An Optimization-Based Method for the Explicit Production Data Analysis of Gas Wells

This work aims at the exploration of production data analysis (PDA) methods without iterations. It can overcome limitations of the advanced type curve analysis relying on the iterative calculation of material-balance pseudotime and current explicit methods reckoning on specific production schedule assumptions. The dynamic material balance equation (DMBE) is strictly proved by the integral variable substitution based on the gas flow equation under the boundary dominated flow (BDF) condition and the static material balance equation (SMBE) of a gas reservoir. We introduce the pseudopressure level function γ(p) and the recovery factor function R(p) to rewrite the DMBE in terms of observed variable Y and estimated variable Ye; then the PDA can be transformed into an optimization problem of minimizing the error between Y and Ye. An optimization-based method for the explicit production data analysis of gas wells (OBM-EPDA), therefore, is developed in the paper, capable of determining the BDF constant and gas reserves explicitly and accurately for variable rate and/or variable flowing pressure systems. Three stimulated cases demonstrate the applicability and validity of OBM-EPDA with small errors less than 1% for estimated values of both reserves and Y. Not second to previous studies, the field case analysis further proves its practicability. It is shown that the nonlinear relation of γ to R can be represented by a polynomial function merely dependent on the inherent properties of the gas production system even before sorting out the production data. The errors of observed variable Y provided by OBM-EPDA facilitate the data quality control, and the elimination of outliers not subject to the BDF condition improves the reliability of the analysis. For various gas systems producing whether at a constant rate, a constant bottomhole pressure (BHP), or under variable rate and variable BHP conditions, the proposed method gives insights into the well-controlled volume and production capacity of the gas well whether in a low-pressure or high-pressure gas reservoir, where the compressibilities of rock and bound water are considered.

Synthesis and characterization of fat-liquor from waste tallow

The fat-liquoring is an important step in leather making before dying to improve the glossiness, appearance, physical and chemical qualities of the leather. Synthetic sulphonated or sulphited oils are generally used to fill fibrous leather & to give it soft, elastic and loose characteristics. Natural fat-liquors (vegetable and animal-based) and synthetic fat-liquors are the two types of emulsions. The emulsion’s charge can be anionic, cationic, or nonionic. In this study, fat-liquor has been made from a bio-waste, namely tallow, which is obtained from a slaughterhouse as a byproduct of the animal hides and skin processing for leather. Triglycerides, a combination of oleic, stearic, and palmitic fatty acids, and glycerol make up the majority of this animal fat. Fat-liquor is made through a series of three reactions, namely, amidation, esterification, and sulphitation. Amidation helps to increase the hydroxyl groups. To react with fat, alkanol amine with a wide emulsifying characteristic isutilised. Anhydrides derived from di-carboxylic acids were then esterified with amidated fat in the next phase. By altering the process recipe, the stability of the emulsion product has been examined, and required raw materials are optimized. Finally, aqueous hydrolyzed sodium metabisulphite is used to sulphite the product, yielding bisulphite and hydroxide ions. The saponification and acid values are computed. The end product has a distinct advantage (anti-foaming & fire-retardant) over traditional fat-liquoring techniques. Material balance is performed once the process flow sheet was created. The process has been scaled up with the help of a preliminary reactor design. The degree of fat-liquoring and the process’ performance are revealed by FTIR spectrum. NMR was used to determine the final product’s structure.

Forecasting the operation of wells in the Bazhenov formation based on a modified dynamic material balance model

Background. Predicting the dynamics of the Bazhenov formation is an important task. Traditionally, it is carried out using geological and hydrodynamic modeling, i. e., solving the direct problem of hydrodynamics. However, for shale reservoirs, this approach is not possible, oil production is a derivative of geology to a lesser extent than technology. Industrial net production rates can be obtained from non-reservoirs in the usual sense. The system of technogenic fractures forms a reservoir associated with oil-saturated rock and the properties of such a system are described by too many parameters with high uncertainty and a number of assumptions [3–7]. On the other hand, there are forecasting methods based on solving the inverse problem of hydrodynamics. Having a sufficient amount of development data, it is possible to predict the dynamics of work based on statistical dependencies [9] or proxy material balance models. The purpose of this work. The purpose of this work was to create a convenient methodology for calculating oil production from the reservoirs of the Bazhenov formation. Methodology. The paper proposes and tests a method for predicting the dynamics of oil, liquid and gas production for wells in the Bazhenov formation based on a modification of the CRM dynamic material balance model (Capacity-Resistive Models — volume-resistive model). Results. The method was tested when calculating the technological indicators of development for the object of one of the fields located in the KhMAO and showed its efficiency, which allows us to recommend it as a basis for drawing up project documents as an alternative to building a hydrodynamic model (GDM).

The sodium 5-butyl-1,2-diphenyl-6-oxo-1,6-dihydropyrimidine-4-olate quantitative content determination in a standard sample

Introduction. The standard samples (SS) use is a necessary condition for the medicines' quality control implementation. Their development is an urgent problem for the pharmaceutical industry, especially for new biologically active compounds that can be further used as pharmaceuticals.Aim. This work aim is to establish the 5-butyl-1,2-diphenyl-6-oxo-1,6-dihydro pyrimidone-4-olate sodium quantitative content, for which anti-inflammatory and analgesic activity was previously proven, in a standard sample.Materials and methods. This work aim is to establish the 5-butyl-1,2-diphenyl-6-oxo-1,6-dihydro pyrimidone-4-olate sodium quantitative content, for which anti-inflammatory and analgesic activity was previously proven, in a standard sample. The main method for establishing a substance quantitative content in the SS is the material balance method. The water determination was carried out according to K. Fisher's method (semimicro method). Sulphated ash was determined according to the XIV edition Russian Federation State Pharmacopoeia General Pharmacopoeia Monograph "Sulphated ash". Related impurities and their content were assessed using the HPLC method on a Flexar liquid chromatograph equipped with a diode array detector (Perkin Elmer, USA). The residual solvents' determination was carried out by the headspace method using a gas chromatograph GC-2010Plus Shimadzu with a flame ionization detector. As an additional method for establishing the main component quantitative content, acidimetric titration with the equivalence point potentiometric indication was carried out.Results and discussion. The percentage was determined for the following indicators: water, residual organic solvents, related impurities, sulphated ash. Using the material balance method, it was found that the 5-butyl-1,2-diphenyl-6-oxo-1,6-dihydropyrimidin-4-olate sodium percentage in a standard sample is 96.01 ± 0.50 %. It was found by acidimetric titration that the 5-butyl-1,2-diphenyl-6-oxo 1,6-dihydropyrimidin- 4-olate sodium quantitative content in SS is 95.12 ± 0.02 %. The difference in the certified value can be explained by the fact that during titration, the SS aciform is released, which precipitates in an aqueous medium and contributes to a shift in the equilibrium and pH value. Consequently, the equivalence point is reached somewhat earlier. However, the data are practically comparable, but it is necessary to use the value obtained by the material balance method.Conclusion. A standard sample certification parameters were determined: water content, residual organic solvents, sulphated ash, related impurities. The main component quantitative content was determined using the material balance method and titrimetry (acidimetry with the equivalence point potentiometric indication).

Hierarchical Approach to Identifying Fluid Flow Models in a Heterogeneous Porous Medium

A three-dimensional numerical hydrodynamic model fairly accurately describes the processes of developing oil and gas fields, and has good predictive properties only if there are high-quality input data and comprehensive information about the reservoir. However, under conditions of high uncertainty of the input data, measurement errors, significant time and resource costs for processing and analyzing large amounts of data, the use of such models may be unreasonable and can lead to ill-posed problems: either the uniqueness of the solution or its stability is violated. A well-known method for dealing with these problems is regularization or the method of adding some additional a priori information. In contrast to full-scale modeling, currently there is active development of reduced-physics models, which are used, first of all, in conditions when it is required to make an operational decision, and computational resources are limited. One of the most popular simplified models is the material balance model, which makes it possible to directly capture the relationship between reservoir pressure, flow rates and the integral reservoir characteristics. In this paper, it is proposed to consider a hierarchical approach when solving the problem of oil field waterflooding control using material balance models in successive approximations: first for the field as a whole, then for hydrodynamically connected blocks of the field, then for wells. When moving from one level of model detailing to the next, the modeling results from the previous levels of the hierarchy are used in the form of additional regularizing information, which ultimately makes it possible to correctly solve the history matching problem (identification of the filtration model) in conditions of incomplete input information.

Performance Analysis of Gas Wells Based on the Conventional Decline Parameters and the Flow Integral Equation

The estimation of reserves and performance prediction are two vital tasks for the development of gas reservoirs where the evaluation of gas in place or well-controlled reserves, as the foundation of the performance analysis of gas wells, turns to be exceedingly significant. Advanced production data analysis or modern rate transient analysis (RTA) methods mainly depend on the iterative calculations of material balance quasitime ( t ca ) and type curve fitting, the essence of which is to update the average reservoir pressure data time and again. The traditional Arps’ decline models are of empirical nature despite the convenience and applicability to the constant bottomhole pressure (BHP) condition. In order to avoid the implicit iteration, this paper develops an explicit method for estimating the average reservoir pressure on the basis of dynamic material balance equation (DMBE), termed “flow integral method,” which can be applied to various gas production systems under boundary-dominated flow (BDF). Based on the flow integral method and the decline parameter evaluation, we employ the hyperbolic decline model to model the gas well performance at a constant BHP. The analytical formulations of decline rate and decline exponent are deduced from the DMBE and the static material balance equation (SMBE) considering the elastic compressibilities of rock pore and bound water. The resulting decline parameter method for explicit estimation of gas reserves boasts a solid and rigorous theory foundation that production rate, decline rate, and average reservoir pressure profiles have reference to each other, and its implementation steps are explained in the paper. The SMBE can, combined with the estimated pressure profile by the flow integral method, also be used to determine gas reserves which is not limited to the constant-BHP condition and can calibrate the estimates of the decline parameter method. The proposed methods are proven effective and reliable with several numerical cases at different BHPs and a field example.

A Novel Mathematical Model for Fracturing Effect Evaluation Based on Early Flowback Data in Shale Oil Reservoirs

For shale oil reservoirs, the horizontal well multistage fracturing technique is mostly used to reform the reservoir in order to achieve economic and effective development. The size of the reservoir reconstruction volume and the quantitative characterization of the fracture system are of great significance to accurately predict the productivity of shale oil wells. There are few flowback models for shale oil reservoirs. To solve this problem, first, a physical model of the simultaneous production of oil, gas, and water in the early flowback stage of shale oil development is established using the material balance equation for a fracture system. Second, the physical model of the underground fracture system is simplified, which is approximately regarded as a thin cylindrical body with a circular section. The flow of the fluid in the fracture system is approximately regarded as radial flow. In this model, the expansion of the fluid and the closure of the fracture are defined as integrated storage coefficients to characterize the storage capacity of the fracture system. Then, the curves illustrating the relationships between the oil-water ratio and the cumulative oil production and between the gas-water ratio and the cumulative gas production are drawn, and the curves are used to divide the flowback stage into an early stage and a late stage because the flowback process of shale oil wells exhibits obvious stage characteristics. Finally, the reservoir reconstruction volume and the related hydraulic fracture parameters are estimated based on the material balance method, and the rationality of the model is verified via numerical simulation. The interpretation results of this novel model are more accurate, making it an effective way to evaluate the hydraulic fracture parameters and transformation effect, and it has guiding significance for the evaluation of the hydraulic fracturing effect in the field.

Climate challenges and hydraulic power industry as a powerful tool to achieve the goals of the Paris Agreement

Fossil fuel energy and increase in concentrations of greenhouse gases in the atmosphere cause global climate change. In pursuance of the goals of the Paris Agreement, the global power industry must switch a significant part of fuel energy production to renewable energy production. The expected share of various sources in the global power industry by the end of the 21st century is provided. However, the limited possibilities of the biosphere make the current level of energy production from renewable sources nearly impossible. The most preferable scenario is proposed to reduce global carbon dioxide emissions by reducing the use of coal by 170 million tons per year, which will ensure a corresponding reduction in emissions by 620 million tons per year and the achievement by 2050 of the material balance of carbon in the emission - flow system. Under the most preferable scenario, it will be necessary to commission alternative replacement powers of about 160 GW per year; at the same time, the average global temperature will additionally rise by 0.6 С compared to the current one. The prospects and advantages of the development of the Russian hydraulic power industry as an environmentally and economically efficient alternative to coal projects are considered. In the emerging reality, Russian hydraulic power companies are advised to determine their ambitious share of the Russian quota for reducing emissions and commissioning 30 GW of replacement hydraulic power capacities by 2050 with additional electricity generation of up to 120 TWh per year.

Enhance the Plant Operating Capacity - Maximize the Revenue/Asset Utilization

To explore the opportunity for maximum utilization for a Sales Gas Compression Facility (SGCF) in line with ADNOC strategy to enhance profitability and asset utilization. A technical study was conducted to increase the processing capacity up to 133% of its design limit by utilizing the available design margins. This was to identify the potential bottlenecks in the facility and suggest debottlenecking options (if bottlenecks are there). The Technical study covered the following activities: Simulation: Process simulation was performed and H&MB (Heat and Material Balance) was generatd. Engineering: Compressor adequacy checks on increased plant throughputs. Static Equipment rating and adequacy checks performed with the concurrence of original equipment menufacturerers. Line sizing adequacy checks and detailed evaluation of the piping. Adequacy check for In-line instruments like control valves, flow elements/transmitters (Note 1) Relief, blowdown and flare system adequacy check. Utilities adequacy checks. Risk assessment workshop was conducted before the capacity test run. Preparation of Test Run procedure before the actual test run. Actual plant capacity test run to verify the study findings. Note 1: Adequacy check of thermowells had been peformed separately prior to the study. It had already been established that the thermowells were adequate for the increased plant throughputs. The study has concluded the following observations for processing 133% of the design capacity Theoratically, the Sales Gas Compression Plant is adequate to handle the sales gas throughput up to 600 MMSCFD (2 running machines) considering the facts that Sales Gas Compressor suction pressure must always be kept at 32 barg through close monitoring by the operaters.If compressor suction pressure starts dropping below 32 barg, the study outcome would no more valid and the plant throughput would be reduced back to the original design capacity of 450 MMSCFD. Moreover, it was recommended to perform a field test run to validate the study outcome by following the Manageement of Change Procedure as applicable. Based on the successful 48 hours test run, it was established that the facility could handle the increased plant throughput of 600 MMSCFD by following the instructions given in the adequacy study.

Physics Informed Neural Networks Based on a Capacitance Resistance Model for Reservoirs Under Water Flooding Conditions

In recent years great interest has risen towards surrogate reservoir models based on data-driven methodologies with the purpose of speeding up reservoir management decisions. In this work, a Physics Informed Neural Network (PINN) based on a Capacitance Resistance Model (CRM) has been developed and tested on a synthetic and on a real dataset to predict the production of oil reservoirs under waterflooding conditions. CRMs are simple models based on material balance that estimate the liquid production as a function of injected water and bottom hole pressure. PINNs are Artificial Neural Networks (ANNs) that incorporate prior physical knowledge of the system under study to regularize the network. A PINN based on a CRM is obtained by including the residual of the CRM differential equations in the loss function designed to train the neural network on the historical data. During training, weights and biases of the network and parameters of the physical equations, such as connectivity factors between wells, are updated with the backpropagation algorithm. To investigate the effectiveness of the novel methodology on waterflooded scenarios, two test cases are presented: a small synthetic one and a real mature reservoir. Results obtained with PINN are compared with respect to CRM and ANN alone. In the synthetic case CRM and PINN give slightly better quality history matches and predictions than ANN. The connectivity factors estimated by CRM and PINN are very similar and correctly represent the underlying geology. In the real case PINN gives better quality history matches and predictions than ANN, and both significantly outperform CRM. Even though the CRM formulation is too simple to predict the complex behavior of a real reservoir, the CRM based regularization contributes to improving the PINN predictions quality compared to the purely data-driven ANN model. The connectivity factors estimated by CRM and PINN are not in agreement. However, the latter method provided results closer to our understanding of the flooding process after many years of operations and data analysis. All considered, PINN outperformed both CRM and ANN in terms of predictivity and interpretability, effectively combining strengths from both methodologies. The presented approach does not require the construction of a 3D model since it learns directly from production data, while preserving physical consistency. Moreover, it represents a computationally inexpensive alternative to traditional full-physics reservoir simulations which could have vast applications for problems requiring many forward evaluations, like the optimization of water allocation for mature reservoirs.