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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]

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]

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]

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]