controlling modelling uncertainty. As a part of the calibration process, various geological hypotheses regarding the presence of an active aquifer were verified, leading to important observations about the drive mechanism of the analyzed reservoir. [ABSTRACT FROM AUTHOR]

Abstract Acquaintance of production changes is crucial to successful production forecasting of all reservoir types. However, the performance prediction of CBM is very complex due to its specific production behavior. In the case of the coalbed methane formations two-phase flow, which is caused mainly by hydraulic fracturing process occurs. Discussed production system is more complicated than single-phase flow and finding the relationship between produced gas and produced water is required. In this paper, we propose a novel mathematical model for two-phase flow phenomena in CBM reservoirs. The proposed model uses analogies to natural competition processes and is based on the Lotka-Volterra model, which is one of the most popular populations dynamics model. We implement set of two logistic equations with additional term for the purpose of gas and water production forecasting and modeling their declines curves. This set of two ordinary differential equations was originally used to model dynamics of populations, which compete to each other. Both interspecies and intraspecies interactions are considered in the proposed mathematical model. The developed model was validated by some simulated and field examples. The proposed model was firstly used to match the production history data (gas and water flow rates) in order to estimate the model parameters. These parameters were then used to predict the two-phase CBM production. The obtained results show a good match between the production data and created decline curves based on the proposed model. Therefore, the developed model can be used as a helpful tool in predicting the CBM two-phase production. Highlights • The novel mathematical model for two-phase production forecasting of CBM well has been proposed. • The proposed model uses analogies to natural competition processes and is based on the Lotka-Volterra model. • Decline curves for CBM gas and water flow rates were developed for some simulated and field examples. • Presented results show that the developed model has been successfully applied to CBM wells in the various production stages. [ABSTRACT FROM AUTHOR]

Highlights • Sunflower fibre is a low-cost and biodegradable oil sorbent. • Spongy structure of sunflower pith is responsible for a high sorption capacity. • The sorption kinetics was well described by a modified logistic model. • A deep-bed pith filtration column had been successful in achieving oily water separation. Abstract The application of agricultural by-products as filter bed material for the treatment of oily wastewater is preferable due to their biodegradability and availability. The goal of this paper was to examine the sorption and filtration properties of sunflower (Lat. Helianthus annuus) fibres in a raw state. SEM imaging and nitrogen adsorption at −196.15 °C were applied to evaluate sorbent morphology. FTIR spectroscopy was used to determine the chemical composition of the pith. Studies on sorption in static conditions (pith fragmentation 1.5–4.0 mm) allowed process kinetics to be specified. The experimental kinetics was fitted to pseudo first-order and pseudo second-order kinetics by a non-linear method. To better predict kinetic parameters, a new modified logistic equation was proposed. Sunflower fibre was found to have high oil sorption capacity (12.67 g/g) and good retention properties. During filtration experiments, an artificial reservoir brine (0.1, 2.0, and 20.0 g/l of oil) was used as an oil-in-water emulsion; sunflower pith was used as a filter medium. The influence of four process parameters (pH, bed height, initial oil concentration, and flow rate) on removal efficiency was studied. Oil concentrations were determined by gas chromatography. The results demonstrated that the prepared filters operated better at a lower pH, lower flow, a deeper bed material, and higher concentration of inlet oil. The filtration efficiency for the removal of crude oil from oily brines reached more than 99% in the initial stage of the process. The results obtained suggest that natural plant fibres can be an effective bed material to separate oil from oily brine. [ABSTRACT FROM AUTHOR]

This study examined the interaction of solid nanoparticles and anionic and non-ionic surfactant at an air-water interface. Aqueous foams stabilized by silica nanoparticles in water with different levels of salinity were studied in detail. The stability of solid/surfactant dispersion was evaluated visually. Nanoparticles content impact and concentration of surfactant on the foamability, deliquification of foams and structure of wet foams were studied. It was found that the foamability of dispersion depends either on the surfactant concentration or on the nanoparticles concentration. The adsorption of hydrophobically modified silica particles and surfactants reduces the air/water interface tension. The results of the examinations showed that the use of nanoparticles allows to increase the efficiency of brine unloading even up to 20%. Surfactant particle and nanosilica present synergistic action, use of 4 wt% of nanoparticles allows to reduce surfactant consumption up to half. The cost of the preparation of the proposed dispersion is slightly higher, about 5%, compared to the sole surfactant. [ABSTRACT FROM AUTHOR]

This paper presents an improved reservoir simulation approach to methane production in a longwall mining environment. The coal beds are naturally fractured systems with the gas adsorbed into the coal matrix. Fractures penetrating the coal matrix have limited storage capacity, but they play the role of a gas transportation system. The proposed simulation technique is based on the assumption that a mass of coal removed by mining transfers its gas to adjacent fractures. By using an ECLIPSE coal bed methane simulator, the pore volume of the matrix represents the coal volume of the simulation cell. Consequently, the exploitation of coal can be simulated by modifying the matrix pore volume over time. This paper presents theoretical backgrounds of this approach and investigates numerical effects. A case study of the Moszczenica coal mine in Poland, including computer simulations of methane production, is also reported to show that a long history of the methane and coal recovery can be reproduced using the proposed technique. [ABSTRACT FROM AUTHOR]

The global increase in energy demand and the decreasing number of newly discovered hydrocarbon reservoirs caused by the relatively low oil price means that it is crucial to exploit existing reservoirs as efficiently as possible. Optimization of the reservoir control may increase the technical and economic efficiency of the production. In this paper, a novel algorithm that automatically determines the intelligent control maximizing the NPV of a given production process was developed. The idea is to build an auto-adaptive parameterized decision tree that replaces the arbitrarily selected limit values for the selected attributes of the decision tree with parameters. To select the optimal values of the decision tree parameters, an AI-based optimization tool called SMAC (Sequential Model-based Algorithm Configuration) was used. In each iteration, the generated control sequence is introduced into the reservoir simulator to compute the NVP, which is then utilized by the SMAC tool to vary the limit values to generate a better control sequence, which leads to an improved NPV. A new tool connecting the parameterized decision tree with the reservoir simulator and the optimization tool was developed. Its application on a simulation model of a real reservoir for which the CCS-EOR process was considered allowed oil production to be increased by 3.5% during the CO2-EOR phase, reducing the amount of carbon dioxide injected at that time by 16%. Hence, the created tool allowed revenue to be increased by 49%. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents novel optimisation model to evaluate the optimal number of wells for underground gas storage operations. More specifically, generalized well-placement scheme has been formulated to minimize the total number of wells required to fulfil prescribed multiple constrains including wells interference. The scheme integrates nonlinear models of reservoir and well performance. The optimization problem has been formulated mathematically and solved by iterative use of the SIMPLEX method. The method is dedicated to gas reservoirs or underground gas storages management. The method may be useful as a base for more detailed numerical reservoir simulation. The paper is illustrated by a field example related to optimisation of the production cycle of the underground gas storage in Poland. [Copyright &y& Elsevier]

Abstract: Injection of cooled geothermal water back into the producing formation is a procedure that maintains reservoir pressure and increases energy extraction efficiency, but, because reinjected fluids tend to be much colder than the reservoir rock, injection can also cause cooling of the fluid produced from nearby wells. It is therefore essential to determine the cold front velocity in a geothermal reservoir. Constant thermal properties of both rock and fluid are generally assumed in order to solve this problem. In this paper, the rock density and heat capacity of the water–rock system as functions of temperature are assumed. Using the method of characteristics, an analytical solution is obtained. It is shown that the variable heat capacity of rock leads to a temperature-dependent speed of propagation of the thermal front. This results in a steepening of the front when cold water is injected into a hot zone, and eventually the formation of a discontinuous solution, or shock. A method is proposed for finding such discontinuous solutions and an equation for the velocity of the thermal front is presented. The difference between the front velocity obtained by means of the weak solution presented here and the classical model with constant thermal properties varies between about 1 and 14%. [Copyright &y& Elsevier]

Presents a semi-analytical model for water-coning control in oil wells with dual completions system. Schematic layout of the flow geometry; Input data used for calculations; Ratio of actual water rate to the critical oil rate.

Hydrocarbon production from a gas condensate reservoir has a major concern to the petroleum industry around the world. Due to pressure decline in a reservoir, liquid drops out of solution and cause a significant reduction of well productivity. As a result, huge amount of valuable oil stays underground. The proper production management for a gas condensate reservoir minimizes or eliminates the blocking problems. Optimization of condensate recovery can be achieved through appropriate selection of production and injection rates. However, determination of the optimal rates is a complex problem, involving many factors including geological uncertainty, rock petrophysical characteristics, fluid properties, economic costs, and technical ability. In this research, nature-inspired optimization algorithms are employed to evaluate the potential of production boost. Numerical testing and comparative study revealed that investigated natural-inspired algorithms outperform widely reported in the literature optimization methods and provide a higher quality solutions. The primary concern of this research is to develop an optimal gas condensate production strategy in terms of economic efficiency by utilization of nature-inspired optimization algorithm and providing their ability to generate better quality solution than engineering approach as well as gradient optimization. [ABSTRACT FROM AUTHOR]

Abstract One of the most crucial tasks in hydrocarbon field development, it is an optimal wells placement. Simulation-based well placement optimization can be intricate to implement or computationally expensive. In this paper, a novel approach for the reduction of computational challenges by combining population-base optimization algorithm with self-adapt reservoir clusterization method to determine initial well placement position for the similar producing zones is presented. The developed methodology supported initial well placement location and allowed to shrink search space area. As a result, the computational time was decreased by 18% for particle swarm optimization and by 21% for genetic algorithm. Additionally, the project profitability was increased by 0.24% in case of genetic algorithm utilization and 0.59% for particle swarm optimization, what confirms the necessity for precise well placement localization. The average convergence factor for the employed algorithms increased from 18.43% to 60.78% for genetic algorithm and from 41.71% to 46.96% for particle swarm algorithm. An additional advantage of the developed clustering method is a notable reduction in time to reach high solution diversity in particle swarm algorithm. Increasing SOM training time for the large data set is negligible in comparison with full reservoir simulation run. The proposed methodology is based on the typical numerical model without requirement of additional dataset or streamline and can be easily transferred from field to field. The power and the workflow utility are demonstrated by the results of significant improvement for the algorithm's convergence rate and time. Highlights • Novel self-adapt methodology to enhance well placement optimization was developed. • Significant reduction of optimization time was observed. • Artificial Kohonen neural network was implemented to solve complex engineering problem. [ABSTRACT FROM AUTHOR]

Huff and Puff Enhanced Oil Recovery method can be regarded as promising process to increase oil production rates from developed field. Worldwide experiences in the application for an industrial-scale of this technology has been extensively discussed for heavy oil and tight oil production, however, field unique does not guarantee success for technology transfer to different site. In this way reservoir simulation is used as a first approximation of the project efficiency. However, numerical simulation requires representative data from laboratory experiments. Furthermore, huff-and-puff should be considered as complex problem, where influences from injection rates, soaking time and production rates can not be neglected. On the other side, conducting laboratory investigations are expensive and time-consuming, therefore, these researches should provide the most valuable information. In the presented methodology, laboratory experiments were conjuncted with the numerical representation of a core sample, to generate trustworthy models which were used for the process optimization. The optimal huff-n-puff operational design was computed using a stochastic population-based particle swarm optimization (PSO) method. As a consequence of high computational cost of a single full physic numerical run, the genetic programming as a novel tool for the huff-and-puff process optimization was successfully implemented. The comparison of the optimized results between genetic programming data-drive model and the full-physic numerical run revealed the right approximation and significant computing time reduction. [ABSTRACT FROM AUTHOR]

The paper presents the separation of liquid CO 2 from flue gases produced by oxy-fuel combustion using a cryogenic method. The outlet N 2 stream from a nitrogen removal unit (NRU) is utilized as the cold source of the condenser in the CO 2 liquefaction system. Due to the low temperature of the expanded nitrogen, no external refrigeration cycle is required. This heat integration allows resource consumption to be reduced and economized. The influence of selected process parameters (flue gas composition and operating pressures) on CO 2 recovery was analyzed. In this process, 83.07% of CO 2 with 99.17% purity is captured. The required energy for separation of the liquefied carbon dioxide is 0.125 kWh/kg CO 2 (449 kJ/kg CO 2 ). Cryogenic CO 2 capture is often considered as an unrealistic CO 2 capture technology due to expected high cooling costs. So far, the required cold duty for CO 2 separation was obtained by integration with processes of LNG regasification or/and cryogenic air separation. This paper shows that the addition of a CO 2 liquefaction and separation module to the cryogenic nitrogen removal processes for natural gas allows a significant reduction of energy consumption. Denitrogenation of natural gas is poorly described in literature and this work provides a comprehensive description of a NRU double columns system. Newly developed CO 2 separation unit offers an unique opportunity to produce liquid CO 2 at moderate conditions. An integration of both cryogenic processes is technically and economically advantageous. A hybrid NRU-CO 2 capture installation is an innovative concept with commercial potential. [ABSTRACT FROM AUTHOR]

The application of Enhanced Oil Recovery (EOR) techniques are encouraged by the current oil price and the growing global demand for oil. Optimizing oil production from current resources becomes a main strategic target for many oil producing companies around the world. Among EOR processes, polymer flooding is an attractive option in many reservoirs. The objective of polymer flooding is to control water mobility inside oil reservoirs to ensure high oil recovery factors. Several designing parameters are critical for the success of polymer flooding applications. Chemical concentration depends on geological features of reservoir and well pattern are crucial to make decisions for the reservoir management. In addition, identifying the time of polymer injection is also important in a project as well as slug size. The goal of this article is to evaluate the efficient polymer injection strategy based on global optimization algorithms. Authors investigated the design of polymer flooding process in terms of optimal concentration, slug size and initiation time employ nature inspired algorithms coupling with reservoir simulation. Numerical testing and comparative study show that proposed natural-inspired algorithms outperform widely reported in literature optimization methods in that it provides a higher quality solution. [ABSTRACT FROM AUTHOR]

Oily brine from the gas and oil industries remains the most difficult wastewater to treat due to its complex chemical composition, which includes aromatic hydrocarbons. Even at low concentrations, the presence of BTX (benzene, toluene, xylenes) can be extremely harmful to aquatic ecosystems. Fixed-bed adsorption columns are recommended for oily water treatment due to their flexibility and easy operation. In this research, pine sawdust modified with polydimethysiloxane (PDMS) and hydrophobic nanosilica was applied as a sorbent in a filtration system. The surface modification of raw fiber allowed to change its morphology and increase the roughness of it. The Yoon–Nelson, Bohart–Adams, Clark, and Belter models were applied to simulate continuous biosorption. The Bohart–Adams model strongly correlated with the experimental data and described the whole dynamic behavior of the column. The effect of feed flow rate (10–50 mL/min) on breakthrough characteristics was determined. Both the breakthrough and saturation time decreased as the flow rate increased. This study indicated that hydrophobized pine sawdust is an effective low-cost potential biosorbent for the removal of BTX fraction from produced water in continuous column mode. [ABSTRACT FROM AUTHOR]

Abstract Standard practice for determining permeability is to perform gas permeability measurement and apply correlations to receive corrected permeability as the value of absolute permeability. However, these correlation are dedicated for the specific reservoirs or unconsolidated porous media and cannot be applicable for other rock formations. The objective of this study is to establish correlation between liquid and gas permeability for the pure dolomite. A series of liquid and gas permeability measurements were performed on cylindrical rock samples from formation located in Poland. To determine Klinkenberg-corrected gas permeability, measurements of gas permeability at three (randomly selected) different inverse mean pressure were extrapolated to infinity. Then values of water and corrected-gas permeability were contrasted against each other. The results showed that Klinkenberg permeability overestimates water permeability and the observed difference is in range of 11–24%. This fact can be caused by water polar nature. Hydrogen bonds are formed with neighboring molecules and cause high surface tension and capillary forces. Later, the effect of overburden pressure on water permeability was studied and results showed reduction in permeability as a function of increasing stress. In the last part of the research, the permeability values obtained by using novel equation has been compared with values using other mathematical formulas designed for sandstone and limestone. We learned that our correlation provides results with higher accuracy than the previous equations. [ABSTRACT FROM AUTHOR]

This paper presents the use of scaling and dimensional analysis to assess the viability of conventional modelling of immiscible displacement occurring when water is injected into the oil-saturated, porous rock—a conventional secondary oil-recovery method. A brief description of the laboratory tests of oil displacement with water performed on long core sets taken from wells operating on a Polish oil reservoir was presented. A dimensionless product generator based on dimensional analysis and Buckingham Π theorem was used to generate all possible combinatorial sets of dimensionless products for physical variables describing the phenomenon. The mathematical model of the phenomenon was transformed to its dimensionless form, using a selected set of the products. The results of the laboratory tests were analyzed as functions of the products. Statistically verified quantities describing both dependent and independent experiment variables were subject to a regression analysis to study dependencies of the experimental results upon selected dimensionless products. The degrees of the dependencies were determined and compared with the model coefficients. The conclusions are drawn for the purposes of model application to correctly describe the laboratory and, consequently, field scale processes of immiscible oil displacement by water. [ABSTRACT FROM AUTHOR]

One of the possibilities to reduce carbon dioxide emissions is the use of the CCS method, which consists of CO2 separation, transport and injection of carbon dioxide into geological structures such as depleted oil fields for its long-term storage. The combination of the advanced oil production method involving the injection of carbon dioxide into the reservoir (CO2-EOR) with its geological sequestration (CCS) is the CCS-EOR process. To achieve the best ecological effect, it is important to maximize the storage capacity for CO2 injected in the CCS phase. To achieve this state, it is necessary to maximize recovery factor of the reservoir during the CO2-EOR phase. For this purpose, it is important to choose the best location of CO2 injection wells. In this work, a new algorithm to optimize the location of carbon dioxide injection wells is developed. It is based on two key reservoir properties, i.e., porosity and permeability. The developed optimization procedure was tested on an exemplary oil field simulation model. The obtained results were compared with the option of arbitrary selection of injection well locations, which confirmed both the legitimacy of using well location optimization and the effectiveness of the developed optimization method. [ABSTRACT FROM AUTHOR]

Drilling cost is one of the most critical aspects in the reservoir management plan. Costs may exceed a million dollars; thus, optimal designing of the well trajectory in the reservoir and completion are essential. The implementation of a multilateral well (MLW) in reservoir management has great potential to optimize oil production. The object of this study is to develop an integrated workflow of end-point multilateral well placement optimization integrated with the reservoir simulator supported by artificial intelligence (AI) methods. The paper covers various types of MLW construction, including: horizontal, bi-, tri-, and quad-lateral wells. For quad-lateral wells, the capital expenditure is highest; nevertheless, acceleration of oil production affects the project's NPV (net present value), indicating the type of well that is most promising to implement in the reservoir. Tri- and quad-lateral wells can operate for 12.1 and 9.8 years with a constant production rate. The decreasing hydrocarbon production rate is affected by reservoir pressure and the reservoir water production rate. Other well design patterns can accelerate water production. The well's optimal trajectory was evaluated by the genetic algorithm (GA) and particle swarm optimization (PSO). The major difference between the GA and PSO optimization runs is visible with respect to water production and is related to the modification of one well branch trajectory in a reservoir. The proposed methodology has the advantage of easy implementation in a closed-loop optimization system coupled with numerical reservoir simulation. The paper covers the solution background, an implementation example, and the model limitations. [ABSTRACT FROM AUTHOR]

In order to achieve synergic environmental effect CO 2 -EOR can be applied to improve oil production and reduce carbon dioxide emission. Greater results can be obtained by the CO 2 huff-n-puff process which in the case of oil reservoirs is based mainly on the oil swelling effect. During the soaking period the primary mechanism distributing CO 2 in the reservoir is molecular diffusion. To calculate mass transferred to oil phase molecular diffusion coefficient is used. Its determination can be based on direct or indirect techniques but no standard method is normed. This paper presents a pulse-echo pressure-volume decay technique to determine oil-CO 2 diffusion coefficient under reservoir conditions with the use of acoustically monitored separator. In the proposed method measurements of difference between signal routing are recalculated to volume change in order to compute diffusion coefficient. Experimental procedure was conducted for three different temperatures. The obtained results are consistent with previous studies on diffusion coefficient determination. The proposed technique is more reliable and yields straightforward solution due to simultaneous binary measurements of pressure and volume system. Image 1 • A PEPVD technique to determine Oil-CO 2 diffusion coefficient with the use of acoustically monitored separator was develop. • The proposed technique yields straightforward solution due to simultaneous binary measurements of pressure and volume system. • Experimental procedure was conducted for three different temperatures. [ABSTRACT FROM AUTHOR]