Application of Epoxy Coatings to Increase the Efficiency of Wax Oils Production

When producing hydrocarbons,an important task relates to the optimization of the stock of the producing well.The main complications for wells in non-working mode are represented by the formation of asphalt-resinparaffin deposits.This issue is one of the most common problems in the production and transportation of oil.A promising method to deal with these deposits is the application of smooth coatings made of epoxy polymers on the inner surface of the production well tubing.In this work,a number of laboratory studies were carried out on the“Cold Finger”installation to assess the effectiveness of this approach.These laboratory studies have shown that the efficiency related to smooth coatings is 27%while the resulting thermal conductivity ranges from 0.259 to 0.279 W/(m°C).These results demonstrate that this technology can reduce the amount of organic deposits and increase the temperature of oil.

A hybrid machine learning approach based study of production forecasting and factors influencing the multiphase flow through surface chokes

Surface chokes are widely utilized equipment installed on wellheads to control hydrocarbon flow rates.Several correlations have been suggested to model the multiphase flow of oil and gas via surface chokes.However,substantial errors have been reported in empirical fitting models and correlations to estimate hydrocarbon flow because of the reservoir's heterogeneity,anisotropism,variance in reservoir fluid characteristics at diverse subsurface depths,which introduces complexity in production data.Therefore,the estimation of daily oil and gas production rates is still challenging for the petroleum industry.Recently,hybrid data-driven techniques have been reported to be effective for estimation problems in various aspects of the petroleum domain.This paper investigates hybrid ensemble data-driven approaches to forecast multiphase flow rates through the surface choke(viz.stacked generalization and voting architectures),followed by an assessment of the impact of input production control variables.Otherwise,machine learning models are also trained and tested individually on the production data of hydrocarbon wells located in North Sea.Feature engineering has been properly applied to select the most suitable contributing control variables for daily production rate forecasting.This study provides a chronological explanation of the data analytics required for the interpretation of production data.The test results reveal the estimation performance of the stacked generalization architecture has outperformed other significant paradigms considered for production forecasting.

Numerical simulation of fracture propagation in Russia carbonate reservoirs during refracturing

Refracturing treatment is often performed on Russian carbonate reservoirs because of the quick production decline of reservoirs.The traditional refracturing model assumes that a refracture initiates in the normal direction relative to the initial hydro-fracture.This assumption is inconsistent with oilfield measurements of refracture propagation trajectories.Indeed,the existing model is not based on an indepth understanding of initiation and propagation mechanisms of the second hydraulic fractures during refracturing.In this study,we use the extended finite element method to investigate refracture propagation paths at different initiation angles.Both the enriched function approach and phantom mode technique are incorporated into the refracturing model,thereby ensuring that the refracture can freely extend on the structured mesh without any refinement near the crack tips.Key factors including production time,stress anisotropy and initiation angle,and the propped mechanical effect are analyzed in detail.This study provides new insight into the mechanism of refracture propagation in unconventional reservoirs.

Impacts of interactions with low-mineralized water on permeability and pore behavior of carbonate reservoirs

Laboratory filtration experiments are employed to investigate effective well killing while minimizing its impacts on surrounding rocks.The novelty of this experimental study lies in the prolonged exposure of rock samples to the killing fluid for seven days,corresponding to the average duration of well workovers in the oilfields in Perm Krai,Russia.Our findings indicate that critical factors influencing the interactions between rocks and the killing fluid include the chemical composition of the killing fluid,the mineralogical composition of the carbonate rocks,reservoir pressure and temperature,and the contact time.Petrophysical analyses using multi-scale X-ray computed tomography,field emission scanning electron microscopy,and X-ray diffraction were conducted on samples both before and after the well killing simulation.The experiments were performed using real samples of cores,crude oil,and the killing fluid.The results from this study indicate that low-mineralized water(practically fresh water)is a carbonate rock solvent.Such water causes the dissolution of rock components,the formation of new calcite crystals and amoeba-like secretions,and the migration of small particles(clay,quartz,and carbonates).The formation of deep channels was also recorded.The assessment reveals that the change in the pH of the killing fluid indicates that the observed mineral reactions were caused by carbonate dissolution.These combined phenomena led to a decrease in the total number of voids in the core samples,which was 25%on average,predominantly among voids measuring between 45 and 70μm in size.The change in the pore distribution in the bulk of the samples resulted in decreases in porosity of 1.8%and permeability of 67.0%in the studied core samples.The results from this study indicate the unsuitability of low-mineralized water as a well killing fluid in carbonate reservoirs.The composition of the killing fluid should be optimized,for example,in terms of the ionic composition of water,which we intend to investigate in future research.

On the prediction of filtration volume of drilling fluids containing different types of nanoparticles by ELM and PSO-LSSVM based models

There is a direct link between the extent of formation damage and the filtration volume of the drilling fluids in hydrocarbon reservoirs.The filtration volume can be diminished by adding different additives to the drilling fluids.Recently,nanoparticles have been extensively used for enhancing the filtration characteristics of the drilling fluids.However,there is no reliable model for investigating the influence of this class of additives on the performance of drilling fluids.Hence in this study,two powerful tools ELM(extreme learning machine)and PSO-LSSVM(particle swarm optimization-least square support vector machine)are applied to determine the effect of various nanoparticles on the filtration volume.The assessment of the models is carried out by computing the statistical parameters,and it is found that ELM has a greater ability to predict the filtration volumes,while PSO-LSSVM performs satisfactorily too.The model predictions and experimental results are in excellent agreement as suggested by the values of root mean squared error(RMSE=0.2459),coefficient of determination(R^(2)=0.999),and mean relative error(MRE=2.028%)for the dataset.The statistical analysis shows that the suggested model can predict the filtration volume with great accuracy.Moreover,through sensitivity analysis of the input parameters,it is found that for a specified nanoparticle,the filtration volume is highly influenced by nanoparticle concentration and it is the essential variable for the optimization process.

Operational method for determining bottom hole pressure in mechanized oil producing wells,based on the application of multivariate regression analysis

One of the major tasks of monitoring production well operations is to determine bottom-hole flowing pressure.The overwhelming majority of wells in the Perm Krai are serviced using borehole pumps,which makes it difficult to take direct bottom-hole flowing pressure measurements.The bottomhole filtration pressure(BHFP)in these wells is very often determined by recalculating the parameters measured at the well mouth(annulus pressure,dynamic fluid level depth).The recalculation is done by procedures based on analytically determining the characteristics of the gas-liquid mixture in the wellbore,which is very inconsistent to perform due to the mixture's complex behavior.This article proposes an essentially different approach to BHFP measurements that relies on the mathematical processing of the findings of more than 4000 parallel mouth and deep investigations of the oil production wells of a large oil-production region.As a result,multivariate mathematical models are elaborated that allow reliably determining the BHFP of oil-production wells in operation.

Nonlinear Young’s Modulus of New Red Sandstone:Experimental Studies

Young’s modulus of New Red Sandstone was investigated experimentally to gain insight into its nonlinear nature.A large experimental programme was carried out by applying a controllable quasi-static and dynamic uniaxial loading to 286 dry sandstone samples of four different sizes.The static and dynamic tests,similar to those aiming at determining the uniaxial compressive strength,were conducted using the state-of-the-art experimental facilities at the University of Aberdeen including a custom-built small experimental rig for inducing a dynamic uniaxial compressive load via a piezoelectric transducer.The obtained results have confirmed a complex nature of Young’s modulus of sandstone.Specifically,under a harmonic dynamic loading,it shows strongly nonlinear behaviour,which is hardening and softening with respect to frequency and amplitude of the dynamic loading,respectively.

Studying the direction of hydraulic fracture in carbonate reservoirs:Using machine learning to determine reservoir pressure

Hydraulic fracturing(HF)is an effective way to intensify oil production,which is currently widely used in various conditions,including complex carbonate reservoirs.In the conditions of the field under consideration,the hydraulic fracturing leads to a significant differentiation of technological efficiency indicators,which makes it expedient to study the patterns of crack formation in detail.Studies were carried out for all wells,which were considered as the objects of impact,to assess the spatial orientation of the cracks formed.The developed indirect method was used for this purpose,the reliability of which was confirmed by geophysical methods.During the analysis,it was found that in all cases,the crack is oriented in the direction of the section of the development system element characterized by the maximum reservoir pressure.At the same time,the reservoir pressure values for all wells were determined at one point in time(at the beginning of HF)using machine learning methods.The reliability of the machine learning methods used is confirmed by the high convergence with the actual(historical)reservoir pressures obtained during hydrodynamic studies of wells.The obtained conclusion about the influence of the reservoir pressure on the patterns of fracture formation should be taken into account when planning hydraulic fracturing under the conditions studied.

Evaluation of opening of fractures in the Logovskoye carbonate reservoir, Perm Krai, Russia

The development of naturally fractured carbonate reservoirs is extremely challenging. Such reservoirshave a dual pore structure consisting of low-permeable matrix with large pore volume and highpermeable fractures constituting main paths for fluid flow. Productivity of wells drilled in such formations tends to decrease rapidly due to the drop in the reservoir pressure and closure of fractures.Therefore, it is crucial to monitor opening of fractures for the effective development of carbonate reservoirs. Three methods for monitoring of opening of fractures including tracer indicators method,Warren and Root method and Victorin’s empirical relation, are applied in the Logovskoye oil reservoir, acarbonate Tournaisian-Famennian formation in Upper Kama Region, Perm Krai, Russia. The threemethods provide reliable estimation of the opening of fractures, which match the reported laboratorydata obtained on thin sections of core samples. The limitations of each method are also discussed. Thetracer indicator method is time-consuming, the Warren and Root method includes hydrodynamic studiesand requires shutdown of wells influencing the oil production, and the application of Victorin’s relationrequires estimation of initial opening and current compressibility of fractures, which can be done usinganalysis of cores or tracer indicators studies. The appropriate method for monitoring of opening offractures should be chosen according to available resources, time, and economic targets of the development project.