Optimization of the Reservoir Management System and the ESP Operation Control Process by Means of Machine Learning on the Oilfields of Salym Petroleum Development N.V.

2021 ◽  
Author(s):  
Anastasia Dmitrievna Musorina ◽  
Grigory Sergeyevich Ishimbayev
Keyword(s):  
Machine Learning ◽  
Input Parameter ◽  
Pressure Sensors ◽  
Development Program ◽  
Gas Production ◽  
Measuring Instruments ◽  
Target Variable ◽  
Pump Design ◽  
Field Development ◽  
Operation Control

Abstract Under the present conditions of oil and gas production, which are characterized by mature production fields and the focus shifted towards digitalization of production processes and use of machine learning (ML) models, the issues related to the improvement of accuracy and consistency of the well operation control data are becoming increasingly important. As a result, SPD has successfully implemented the project of using annular pressure sensors in combination with machine learning models to control the well annular pressure as part of the field development program compliance. Under the field development program, echosounder and telemetry system readings are typically used to control the annular pressure and the dynamic flowing level. Echosounders, however, are not designed as measuring instruments, the accuracy of their readings being low and making it impossible to reliably evaluate the well's dynamic flowing level and annular pressure, as well as to achieve the well's maximum potential, and the telemetry systems used to measure the pump intake pressure may go wrong. This manuscript describes the approach to the producer well annular pressure assessment based on the machine learning model data. The machine learning (ML) model is a function of the target variable (bottom-hole pressure), which is predicted on the basis of the actual data: static parameters (well schematic, pump design) and dynamic parameters (annular and line pressures, flowrate). The input parameter interpretation results in the most probable value of the target variable based on the historic data.

Integrated Subsurface to Surface Modeling to Assess Reservoir Uncertainty Quantification to A Carbonate Reservoir

2020 ◽  
Author(s):  
A. Chaterine
Keyword(s):  
Uncertainty Quantification ◽  
Gas Production ◽  
Carbonate Reservoir ◽  
Production Volume ◽  
Production Forecast ◽  
Uncertainty Range ◽  
Field Development ◽  
Production Forecasting ◽  
Production Scheme ◽  
Rock Compressibility

This study accommodates subsurface uncertainties analysis and quantifies the effects on surface production volume to propose the optimal future field development. The problem of well productivity is sometimes only viewed from the surface components themselves, where in fact the subsurface component often has a significant effect on these production figures. In order to track the relationship between surface and subsurface, a model that integrates both must be created. The methods covered integrated asset modeling, probability forecasting, uncertainty quantification, sensitivity analysis, and optimization forecast. Subsurface uncertainties examined were : reservoir closure, regional segmentation, fluid contact, and SCAL properties. As the Integrated Asset Modeling is successfully conducted and a matched model is obtained for the gas-producing carbonate reservoir, highlights of the method are the following: 1) Up to ± 75% uncertainty range of reservoir parameters yields various production forecasting scenario using BHP control with the best case obtained is 335 BSCF of gas production and 254.4 MSTB of oil production, 2) SCAL properties and pseudo-faults are the most sensitive subsurface uncertainty that gives major impact to the production scheme, 3) EOS modeling and rock compressibility modeling must be evaluated seriously as those contribute significantly to condensate production and the field’s revenue, and 4) a proposed optimum production scenario for future development of the field with 151.6 BSCF gas and 414.4 MSTB oil that yields a total NPV of 218.7 MMUSD. The approach and methods implemented has been proven to result in more accurate production forecast and reduce the project cost as the effect of uncertainty reduction.

Knee Muscle Force Estimating Model Using Machine Learning Approach

2020 ◽  
Author(s):  
Anurag Sohane ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Muscle Force ◽  
Vastus Lateralis ◽  
Input Parameter ◽  
Research Work ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Muscle Forces ◽  
Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

Analytical Model of Micropyramidal Capacitive Pressure Sensors and Machine‐Learning‐Assisted Design

2021 ◽  
pp. 2100634
Author(s):  
Chao Ma ◽  
Gang Li ◽  
Longhui Qin ◽  
Weicheng Huang ◽  
Hongrui Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Analytical Model ◽  
Pressure Sensors

scholarly journals New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production

Fuels ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 286-303
Author(s):  
Vuong Van Pham ◽  
Ebrahim Fathi ◽  
Fatemeh Belyadi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Shale Gas ◽  
Field Data ◽  
Marcellus Shale ◽  
Hybrid Approach ◽  
Gas Production ◽  
Bayesian Optimization ◽  
Real Field ◽  
Engineering Problems

The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.

scholarly journals Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
History Matching ◽  
Optimization Problems ◽  
Learning Technologies ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Learning Approaches ◽  
Field Development ◽  
Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

scholarly journals Pervasive Lying Posture Tracking

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5953 ◽  
Author(s):  
Parastoo Alinia ◽  
Ali Samadani ◽  
Mladen Milosevic ◽  
Hassan Ghasemzadeh ◽  
Saman Parvaneh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computational Models ◽  
Learning Algorithms ◽  
Pressure Sensors ◽  
Machine Learning Algorithms ◽  
Sensor System ◽  
Accurate Detection ◽  
Research Questions ◽  
Posture Tracking

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

Artificial Intelligence Assisted Well Portfolio Optimization - An Automated Reservoir Management Advisory System to Maximize the Asset Value - Case Study from ADNOC Onshore

2021 ◽  
Author(s):  
Subba Ramarao Rachapudi Venkata ◽  
Nagaraju Reddicharla ◽  
Shamma Saeed Alshehhi ◽  
Indra Utama ◽  
Saber Mubarak Al Nuimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Simulation Models ◽  
Production Optimization ◽  
Added Value ◽  
Risk Scores ◽  
Well Performance ◽  
Field Development ◽  
Advisory System ◽  
Gas Lift

Abstract Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability. The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation. The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process. The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation. A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and rank the inactive strings, well conversion to gas lift candidates & underperforming candidates resulting into successful cost optimization and production gain.

scholarly journals ANTHROPOGENIC INFLUENCE OF THE KOMSOMOLSK OIL AND GAS CONDENSATE FIELD ON MODERN DEFORMATION PROCESSES

2018 ◽  
pp. 11-20 ◽  
Author(s):  
Yu. V. Vasilev ◽  
D. A. Misyurev ◽  
A. V. Filatov
Keyword(s):  
Oil And Gas ◽  
Gas Production ◽  
Gas Condensate ◽  
Industrial Safety ◽  
Subsidence Trough ◽  
Oil And Gas Production ◽  
Field Development ◽  
Repeated Observations ◽  
Deformation Processes ◽  
The Relationship

The authors created a geodynamical polygon on the Komsomolsk oil and gas condensate field to ensure the industrial safety of oil and gas production facilities. The aim of its creation is mul-tiple repeated observations of recent deformation processes. Analysis and interpretation of the results of geodynamical monitoring which includes class II leveling, satellite observations, radar interferometry, exploitation parameters of field development provided an opportunity to identify that the conditions for the formation of recent deformations of the earth’s surface is an anthropogenic factor. The authors identified the relationship between the formation of subsidence trough of the earth’s surface in the eastern part of the field with the dynamics of accumulated gas sampling and the fall of reservoir pressures along the main reservoir PK1 (Cenomanian stage).

scholarly journals Research on Gas-liquid Mixed Transportation Technological Adaptability: A Case Study of the Western Sichuan Gas Field

2017 ◽  
Vol 10 (1) ◽  
pp. 37-47
Author(s):  
Qingsha Zhou ◽  
Kun Huang ◽  
Yongchun Zhou
Keyword(s):  
Flow Simulation ◽  
Field Tests ◽  
Economic Benefits ◽  
Gas Production ◽  
Simulation Software ◽  
Rapid Decline ◽  
Gas Field ◽  
Western Sichuan ◽  
Field Development ◽  
Well Production

Background: The western Sichuan gas field belongs to the low-permeability, tight gas reservoirs, which are characterized by rapid decline in initial production of single-well production, short periods of stable production, and long periods of late-stage, low-pressure, low-yield production. Objective: It is necessary to continue pursuing the optimization of transportation processes. Method: This paper describes research on mixed transportation based on simplified measurements with liquid-based technology and the simulation of multiphase processes using the PIPEPHASE multiphase flow simulation software to determine boundary values for the liquid carrying process. Conclusion: The simulation produced several different recommendations for the production and maximum multiphase distance along with difference in elevation. Field tests were then conducted to determine the suitability of mixed transportation in western Sichuan, so as to ensure smooth progress with fluid metering, optimize the gathering process in order to achieve stable and efficient gas production, and improve the economic benefits of gas field development.

Feasibility Study on the Implementation of CO2-EOR Coupled with Sequestration in Trinidad via Reservoir Simulation

2014 ◽  
Author(s):  
J.. Narinesingh ◽  
D. V. Boodlal ◽  
D.. Alexander
Keyword(s):  
Primary Production ◽  
Oil Recovery ◽  
Net Present Value ◽  
Fluid Model ◽  
Gas Production ◽  
Economic Feasibility ◽  
Utilization Rate ◽  
Optimum Number ◽  
Field Development ◽  
Co2 Eor

Abstract The paper seeks to assess the technical and economic feasibility of implementing carbon dioxide enhanced oil recovery (CO2 EOR) in Trinidad and Tobago from flue gas production whilst mitigating the effect of greenhouse gases via CO2 sequestration. An existing power plant in Trinidad was selected as the CO2 source. As such, actual CO2 volumes and properties were found and used in this analysis. However, a hypothetical field was chosen as the appropriate sink, which can be analogous to a field in onshore Trinidad. A detailed reservoir model was built using the compositional fluid model CMG-GEM. Various scenarios were simulated to determine the optimum number of producers for primary production and the best location of the injectors for CO2 EOR. The optimum number of producers for the reservoir during primary production was found. In addition, the most favorable location of the injector to avoid early breakthrough and increase oil recovery was also determined. Many key parameters were reported from this investigation. These included OIIP, forecasted production and primary recovery. After primary production, CO2 EOR was then implemented with the use of the reservoir and fluid models and the additional recovery is reported. Other Key CO2-EOR parameters such as CO2 utilization rate and total sequestered CO2 were also quantified. Though a hypothetical reservoir was used, all associated data were defined and once an actual reservoir is known, the same technically rigid methodology can be applied. The OIIP was found to be 6.74 MMSTB for the selected reservoir. Based on an economic net present value (NPV) assessment, the optimum number of production wells for field development was found to be 3. At the end of primary production from these three wells (with 2.375 in. tubing), a total of 1.83 MMSTB were produced. This corresponded to a primary recovery factor of 27.2% over 4 years and 2 months. For CO2 EOR coupled with sequestration, these three wells were manipulated and used as 1 injector and 2 producers. CO2 EOR led to another 1.07 MMSTB of recovery for a total of 2.9 MMSTB (43.04% Recovery) for the ten year life of the project. A total of 5427 MMSCF (287 000 tons) of CO2 was sequestered in the reservoir (40.39% Storage) at an injection pressure of 1400 psi.

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