An ESP Production Optimization Algorithm Applied to Unconventional Wells

2021 ◽  
Author(s):  
Fernando Bermudez ◽  
Noor Al Nahhas ◽  
Hafsa Yazdani ◽  
Michael LeTan ◽  
Mohammed Shono
Keyword(s):  
Gas Production ◽  
Oil Wells ◽  
Production Optimization ◽  
Operating Conditions ◽  
Tight Oil ◽  
Daily Production ◽  
Free Gas ◽  
Maximum Production ◽  
Electrical Submersible Pumps ◽  
High Production

Abstract The objectives and Scope is to evaluate the feasibility of a Production Maximization algorithm for ESPs on unconventional wells using projected operating conditions instead of current ones, which authors expect will be crucial in adjusting the well deliverability to optimum frequencies on the rapidly changing conditions of tight oil wells. Actual production data for an unconventional well was used, covering from the start of Natural Flow production up to 120 days afterwards. Simulating what the production would be if a VFD running on IMP Optimization algorithms had been installed, new values for well flowing pressures were calculated, daily production scenarios were evaluated, and recommended operating frequencies were plotted. Result, observations, and conclusions: A. Using the Intelligent Maximum Production (IMP) algorithm allows maximum production from tight oil wells during the initial high production stage, and the prevention of gas-locking at later stages when gas production increases. B. The adjustment of frequency at later stages for GOR wells is key to maintaining maximum production while controlling free gas at the intake when compared against controlling the surface choke. Novel/additive information: The use of Electrical Submersible Pumps for the production of unconventional wells paired with the use of a VFD and properly designed control algorithms allows faster recovery of investment by pumping maximum allowable daily rates while constraining detrimental conditions such as free gas at the intake.

scholarly journals Experimental Study on the Characteristics of Adsorbed Gas and Gas Production in Shale Formations

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhiming Hu ◽  
Xianggang Duan ◽  
Nan Shao ◽  
Yingying Xu ◽  
Jin Chang ◽  
...  
Keyword(s):  
Shale Gas ◽  
Physical Simulation ◽  
Gas Production ◽  
Production Optimization ◽  
Gas Reservoirs ◽  
Tight Sandstone ◽  
Free Gas ◽  
Adsorbed Gas ◽  
Shale Gas Reservoirs ◽  
Shale Reservoirs

Adsorbed gas and free gas both exist in shale reservoirs simultaneously due to the unique nanoscale pore structure, resulting in the complex flow mechanism of gas in the reservoir during the development process. The dynamic performance analysis of shale reservoirs has mostly been conducted by the numerical simulation and theoretical model, while the physical simulation method for relevant research is seen rarely in the literature. Thus, in this paper, an experiment system was designed to simulate the degraded development experiments of shale, coal, and tight sandstone to reveal the output law of gas in different occurrence states of shale reservoirs and clarify the pressure propagation rules of different reservoirs, and then, adsorption gas and free gas production laws were studied by theoretical models. Research indicated the following: (1) The gas occurrence state is the main factor that causes the difference of the pressure drop rate and gas production law of shale, coal, and tight sandstone. During the early stage of the development of shale gas, the free gas is mainly produced; the final contribution of free gas production can reach more than 90%. (2) The static desorption and dynamic experiments confirm that the critical desorption pressure of adsorbed gas is generally between 12 and 15 MPa. When the gas reservoir pressure is lower than the critical desorption pressure in shale and coal formation, desorption occurs. Due to the slow propagation of shale matrix pressure, desorption of adsorbed gas occurs mainly in the low-pressure region close to the fracture surface. (3) The material balance theory of closed gas reservoirs and the one-dimensional flow model of shale gas have subsequently validated the production performance law of adsorbed gas and free gas by the physical simulation. Therefore, in the practical development of shale gas reservoirs, it is recommended to shorten the matrix supply distance, reduce the pressure in the fracture, increase the effective pressure gradient, and enhance the potential utilization of adsorbed gas as soon as possible to increase the ultimate recovery. The findings of this study can help for a better understanding of the shale reservoir utilization law so as to provide a reference for production optimization and development plan formulation of the shale gas reservoirs.

scholarly journals Evaluation of natural gas production optimization in Kailashtila gas field in Bangladesh using decline curve analysis method

2015 ◽  
Vol 50 (1) ◽  
pp. 29-38 ◽  
Author(s):  
MS Shah ◽  
HMZ Hossain
Keyword(s):  
Production System ◽  
Gas Production ◽  
Curve Analysis ◽  
Production Optimization ◽  
Reservoir Pressure ◽  
Gas Field ◽  
Wellhead Pressure ◽  
Decline Curve Analysis ◽  
Decline Curve ◽  
Overall Performance

Decline curve analysis of well no KTL-04 from the Kailashtila gas field in northeastern Bangladesh has been examined to identify their natural gas production optimization. KTL-04 is one of the major gas producing well of Kailashtila gas field which producing 16.00 mmscfd. Conventional gas production methods depend on enormous computational efforts since production systems from reservoir to a gathering point. The overall performance of a gas production system is determined by flow rate which is involved with system or wellbore components, reservoir pressure, separator pressure and wellhead pressure. Nodal analysis technique is used to performed gas production optimization of the overall performance of the production system. F.A.S.T. Virtu Well™ analysis suggested that declining reservoir pressure 3346.8, 3299.5, 3285.6 and 3269.3 psi(a) while signifying wellhead pressure with no changing of tubing diameter and skin factor thus daily gas production capacity is optimized to 19.637, 24.198, 25.469, and 26.922 mmscfd, respectively.Bangladesh J. Sci. Ind. Res. 50(1), 29-38, 2015

A Practical Approach to Expedite a Pore to Process Simulation Model for a Rich Gas Condensate Reservoir – Case Study

2021 ◽  
Author(s):  
Mohamed Ibrahim Mohamed ◽  
Ahmed Mahmoud El-Menoufi ◽  
Eman Abed Ezz El-Regal ◽  
Ahmed Mohamed Ali ◽  
Khaled Mohamed Mansour ◽  
...  
Keyword(s):  
Simulation Model ◽  
Production Optimization ◽  
Operating Conditions ◽  
Gas Condensate ◽  
Western Desert ◽  
Design Parameters ◽  
Compositional Approach ◽  
Process Plant ◽  
Black Oil

Abstract Field development planning of gas condensate fields using numerical simulation has many aspects to consider that may lead to a significant impact on production optimization. An important aspect is to account for the effects of network constraints and process plant operating conditions through an integrated asset model. This model should honor proper representation of the fluid within the reservoir, through the wells and up to the network and facility. Obaiyed is one of the biggest onshore gas field in Egypt, it is a highly heterogeneous gas condensate field located in the western desert of Egypt with more than 100 wells. Three initial condensate gas ratios are existing based on early PVT samples and production testing. The initial CGRs as follows;160, 115 and 42 STB/MMSCF. With continuous pressure depletion, the produced hydrocarbon composition stream changes, causing a deviation between the design parameters and the operating parameters of the equipment within the process plant, resulting in a decrease in the recovery of liquid condensate. Therefore, the facility engineers demand a dynamic update of a detailed composition stream to optimize the system and achieve greater economic value. The best way to obtain this compositional stream is by using a fully compositional integrated asset model. Utilizing a fully compositional model in Obaiyed is challenging, computationally expensive, and impractical, especially during the history match of the reservoir numerical model. In this paper, a case study for Obaiyed field is presented in which we used an alternative integrated asset modeling approach comprising a modified black-oil (MBO) that results in significant timesaving in the full-field reservoir simulation model. We then used a proper de-lumping scheme to convert the modified black oil tables into as many components as required by the surface network and process plant facility. The results of proposed approach are compared with a fully compositional approach for validity check. The results clearly identified the system bottlenecks. The model can be used to propose the best tie-in location of future wells in addition to providing first-pass flow assurance indications throughout the field's life and under different network configurations. The model enabled the facility engineers to keep the conditions of the surface facility within the optimized operating envelope throughout the field's lifetime.

scholarly journals IMPROVE THE PYROLYTIC GAS PRODUCTION BY USING THE CO-PYROLYSIS TECHNIQUE : AN EXPERIMENTAL STUDY

2021 ◽  
Vol 21 (2) ◽  
Author(s):  
Abo . Zahra A.I ◽  
M.K. Abd El- Wahab ◽  
M.A. Tawfik
Keyword(s):  
Energy Conversion ◽  
Fluidized Bed ◽  
Conversion Efficiency ◽  
Fixed Bed ◽  
Energy Conversion Efficiency ◽  
Gas Production ◽  
Operating Conditions ◽  
Energy Unit ◽  
Heating Value ◽  
Slow Pyrolysis

The target of the biomass co-pyrolysis is improvingthe heating value of the produced bio-products of a certain type of feedstock, besides disposal of more than one residue in the same time. Thus, this work aims to operate a local fabricated fixed-bed pyrolyzer to improve the pyrolytic gas yield produced by the ground pieces of three biomass residues namely Mango trees Pruning Logs (MPL), Sugarcane bagasse (SB) and Rice straw (RS) using an affordable slow pyrolysis technique. This work was carried out under slow pyrolysis conditions represented in final pyrolysis temperature of 400 °C, vapor residence time of 4 min, heating rate of 0.01-1 °C/s in full absence of oxygen. The pyrolytic gas production was assessed under different feedstock mixing ratios of (1:2:1), (1:1:2) and (2:1:1) as ratio of (RS: SB: MPL), particle lengths of 1-5, 10-15 and 20-25 mm, with and without sandy bed at the bottom of pyrolysis chamber as a fluidized bed. The obtained results showed that, using the fluidized fixed-bed pyrolyzer under slow co-pyrolysis conditions gave the optimum results where in, the pyrolytic gas concentration, gas yield, higher heating value of pyrolytic gasand energy conversion efficiency were 55%, 1.09 Nm3 /kg, 14.97 MJ/Nm3 and 85.43%, respectively, and 53.7%, 1.08 Nm3 /kg, 13.75 MJ/Nm3 ,77.71% in case of using the pyrolyzer without fluidized bed under the same operating conditions. So, the pyrolyzer with fluidized bed achieves an increment in the higher heating value and energy conversion efficiency by about 8.15% and 9.03%, respectivly over the pyrolyzer without fluidized bed.Furthermore, the cost per energy unit of pyrolytic gas produced by the fluidized bed pyrolyzer is lower than the common two fossil gaseous fuels of natural gas and LPG costs by about 28.57% and 80%, respectively.

AI for Production Forecasting and Optimization of Gas Wells: A Case Study on a Middle-East Gas Field

2021 ◽  
Author(s):  
Jimmy Thatcher ◽  
Abdul Rehman ◽  
Ivan Gee ◽  
Morgan Eldred
Keyword(s):  
Machine Learning ◽  
Optimal Parameter ◽  
Gas Production ◽  
Production Optimization ◽  
Machine Learning Techniques ◽  
Gas Wells ◽  
Optimal Production ◽  
Unsupervised Machine Learning ◽  
Production Forecasting ◽  
Run Time

Abstract Oil & Gas extraction companies are using a vast amount of capital and expertise on production optimization. The scale and diversity of information required for analysis is massive and often leading to a prioritization between time and precision for the teams involved in the process. This paper provides a success story of how artificial intelligence (AI) is used to dynamically and effeciently optimize and predict production of gas wells. In particular, we focus on the application of unsupervised machine learning to identify under different potential constraints the optimal production parameter settings that can lead to maximum production. A machine learning model is supported by a decision support system that can enhance future drilling operations and also help answer important questions such as why a particular well or group of wells is producing differently than others of the same type or what kind of parameters that work on different wells in different conditions. The model can be advanced to optimize within field constraints such as facility handling capacity, quotas, budget or emmisions. The methods used were a combination of similarity measures and unsupervised machine learning techniques which were effective in identifying wells and clusters of wells that have similar production and behavioral profiles. The clusters of wells were then used to identify the process path (specific drilling and completion, choke size, chemicals, etc processes) most likely to result in optimal production and to identify the most impactful variables on production rate or cumulative production via an additional clustering of the principle charactersitics of the well. The data sets used to build these models include but are not limited to gas production data (daily volume), drilling data (well logs, fluid summary etc.), completion data (frac, cement bond logs), and pre-production testing data (choke, pressure etc.) Initial results indicate that this approach is a feasible approach, on target in terms of accuracy with traditional methods and represents a novel, data driven, method of identifying optimal parameter settings for desired production levels; with the ability to perform forecasts and optimization scenarios in run-time. The approach of using machine learning for production forecasting and production optimization in run-time has immense values in terms of the ability to augment domain expertise and create detailed studies in a fraction of the time that is typically required using traditional approaches. Building on same approach to optimise the field to deliver most reliable or most effeciently against a parameter will be an invaluable feature for overall asset optimisation.

Corrosion and Wear Resistant Boronizing for Tubulars and Components Used Down-Hole

2021 ◽  
Author(s):  
Roger Machado ◽  
Paola Andrea de Sales Bastos ◽  
Danny Daniel Socorro Royero ◽  
Eugene Medvedovski
Keyword(s):  
Oil And Gas ◽  
Chemical Vapour ◽  
Protective Layer ◽  
Gas Production ◽  
Operating Conditions ◽  
Material Loss ◽  
Oil And Gas Production ◽  
Dimensional Changes ◽  
Wear And Corrosion ◽  
Service Conditions

Abstract Components and tubulars in down-hole applications for oil and gas production must withstand severe wear (e.g. erosion, abrasion, rod wear) and corrosion environments. These challenges can be addressed through boronizing of steels achieved employing chemical vapour deposition-based process. This process permits protection of the entire working surfaces of production tubulars up to 12m in length, as well as various sizes of complex shaped components. The performance of these tubulars and components have been evaluated in abrasion, erosion, and corrosion conditions simulating the environment and service conditions experienced in down-hole oil and gas production. Harsh service conditions are very common in the oil industry and the combination of abrasion, friction-induced wear, erosion, and corrosion environments can be quite normal in wells producing with the assistance of artificial lift methods. The boronized steel products demonstrated significantly higher performance in terms of material loss when exposed to harsh operating conditions granting a significant extension of the component service life in wear and corrosion environments. As opposed to many coating technologies, the boronizing process provides high integrity finished products without spalling or delamination on the working surface and minimal dimensional changes. Successful application of tubulars and components with the iron boride protective layer in oil and gas production will be discussed and presented.

Production Optimization in Mature Field Through Scenario Prediction Using a Representative Network Model: A Rapid Solution Without Well Intervention

2021 ◽  
Author(s):  
Edwin Lawrence ◽  
Marie Bjoerdal Loevereide ◽  
Sanggeetha Kalidas ◽  
Ngoc Le Le ◽  
Sarjono Tasi Antoneus ◽  
...  
Keyword(s):  
Network Model ◽  
Test Data ◽  
Production Optimization ◽  
Operating Conditions ◽  
Fine Tuning ◽  
Well Test ◽  
Production Rates ◽  
Mature Field ◽  
Gas Lift ◽  
Field Production

Abstract As part of the production optimization exercise in J field, an initiative has been taken to enhance the field production target without well intervention. J field is a mature field; the wells are mostly gas lifted, and currently it is in production decline mode. As part of this optimization exercise, a network model with multiple platforms was updated with the surface systems (separator, compressors, pumps, FPSO) and pipelines in place to understand the actual pressure drop across the system. Modelling and calibration of the well and network model was done for the entire field, and the calibrated model was used for the production optimization exercise. A representative model updated with the current operating conditions is the key for the field production and asset management. In this exercise, a multiphase flow simulator for wells and pipelines has been utilized. A total of ∼50 wells (inclusive of idle wells) has been included in the network model. Basically, the exercise started by updating the single-well model using latest well test data. During the calibration at well level, several steps were taken, such as evaluation of historical production, reservoir pressure, and well intervention. This will provide a better idea on the fine-tuning parameters. Upon completion of calibrating well models, the next level was calibration of network model at the platform level by matching against the platform operating conditions (platform production rates, separator/pipeline pressure). The last stage was performing field network model calibration to match the overall field performance. During the platform stage calibration, some parameters such as pipeline ID, horizontal flow correlation, friction factor, and holdup factor were fine-tuned to match the platform level operating conditions. Most of the wells in J field have been calibrated by meeting the success criterion, which is within +/-5% for the production rates. However, there were some challenges in matching several wells due to well test data validity especially wells located on remote platform where there is no dedicated test separator as well as the impact of gas breakthrough, which may interfere to performance of wells. These wells were decided to be retested in the following month. As for the platform level matching, five platforms were matched within +/-10% against the reported production rates. During the evaluation, it was observed there were some uncertainties in the reported water and gas rates (platform level vs. well test data). This is something that can be looked into for a better measurement in the future. By this observation, it was suggested to select Platform 1 with the most reliable test data as well as the platform rate for the optimization process and qualifying for the field trial. Nevertheless, with the representative network model, two scenarios, reducing separator pressure at platform level and gas lift optimization by an optimal gas lift rate allocation, were performed. The model predicts that a separator pressure reduction of 30 psi in Platform 1 has a potential gain of ∼300 BOPD, which is aligned with the field results. Apart from that, there was also a potential savings in gas by utilizing the predicted allocated gas lift injection rate.

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