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Availability of gases at the field level makes attractive to water-alternating-gas (WAG) process for low viscosity and light oils carbonate reservoir. However, impact of reservoir heterogeneity on WAG performance is crucial before field application. In general, ramp carbonates have heterogeneity due to variation of permeability and porosity. However, WAG performance significantly affected by permeability variations. This article investigates merits and demerits of WAG displacement due to permeability heterogeneities such as permeability anisotropy, high permeability streaks (HKS), matrix permeability, dolomite and thin dense stylolite layers. High-resolution compositional simulations with tuned equation of state (EoS) were carried out using 2D and 3D sector models. The study focuses on WAG performance in terms of oil recovery, vertical sweep, solvent utilization, gas oil ratio (GOR), water cut (WCT), WAG response time, gravity override, hysteresis, un-contacted oil saturation and economics. The results of simulation show that the heterogeneous reservoir provides initially faster WAG response, lower expected ultimate recovery (EUR), faster gas breakthrough, higher GOR and WCT production compared to homogeneous reservoir. The gas gravity override at smaller wells spacing is less in homogeneous reservoir as compared to heterogeneous reservoir, but it is reverse in case of larger well spacing. In heterogeneous reservoir, the HKS shows significant gas override resulting in poor vertical sweep due to capillary holding, and the high permeability dolomite layer shows early water breakthrough. This reservoir has higher solvent utilization in initial stage, and then, it becomes nearly equal to homogeneous reservoir. Simulation in both reservoirs overestimates incremental recovery of 2–3% OOIP at one pore volume injection because of not involving un-contacted oil saturation as predicted in core flood. The findings of this study will help to understand WAG performance and design in highly heterogeneous reservoirs for field applications. Graphical abstract

The application of any empirical correlation is recommended only within its range of validity. Nonetheless, the indiscriminate usage of black oil correlations for fluid modeling happens frequently. This work evaluates Lasater’s (1958) correlation for the solution gas-oil ratio (Rs) fluid property specifically for oils with high CO2 content and high GOR. The correlation results are compared to experimental data, and high prediction errors are observed. Finally, the influence of the Rs prediction error on the flow simulation is assessed.

Measuring oil production rates of individual wells is important to evaluate a well's performance. Multiphase flow meters (MPFMs) and test separators have been used to estimate well production rates. Due to economic and technical issues with MPFMs, especially for high gas-oil ratio (GOR) reservoirs, the use of a choke formula for estimating well production rate is still popular. The objective of this study is to implement different artificial intelligence (AI) techniques to predict the oil rate through wellhead chokes. Support-vector machine (SVM) and random forests (RF) were used to generate different models to predict the production rates for high GOR and WC wells. A set of data (548 wells) was obtained from oil fields in the Middle East. GOR varied from 1000 to 9351 scf/stb, and WC ranged from 1 to 60%. Around 300 wells were flowing under critical flow conditions, while the rest were subcritical. Hence, two cases were studied using each AI model. Seventy percent of the data was used to train both RF and SVM models, while 30% of the data was used to test and validate these models. The developed RF and SVM models were then compared against the previous empirical formulas. The RF model in both critical and subcritical flow conditions was able to perfectly match the actual oil rates. SVM was able to predict the general trend for the oil rates but missed some of the sharp changes in the oil rate trend. The average absolute percent error (AAPE) values in the subcritical flow for SVM and RF were 1.7 and 0.7%, respectively, while in the critical flow, the AAPE values were 1.4 and 0.75% for SVM and RF models, respectively. SVM and RF models outperform the published formulas by 34%. The results from this study will help to estimate the real-time oil and gas rates based on the available data from wellhead chokes without the need for field intervention.

Tremendous progress of developing nuclear magnetic resonance (NMR) fluid analyzer has been witnessed in the oil industry for last two decades. This device allows extensive and accurate exploration of fluid properties, such as its hydrogen content, composition, viscosity, hydrogen index (HI), mud filtrate invasion, gas to oil ratio, average velocity, velocity distribution etc., in the situations of in situ downhole or surface Petro-pipelines. In this review article, we focus on the design principle, manufacturing, implementation, methodology and applications of NMR fluid analyzer to oil and gas industry. A detailed description of the state-of-art NMR fluid analyzers was firstly given to exhibit their respective characteristics. With these experiences on hand, we introduced a series of NMR fluid analyzers designed by us at China University of Petroleum-Beijing with continuous optimizations, in terms of magnet construction, antenna layout, circuit design and operating surroundings. These systems discussed in this article have been demonstrated to achieve multiple NMR parameter acquisition when the fluid is in stationary or flowing state. In the end, a prototype was fabricated and validated considering a vast of engineering influences, such as variable temperatures in a large range, high pressure, limited volume, detection efficiency, etc. A particular emphasis of this paper is to expedite the measurement efficiency of the NMR fluid analyzer to reduce the operation costs. This dilemma can be Figured out by upgrading both pulse sequence and observational mode. For different fluid states, two rapid pulse sequences were proposed to sufficiently obtain the multi-dimensional NMR correlation map. Meanwhile, two observational modes were developed to take full advantage of the polarization time, during which the individual antenna was systematically switched. Another domain of interest in this review concerns the applications of this new tool. For stationary fluids case, accurate identification of fluid properties is of great value for scheme building in oil and gas exploration process. Particularly, it can acquire the fluid content by different NMR responses of different components. In addition, with Bloembergen theory and Stokes–Einstein equation, not only molecular dynamics and composition, but also oil viscosity can be readily evaluated. Moreover, HI information of crude oils will be speculated through partial least square regression. As for flowing fluids case, velocity is a significant parameter to understand the in situ fluid exploitation and therefore evaluate the productivity of certain oil wells or pipelines. Regarding to the unique magnet and antenna designs in our NMR fluid analyzer; this review adopts two distinct methods to obtain flow velocity at a wide rating scale. The first one is a time-of-flight method adaptive in a homogeneous magnetic field, which is suitable in the case of fluid at high flow velocity. The other one relies on the adjacent echo phase difference at a magnetic field with constant gradient, which is preferred for relatively low flow velocity. In the near future, this tool will be tested underground to offer individual fluid velocities by combining both the stationary and flowing analysis methods.

Petroleum resource assessment using reservoir volumetric approach relies on porosity and oil/gas saturation characterization by laboratory tests. In liquid-rich resource plays, the pore fluids are subject to phase changes and mass loss when a drilled core is brought to the surface due to volume expansion and evaporation. Further, these two closely related volumetric parameters are usually estimated separately with gas saturation inferred by compositional complementary law, resulting in a distorted gas to oil ratio under the circumstances of liquid hydrocarbon loss from sample. When applied to liquid-rich shale resource play, this can lead to overall under-estimation of resource volume, distorted gas and oil ratio (GOR), and understated resource heterogeneity in the shale reservoir. This article proposes an integrated mass balance approach for resource calculation in liquid-rich shale plays. The proposed method integrates bulk rock geochemical data with production and reservoir parameters to overcome the problems associated with laboratory characterization of the volumetric parameters by restoring the gaseous and light hydrocarbon loss due to volume expansion and evaporation in the sample. The method is applied to a Duvernay production well (14-16-62-21W5) in the Western Canada Sedimentary Basin (WCSB) to demonstrate its use in resource evaluation for a liquid-rich play. The results show that (a) by considering the phase behavior of reservoir fluids, the proposed method can be used to infer the quantity of the lost gaseous and light hydrocarbons; (b) by taking into account the lost gaseous and light hydrocarbons, the method generates an unbiased and representative resource potential; and (c) using the corrected oil and gas mass for the analyzed samples, the method produces a GOR estimate close to compositional characteristics of the produced hydrocarbons from initial production in 14-16-62-21W5 well.

Accurate prediction of PVT properties of heavy oil system is of great significance to the design of injection–production parameters and dynamic analysis of multi-thermal fluid stimulation in heavy oil reservoir. The saturation pressure and viscosity of Bohai heavy oil system at different temperature and different gas oil ratio conditions were tested and analyzed. The functional relations of regression equations for saturation pressure and viscosity are constructed based on classical PVT correlations, but the parameters in the equations different from classical correlations are obtained from present experiment test data with multiple regression method. The empirical formula analysis results indicate that the 2-parameter equation can almost completely fit the experimental data, but its application scope is narrow. Although the 4-parameter equation has an extensive adaptability, its fitting accuracy is very low. The 3-parameter equation can not only better fit the saturation pressure and viscosity of heavy oil for the same gas under different dissolved gas oil ratios and different temperatures, but also has a wide application range. It is recommended to use the 3-parameter equation for physical property analysis and calculation of heavy oil. The research results provide a basis for the accurate prediction of heavy oil PVT parameters.

Reservoir fluid, pressure volume temperature properties such as bubble point pressure, gas solubility, oil and gas formation volume factors, and viscosities are important elements in the reservoir management and field development. Therefore, this study aims to distribute the bubble point pressure on the main oil reservoirs in the Rumaila oilfield, laboratories tests results from 103 pressure volume temperature reports were used, these tests were obtained from the Mishrif, Upper Shale and Main pay reservoirs. Pressure gradient in the study area was defined using Surfer14 software, and in order to avoid the variation of low-pressure areas in the future drilling wells plan, continuous monitoring and focus on these areas, pressure maps were drawn then reading graduation of pressure on these maps to obtain final formula to adopt a specific range of saturation pressures values ​​and distribute them among Rumaila oilfield. The study also compares between the main sub-units of the Main pay reservoir and Mishrif reservoir in the North Rumaila and other reservoirs in the South Rumaila oilfield as well as to define other factors in pressure volume temperature reports such as gas oil ratio, viscosity and formation volume factor that contribute to the saturation pressure variation.

In this paper the pressure drop and PVT data that used in the model to describe the behavior ofreservoir fluids of 3rd pay reservoir of Zubair field is explained. The wells in Hammar-Shuaiba area showhigh Gas Oil Ratio, exceeding 1,000 scf/stb. This is bad sign and that mean reservoir pressure is reducedramatically and gas will produced , finally the energy that use to push the oil from reservoir to thesurface will decrease. Eleven samples have been collected and analyzed from all 3rd pay reservoirs overthe years, seven samples in Hammar –Shuaiba area. The PVT data resulted to be scattered, being notpossible to define any acceptable conclusion about their trend versus depths, taking also into account thatthey are not referred to the same temperature. The main difference between the old and new PVT is theBubble Point pressure at reservoir conditions, which increases from 2646 psi to 2760 psi. Historicalpressure behavior shows that water Injection is beneficial to maintain stable pressure trend. Pressureanalysis shows a strong depletion start from 2013 in various zones of Hammar Shuaiba domes.

PVT properties of a hydrocarbon reservoir, in addition to estimate oil and gas in place, are required for optimum dynamic reservoir management. In carbonate reservoirs, it is more important as reservoir’s fluid properties usually vary laterally or/and vertically. In this paper, summary results of a study which carried out on the reported PVT properties of reservoir ‘A’ are presented. Reservoir ‘A’ is a real oil carbonate reservoir which suffers from variation of PVT properties. The oil gravity and GOR (gas-oil ratio) in this reservoir are reported to be in the range of 23°–36° API and 300–1400 SCF/STB (Standard Cubic Feet/Stock Tank Barrel), respectively. These wide ranges of PVT properties raised questions about the accuracy and consistency of PVT laboratory test results, FBDST (Full Bore Drill Stem Test) measured data and reservoir characterization, such as effect of seismic recognized faults and the validity of laboratory PVT measurements which conducted on the reservoir fluid samples gathered from adjacent wells in one zone. So, an extensive, systematic study has been conducted on the available fluid data to check the validity of data and find out an acceptable, logical hypothesis about PVT variation across the reservoir, vertically and/or laterally. This study reveals that the reported laboratory measured PVT analysis, oil API gravity and GOR (gas oil ratio) measured in well tests campaign are valid. The PVT variation is dominantly vertical owing to compositional gravity segregation due to relatively high thickness of reservoir, about 300 m (984 ft). It is also distinguished that seismic detected faults (if exist) do not act as barriers at least at geological time.

Reservoir fluid properties are very important in reservoir engineering computations such as material balance calculation, well test analysis, reserves estimate and numerical reservoir simulations. Ideally, these properties should be obtained from laboratory pressure-volume-temperature (PVT) analysis. Quite often, however, these measurements are either not available, or very costly to obtain. In such cases, empirically derived correlations are used to estimate the needed properties, all computation therefore, will depend on the accuracy of the correlations used for estimating the fluid properties. Hence in this study, Standing’s correlation for estimating the solution gas-oil ratio was optimized using a particle swarm optimization (PSO) algorithm to minimize the error associated in estimating solution gas-oil ratio from correlation at various depletion pressure. The optimized correlation was taken as a function of bubble point pressure, API gravity, gas gravity and reservoir temperature. PVT data from differential liberation test was used to validate this study’s correlation and the result obtained shows that the optimized correlation for this study matches closely with the experimental values, also the newly optimized correlation was validated with other models and the results gave the least average relative error of 3.34 and a correlation coefficient of 0.998 after 216th successive iterations by the particle swarm optimization algorithm. Okotie, S. | Department of Petroleum Engineering, Federal University of Petroleum Resources (FUPRE), Effurun, Delta State, Nigeria

Exact determination of pressure-volume-temperature (PVT) properties of the reservoir oils is necessary for reservoir calculations, reservoir performance prediction, and the design of optimal production conditions. The objective of this study is to develop intelligent and reliable models based on multilayer perceptron (MLP) and radial basis function (RBF) neural networks for estimating the solution gas–oil ratio as a function of bubble point pressure, reservoir temperature, oil gravity (API), and gas specific gravity. These models were developed and tested using a total of 710 experimental data sets representing the samples of crude oil from various geographical locations around the world. Performance of the developed MLP and RBF models were evaluated and investigated against a number of well-known empirical correlations using statistical and graphical error analyses. The results indicated that the proposed models outperform the considered empirical correlations, providing a strong agreement between predicted and experimental values, However, the developed RBF exhibited higher accuracy and efficiency compared to the proposed MLP model. Keywords: Solution gas oil ratio, Multilayer perceptron, Radial basis function, Empirical correlation

Well-performance investigation highly depends on the accurate estimation of its oil and gas flowrates. Testing separators and multiphase flowmeters (MPFMs) are associated with many technical and operational issues. Therefore, this study aims to implement the support vector machine (SVM), and random forests (RF) as machine learning (ML) methods to estimate the well production rate based on chokes parameters for high GOR reservoirs. Dataset of 1131 data points includes GOR, upstream and downstream pressures (PU and PD), choke size (D64), and actual data of oil and gas production rates. The data have GOR was up to 9265 SCF/STB, the oil rate varied from 1156 and 7982 BPD. SVM and RF models were built to estimate the production rates. The ML models were trained using seventy percent of the dataset, while the models were tested and validated using 30% of the dataset. The dataset was classified to 622 wells that were flowing at critical flow compared with 509 wells that were flowing at subcritical conditions based on a PD/PU ratio of 0.55. Four machine learning models were developed using SVM and RF for subcritical flow and critical flow conditions. Different performance indicators were applied to assess the developed models. SVM and RF models revealed average absolute percentage error (AAPE) of 1.3 and 0.7%, respectively, in the case of subcritical flow conditions. For critical flow conditions, the AAPE was found to be 1.7% in the SVM model, and 0.8% in the RF model. The developed models showed a coefficient of determination (R2) higher than 0.93. All developed ML models perform better than empirical correlations. These results confirm the capabilities to predict the oil rates from the choke parameters in real-time without the requirement of instrument installation of wellsite intervention.

The better understanding and estimation of reservoir fluids properties have straight effects on accuracy of different processes such as simulation, well testing, and material balance calcul...

Summary Molecular diffusion plays a dominant role in various reservoir processes, especially in the absence of convective mixing. In general, gas diffusion in oils depends on several factors such as pressure, temperature, oil viscosity, and gas/oil ratio (GOR). Out of these factors, the effects of GOR and live-oil-compositional changes on diffusivity are rare or not available in the literature. The current work fills this gap and presents the experimental observations on the effect of GOR on gas diffusivity in reservoir-fluid systems. Synthetic live oils were created by combining stock-tank oil (STO) and methane in various ratios. Constant-composition-expansion (CCE) experiments were performed with these oils to obtain their bubblepoints and liquid densities in relation to GOR. Methane diffusivity in these oils was obtained from pressure-decay (PD) tests at high-pressure/high-temperature (HP/HT) conditions. The diffusion and solubility parameters were estimated from PD data using the diffusion model and integral-based linear regression presented in previous work (Ratnakar and Dindoruk 2015, 2018). The experimental and modeling methodologies are presented here in sufficient detail to allow readers to replicate and evaluate the results. In this work, we experimentally investigated the effect of GOR on methane diffusivity in oils at HP/HT conditions using PD tests. In particular, We present experimental data for bubblepoints and liquid density of synthetic oils with various GOR values. For the range of GORs considered, these measurements show that the bubblepoint pressure increases linearly with GOR. Late-transient solution (LTS) of the PD model was used to obtain diffusivity parameters by regressing against experimental data. It is found that as the GOR value increases (that is, when oil becomes lighter), the diffusivity value increases, which is in accordance with the Stokes-Einstein relation. Most importantly, an empirical correlation is developed on the basis of a limited data set to describe the variation in diffusivity values with GOR. This can be important when experimental data are available for the STO but not for the live oils. It can also be extremely useful in gas-injection processes where the amount of gas dissolved in the oil varies, leading to variations in diffusivity.

We study the impact of explicitly including hysteresis effects (HEs) for relative permeabilities in numerical simulations of enhanced oil recovery approaches grounded on the application of Water Alternate Gas (WAG) injection protocols. Our work explores the significance of hysteresis exhibited by the relative permeability of the non-wetting and/or intermediate wetting phases on the results of reservoir simulations performed within a black-oil modeling approach. We consider four diverse strategies that can be employed to model relative permeabilities of fluid phases under WAG scenarios implemented in a water-wet system following primary waterflooding. The effects of all these modeling strategies are assessed in terms of field oil efficiency (FOE) and gas oil ratio (FGOR) evaluated at the total production lifetime of the reservoir, as compared against a base case where only waterflooding is applied to promote oil recovery. Two of these strategies are implemented and used in typical commercial black-oil reservoir simulators and either neglect hysteresis or consider it only for the gas phase. We then examine a procedure where only hysteresis for relative permeability of the oil phase is considered. Finally, we explicitly consider hysteresis of both non- and intermediate-wetting phases. Our results generally show that including HEs yields an increased FOE and a decreased FGOR. This is consistent with the observation that when injection well controls are set at an optimum value, a non-negligible portion of the domain is associated with saturation paths corresponding to three-phase flow conditions, where saturation history can play a significant role. As compared against a base case where waterflooding is considered as the sole strategy for oil recovery, including hysteresis on three-phase relative permeabilities in the numerical simulations of WAG promotes an increased net present value. We also analyze the impact on reservoir simulation responses of the main WAG injection parameters, i.e., (i) injection rate of gas and water, and duration of (ii) WAG cycles, and (iii) primary waterflooding.

In order to study a potential way to store CO2 and enhance heavy oil production performance, five experiments are implemented on the CO2 huff 'n' puff process using long cores. The production profiles of the CO2 huff 'n' puff process are analyzed, including pressure, heavy oil recovery factor, gas production, cumulative gas oil ratio, and pressure difference. The pressure drops indicate the CO2 diffusion in heavy oil. The pressure drop in the first cycle is much lower than those in the subsequent cycles. The heavy oil recovery factor is higher than 32.75% and can reach as high as 38.02% under the pressure depletion rate of 1 kPa/min. A main trend observed for each test is that the heavy oil recovery factor decreases with increases in the cycle number. With oil production, a growing space is available for CO2 injection in the core, resulting in a higher volume of injected CO2 together with increasing gas production and a cumulative gas oil ratio. With less heavy oil production, the pressure difference between the end port and the production port decreases with the cycle number increases. A novel equation is developed to study the relationship between CO2 production and heavy oil production, and the agreement between the equation and the experimental data is extremely high ( R 2 > 0.97 ). This novel equation can be applied to predict the production performance in the later production period in the same cycle and/or to predict the performance in the subsequent cycles. Via the analyzation of the production performance of the CO2 huff 'n' puff process in heavy oil under different pressure depletion rates and different soaking times, the effect parameters, including pressure depletion rates, soaking time and cycle numbers, are optimized in this study. The optimized pressure depletion rates, soaking time, and cycle numbers are 1 kPa/min, 5 h and 3 cycles, respectively. The optimized parameters gained in the tests were upscaled using the scaling criteria, and the upscaled parameters can be applied in the field pilot test to enhance heavy oil recovery using the CO2 huff 'n' puff process.

Summary Gas Oil Ratio (GOR) is one of the most important production parameters in oil reservoirs. GOR increase may lead to shutting-in the wells due to being uneconomic. The main goal of this study is to investigate two simultaneous water and gas injection scenario in a real fractured oil field with high producing GOR. This field is located in the south-west of Iran and the production from this field was started in 2013. A dynamic reservoir model was built by using the reservoir properties, reservoir fluid model, reservoir geometry, relative permeability and production data. A history match was conducted to validate the reservoir model for future forecasts. After validating the model, two scenarios were investigated including 1) one water injection well and two gas injection wells and 2) one gas injection well and two water injection wells. Then, an optimization study was conducted on each scenario. Production rate, water and gas injection rate, status of the wells were optimized. This optimization caused improved oil recovery of each scenario. The scenario with two water injection wells has better results than the other one due to the controlling gas oil ratio and preventing of production wells shut-in.

The gas compression plant is a core and major unit in oil and gas industries that have high gas oil ratio or considerable gas production. Compressed gas is needed as fuel, support processing handling, increase reservoir builds up pressure by gas injection as well as a useful product. Gas plants are critical and dangerous working location and it is classified as a critical zone due to circumstance parameters like high pressure, high temperature, gas specifications and the potential to impact to human health, safety, environment and possibility to impact invested revenues in case of incidents. Therefore, all recorded compression plant operational failures shall be assessed and reviewed in order to decrease the unit down time and increase plant safety and efficiency. In general, limited studies were conducted in gas plant maintenance management. This paper studied a working gas compression unit in an operating oil and gas field in order to present the followings: • A model of failures raking and sorting in gas compression plants based on total down time importance (TDTI) grouping. • A model of failures ranking by using the risk priority number (RPN). RPN is giving priorities based on associated risk and TDTI as a new method is providing rankings based on maximum contribution to the total occurred down time. Therefore, the study is elaborating to demonstrate these two methods and highlighted the areas of difference which need attention of the owner and the site working team., Graphical abstract Image, graphical abstract

Predictive analysis of the reservoir surveillance data is crucial for the high-efficiency management of oil and gas reservoirs. Here we introduce a new approach to reservoir surveillance that uses the machine learning tree boosting method to forecast production data. In this method, the prediction target is the decline rate of oil production at a given time for one well in the low-permeability carbonate reservoir. The input data to train the model includes reservoir production data (e.g., oil rate, water cut, gas oil ratio (GOR)) and reservoir operation data (e.g., history of choke size and shut-down activity) of 91 producers in this reservoir for the last 20 years. The tree boosting algorithm aims to quantitatively uncover the complicated hidden patterns between the target prediction parameter and other monitored data of a high variety, through state-of-the-art automatic classification and multiple linear regression algorithms. We also introduce a segmentation technique that divides the multivariate time-series production and operation data into a sequence of discrete segments. This feature extraction technique can transfer key features, based on expert knowledge derived from the in-reservoir surveillance, into a data form that is suitable for the machine learning algorithm. Compared with traditional methods, the approach proposed in this article can handle surveillance data in a multivariate time-series form with different strengths of internal correlation. It also provides capabilities for data obtained in multiple wells, measured from multiple sources, as well as of multiple attributes. Our application results indicate that this approach is quite promising in capturing the complicated patterns between the target variable and several other explanatory variables, and thus in predicting the daily oil production rate.

The paper presents a methodology and results of a study to derive the gas-oil ratio (GOR) of oil in situ based on downhole ultrasonic sound speed formation tester measurements. Authors provide several cases as a reference for in-situ fluid acoustic measurement values, and cleanup profiles in various borehole and downhole settings, to illustrate their benefits for formation testing as well as other geoscience applications. Formation tester dynamic fluid measurements of analysis of 47 samples from 25 wells were assessed. Reference GOR measurements were collected from different sources. The relationship between in-situ sound speeds corresponding to reference GOR was assessed and showed strong correlation within the investigated range. To enhance estimation accuracy, another two methods of deriving GOR using fluid slowness, downhole condition parameters, and computed pseudo-density are presented. The assessment of formation tester fluid slowness data showed its consistent association with live oil GOR within the studied range (~20-1,500 scf/bbl). Further increase in GOR showed change in the relationship trend. Several methodologies of GOR estimation are discussed including multiple linear regression using fluid slowness, in-situ pressure, temperature, and linear regression with estimated pseudo-density of a fluid. The multiple regression output showed slightly better match with laboratory data. Results of other data modelling approaches are also discussed. The proposed GOR evaluation methodology is based on acoustic surveys made downhole without phase separation. The results are available at the time of formation testing cleanup and fluid sample capturing stages. The additional benefit of the method is to aid in assuring fluids are maintained in single phase for collection of representative samples.

Bubble point pressure (Pb), oil formation volume factor (BO), and solution gas-oil ratio (Rs) are considered the key parameters required to describe and characterize the crude oil. Accurate determination for crude oil properties are necessary for multi-operation in reservoir evaluation, such as reserve estimation, enhanced oil recovery, oil reservoir performance prediction, designing pipelines and production equipment, and reservoir simulation. Traditional techniques used to calculate PVT data are usually expensive or unavailable, so there are a huge number of empirical correlations developed to estimate PVT properties as a function of production data. But when we used these correlations to predict crude oil properties, big errors are attained. The main target of this study is to find a better and accurate approach for predicting the properties of crude oil. This paper developed new empirical correlations for predicting the properties of reservoir oil as a function of PVT properties such as (P, T, Bo, Rs) using genetic algorithm technique. The simulation model is built using MATLAB software which contains the optimization tool that includes a genetic algorithm tool in it. To validate these correlations, 130 data sets of different crude oils were used. The results obtained showed that the developed empirical correlations from the genetic algorithm model appeared excellent accuracy of predicting crude oil properties compared to their relevant published correlations. The average absolute error for all correlations that the genetic algorithm applied to them is decreased. This technique can be applied to predict crude oil properties with a high level of accuracy.

Depending on the type, quality and quantity of available data, different methods exist to determine the hydrocarbon–hydrocarbon contacts in reservoirs. These methods are either direct or indirect. While the direct method involves consciously drilling a well to penetrate potential fluid contacts, the indirect methods include pressure measurements across at least two different fluid types, and/or, the acquisition and subsequent characterization of downhole fluid samples. Recognizing that most petroleum reservoirs are compositionally graded to varying degrees, compositional grading simulation (CGS) is an inexpensive indirect method of evaluating potential gas–oil contact (GOC) in petroleum reservoirs. However, this typically requires detailed pressure–volume–temperature (PVT) data of the hydrocarbon fluids. But, in many cases, PVT data may be limited. To address the challenges in such cases, this paper proposes a new predictive model and practical workflow, hence extending the applicability of CGS. Employing several PVT datasets and rigorous CGS for known black and volatile oil reservoirs, a new semiempirical function is developed for estimating saturation pressure gradient from gas oil ratio (GOR), which is a readily measurable fluid property. Complementing this function, is the identification of a robust empirical model to predict saturation pressure. The associated workflow provides a systematic integration of the predictive saturation gradient and saturation pressure models to estimate the degree of undersaturation, and hence, potential GOC depth in a reservoir characterized by limited PVT dataset. For validation tests, some cases of saturated and undersaturated reservoirs in the Niger Delta are presented. The results confirm the applicability of the new method in the Niger Delta and elsewhere.

Polymers have been used in water alternative gas, to viscosify the water and improve the overall sweep efficiency. The use of polymer alternative gas was successful in increasing the oil production in high permeability zones. However, few practical factors affecting the field applicability have been overlooked. Therefore, this study is aimed at bridging the gap between the possibility of using several EOR such as water flooding, CO2 flooding, water alternative gas, polymer flooding and polymer alternative gas. The research based on progressive comparison considering constant constraint. The numerical simulation STARS-CMG was used to predict the characteristics and behaviour of the fluid in the reservoir. The designed flooding pattern chosen was a single producer-single injection (P-I) scheme in homogeneous high permeable reservoir. The results of oil incremental recovery showed the following order compared to Water flooding

Prosper software is used for simulating the natural production rates. Prosper calculated both natural and optimized production rates for the existing well, based on the available data. At the early or initial time, the performance of the well deliverability is very high, but after several years of production, production rate declined to a certain level for this well in Kirkuk oil field. Natural well capacity can be defined by using four different approaches; Darcy, Pi-Entry method, Fetkovich's and Vogel method. Actual production rate of the well is known from the given data. The approach which gives the result of the rate closer to the actual production rate will be chosen to implement artificial lift. To optimize production and to get initial rate, artificial lift will be designed; both ESP (Electric Submersible Pump) and Gas lift. Sensitivity analysis are also performed for all the cases of the current well, considering the effects of two different central parameters; which include operating frequency and gas oil ratio for ESP design; Gas injection rate and Gas liquid ratio for Gas lift design for the well output, to obtain a model of behavior of the production system and determining optimal input parameters. Also selecting the suitable equipment depending on the available data of well stream and well properties. To conclude, designing ESP will give an effective production rate as compared to Gas lift method, based on the application of sensitivity analysis and the cost of each method.

Summary This research is focused on developing a methodology for estimating well production forecasting that supports conceptual field development plans in organic-rich mudstone reservoirs encompassing typical exploitation stages in these kind of reservoirs: exploratory, appraisal, commercial demonstration, and manufacturing. The forecast simultaneously involves produce fluids (oil, water and gas) from both flow-back and decline periods in multistage hydraulic fracture (MSHF) horizontal wells. This methodology is applied in new exploration areas where vertical wells are already drilled. Estimated Ultimate Recovery (EUR) per horizontal well is estimated probabilistically combining volumetric and stochastic methods taken into account uncertainties associated with each petrophysical, fluid, and recovery factor (RF) properties. Arp´s modified Decline Curve Analysis (DCA) is applied based on typical behaviors observed from USA unconventional plays to have ranges for each DCA variables, especially initial decline (D_i) and b-factor. Thus, ranges for flow-back time, initial oil rate (Q_oi,), water oil ratio, and gas oil ratio are tuned to match the probabilistic EUR. Then, aggregation curve methods are used to obtained EUR probabilistic distribution as a function of numbers of wells.

In this study, foam is selected to treat production wells of heterogeneous reservoirs that are suffering from high amounts of gas oil ratio (GOR). To this end, a numerical study is carried out by injecting foam through the perforations that produce high amounts of gas to block the gas path and allow much more oil to be simultaneously produced from the remaining perforations by applying a new technology in well completion. For this purpose, three heterogeneous synthetic base sectors are simulated in which large amounts of injected gas are produced from the upper, middle and lower perforations of the producing well, known as gas overriding, gas channeling-midriding and gas channeling-underriding, respectively. Since Kv/Kh is an important, decisive and effective parameter, different values of it have been considered for each of three base sector models including 0.001, 0.01, 0.1, 0.3, 0.5, 0.7, 0.9 and 1.0. Then, depending on the magnitude of Kv/Kh, the most optimal treatment method is sensitivity analyzed. In the following, foam injection parameters, e.g. surfactant concentration, depth of packer set, foam injection sequence and foam injection rate are sensitivity analyzed. Also, the phenomenon of gas cross-flow during the treatment process is investigated. The simulation results show that if after some time following well treatment, the GOR level increased, it is suggested to take a production test to determine what length of the production well below the packer produces lots of gas to set the packer at the lower depth and inject a larger volume of foam to block that gas path so that the GOR is reduced. The results also conclude that the magnitude of Kv/Kh on one hand and the location of gas entry to the production well on the other hand play a key role in the selection of the best treatment scenario in heterogeneous reservoirs. This has led to presenting a protocol for treatment of high GOR production wells in a wide range of reservoirs based on anisotropy ratios. Finally, in order to validate the protocol, a real specific sector of a heterogeneous carbonate reservoir located in the Middle East, in which the GOR level has increased, is simulated and treated. The results indicate that the protocol is efficient and causes a reduction in GOR by 60%.

This study presents Artificial Neural Network (ANN) and Fuzzy Logic (FL) techniques to predict of some important reservoir fluid properties, like, bubble point pressure (Pb), oil formation volume factor at bubble point pressure (βo at Pb), and solution gas oil ratio at bubble point pressure (Rs at Pb) using chemical composition of crude oil. The presented models here were established using 1500 data points, collected from mainly unpublished sources. Statistical analysis was conducted to see which of the Artificial Intelligence techniques (AI) were more reliable and accurate for predicting the reservoir fluid properties. The new (FL) models outperformed most of the (ANN) models. The presented models provide good estimation for (Pb), βo at Pb, and Rs at Pb with correlation coefficient (R2) of 0.995, 0.991, and 0.998, respectively.

Horizontal well drilling is a well-established technology to enhance well productivity by increasing reservoir contact compared to that of vertical wells under the same conditions. In gas condensate reservoirs, in addition to the three-dimensional (3D) nature of the flow geometry around the horizontal well, the flow behaviour is further complicated by the phase change and the variation of relative permeability (kr) due to the coupling (increase in kr by an increase in velocity or decrease in IFT) and inertia (a decrease in kr by an increase in velocity) effects. There are no practically attractive simple methods for well productivity calculations that account for these effects. Therefore, as an alternative, numerical simulation of such a complex 3D flow using commercial numerical simulators is usually adopted. This approach requires a 3D fine grid compositional approach which is very demanding, cumbersome and often associated with convergence problems due to numerical instability. Consequently, the introduction of a quick and reliable tool for long term well productivity calculations for gas-condensate systems is the main objective of the present work. An in-house simulator was developed to realistically simulate the multiphase flow of gas and condensate around horizontal wells. Using this model, a large data bank was then generated covering the impact of a wide range of pertinent geometric and flow parameters on well performance including: well and reservoir geometries, reservoir and bottom-hole pressure, fluid velocity, gas oil ratio and fluid composition. Based on the results of these simulations, a new method has been proposed to predict the productivity of horizontal wells for gas and condensate systems. In this approach, the flow behaviour of gas and condensate around the well is quantified in terms of the effective wellbore radius of an equivalent open hole that replicates flow around the actual 3D system. The effective wellbore radius varies with fluid properties, velocity and interfacial tension (IFT), reservoir and wellbore conditions. The integrity of the new methodology has also been verified for various fluids and flow conditions. This approach, included in a simple spreadsheet, can predict the horizontal well performance, significantly facilitating engineering and management decisions on the application of costly horizontal well technology.

Oil formation volume factor (OFVF) is considered one of the main parameters required to characterize the crude oil. OFVF is needed in reservoir simulation and prediction of the oil reservoir performance. Existing correlations apply for specific oils and cannot be extended to other oil types. In addition, big errors were obtained when we applied existing correlations to predict the OFVF. There is a massive need to have a global OFVF correlation that can be used for different oils with less error.The objective of this paper is to develop a new empirical correlation for oil formation volume factor (OFVF) prediction using artificial intelligent techniques (AI) such as; artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM). For the first time we changed the ANN model to a white box by extracting the weights and the biases from AI models and form a new empirical equation for OFVF prediction. In this paper we present a new empirical correlation extracted from ANN based on 760 experimental data points for different oils with different compositions.The results obtained showed that the ANN model yielded the highest correlation coefficient (0.997) and lowest average absolute error (less than 1%) for OFVF prediction as a function of the specific gravity of gas, the dissolved gas to oil ratio, the oil specific gravity, and the temperature of the reservoir compared with ANFIS and SVM. The developed empirical equation from the ANN model outperformed the previous empirical correlations and AI models for OFVF prediction. It can be used to predict the OFVF with a high accuracy. Keywords: Oil formation volume factor, Artificial intelligent, Reservoir management, Artificial neural network

The production process in the gas reservoir with an aquifer is complex. With gas production, aquifer water could possibly flow into the production well and accumulate within the well bore, which leads to a lower production rate and may even block the producer. However, few studies in the literature investigate the damage caused by the liquid phase in this kind of reservoir or predict gas productivity using the relationship between reservoir pressure and water gas ratio (WGR). For this reason, it is important to know the effects of the formation of liquid phase behavior on production when an aquifer is present under a gas reservoir. From the results of published literature reviews, we found that studies focused on the production of a gas reservoir with bottom water. Nevertheless, for gas well damage from the liquid phase behavior, we found that there was no statistical data or mathematical model of the relationship between reservoir pressure and the gas oil ratio (GOR), which affects production. In this research, based on the theory of fluid flow in porous media, a new mathematical model of water and gas production and a new equation on gas well productivity is developed. To verify the model and equation, gas production data collected from the field are applied. By analyzing the typical gas reservoir with bottom water and the collected data, influences from the liquid phase behavior are shown. In this way, mathematical relationships between reservoir pressure and the WGR and between the GOR and production decline were obtained. The new gas productivity model is derived from the gas and water pseudo pressure functions, which can be applied to analyze well damage caused by the liquid phase. In order to verify the mathematical model, production data were collected from a typical gas reservoir with an aquifer located in the Changxing gas reservoir. The results indicate that a semi-logarithmic linear relationship is obtained between the WGR and productivity decrease. When the WGR increases from 0.5 to 15 m 3 /10 4 m 3 , damage caused by liquid phase decreases to 59%. The tendency of gas productivity in the Changxing gas reservoir was obtained so that it decreases as reservoir pressure decreases and increases as the WGR decreases. The gradient of the gas productivity deduction increases as the WGR increases. By the end of the data analysis, two linear equations indicating the relationship between gas productivity and reservoir pressure and the relationship between gas productivity and the water gas ratio are obtained: Q A O F = − A 1 l n W G R − B 1 and Q A O F = A 2 l n P − B 2 , respectively. The new model and these two equations can be applied to predict gas productivity in the gas reservoir with an aquifer and determine the damage level to the gas well. They also can be used to guide development plans in the gas reservoir with an aquifer.

Artificial intelligence (AI) methods and applications have recently gained a great deal of attention in many areas, including fields of mathematics, neuroscience, economics, engineering, linguistics, gaming, and many others. This is due to the surge of innovative and sophisticated AI techniques applications to highly complex problems as well as the powerful new developments in high speed computing. Various applications of AI in everyday life include machine learning, pattern recognition, robotics, data processing and analysis, etc. The oil and gas industry is not behind either, in fact, AI techniques have recently been applied to estimate PVT properties, optimize production, predict recoverable hydrocarbons, optimize well placement using pattern recognition, optimize hydraulic fracture design, and to aid in reservoir characterization efforts. In this study, three different AI models are trained and used to forecast hydrocarbon production from hydraulically fractured wells. Two vastly used artificial intelligence methods, namely the Least Square Support Vector Machine (LSSVM) and the Artificial Neural Networks (ANN), are compared to a traditional curve fitting method known as Response Surface Model (RSM) using second order polynomial equations to determine production from shales. The objective of this work is to further explore the potential of AI in the oil and gas industry. Eight parameters are considered as input factors to build the model: reservoir permeability, initial dissolved gas-oil ratio, rock compressibility, gas relative permeability, slope of gas oil ratio, initial reservoir pressure, flowing bottom hole pressure, and hydraulic fracture spacing. The range of values used for these parameters resemble real field scenarios from prolific shale plays such as the Eagle Ford, Bakken, and the Niobrara in the United States. Production data consists of oil recovery factor and produced gas-oil ratio (GOR) generated from a generic hydraulically fractured reservoir model using a commercial simulator. The Box-Behnken experiment design was used to minimize the number of simulations for this study. Five time-based models (for production periods of 90 days, 1 year, 5 years, 10 years, and 15 years) and one rate-based model (when oil rate drops to 5 bbl/day/fracture) were considered. Particle Swarm Optimization (PSO) routine is used in all three surrogate models to obtain the associated model parameters. Models were trained using 80% of all data generated through simulation while 20% was used for testing of the models. All models were evaluated by measuring the goodness of fit through the coefficient of determination (R2) and the Normalized Root Mean Square Error (NRMSE). Results show that RSM and LSSVM have very accurate oil recovery forecasting capabilities while LSSVM shows the best performance for complex GOR behavior. Furthermore, all surrogate models are shown to serve as reliable proxy reservoir models useful for fast fluid recovery forecasts and sensitivity analyses.

This paper presents the application of a steady-state gradient control using transient measurements to a gas-lift optimization problem. Optimal operation of a gas-lifted field involves controlling the marginal gas-oil ratio (mGOR), which is the steady-state gradient of the oil rate with respect to the gas lift rate. In this paper, we apply a novel method to estimate the marginal GOR online using a dynamic model and transient measurements, without the need for additional perturbation. The proposed method is based on linearizing the dynamic model around the current operating point to estimate the marginal GOR, which is then controlled using simple feedback controllers to achieve optimal operation. In case of disturbances, the proposed method is able to adjust fast to the new optimal point, without the need to solve computationally expensive optimization problems. By using transient measurements, it does not need to wait for the process to reach steady-state to update the model. The proposed method was tested in simulations and was shown to provide similar performance as an economic MPC.

Dispersion models for the simulation of an industrial Fluid Catalytic Cracking Riser Reactor have been developed. The models were developed based on the principle of conservation of mass and energy on the reacting species due to bulk flow and axial dispersion. The four-lump kinetic scheme was used to describe the cracking reactions occurring in the reactor. The model equations were a set of parabolic Ordinary Differential Equations which were reduced to first order differential equations by appropriate substitutions and integrated numerically using 4th order Runge Kutta algorithm using Visual Basic 6.0. Results obtained showed a maximum percentage deviation ranging from 0.31% to 5.7% between model predictions and industrial plant data indicating reasonable agreement. Simulation of model at various operating parameters gave optimum gasoline yield of 45.6% of the most significant variable of temperature (658 K), superficial velocity (0.1 m/s), catalyst to gas oil ratio (7.0) and diffusion coefficient of 0.23 m2/s.