scholarly journals General Optimization Model of Modular Equipment Selection and Serialization for Shale Gas Field

2021 ◽  
Vol 9 ◽  
Author(s):  
Bingyuan Hong ◽  
Xiaoping Li ◽  
Xuemeng Cui ◽  
Jingjing Gao ◽  
Yu Li ◽  
...  
Keyword(s):  
Shale Gas ◽  
Optimization Model ◽  
Supply And Demand ◽  
Gas Production ◽  
Equipment Management ◽  
Gas Field ◽  
Actual Case ◽  
Processing Cost ◽  
Green Production ◽  
Productivity Prediction

The potential technical and economic advantages and flexible operability of modular equipment make it increasing widely used in gas field production and development. In addition to considering the manufacturing process, the selection and serialization of modular equipment should be made according to the productivity prediction of a gas well, so as to meet the field demand to the greatest extent and enhance the flexibility of gathering and transportation system. This article proposes a method to determine the use planning of modular equipment in shale gas field. Considering the processing capacity, processing cost, floor area, construction cost of modular equipment, and the changes of market supply and demand, an optimization model is established. On the basis of the abovementioned model, the method of serialization of modular equipment is proposed. The effectiveness of the model is verified by an actual case study. It is proved that the model can optimize the layout of modular equipment, make the modular equipment run efficiently and economically, reduce costs, and increase efficiency. This study provides a reference for optimizing the equipment management strategy and promoting green production practice of shale gas production.

Comprehensive Analysis of Production Loggings in Fuling Shale Gas Play in China

2021 ◽  
Author(s):  
Yaowen Liu ◽  
Wei Pang ◽  
Jincai Shen ◽  
Ying Mi
Keyword(s):  
Shale Gas ◽  
Mineral Content ◽  
Horizontal Wells ◽  
Organic Content ◽  
Gas Production ◽  
Liquid Loading ◽  
Comprehensive Overview ◽  
Gas Field ◽  
Well Trajectory ◽  
Positive Effect

Abstract Fuling shale gas field is one of the most successful shale gas play in China. Production logging is one of the vital technologies to evaluate the shale gas contribution in different stages and different clusters. Production logging has been conducted in over 40 wells and most of the operations are successful and good results have been observed. Some previous studies have unveiled one or several wells production logging results in Fuling shale gas play. But production logging results show huge difference between different wells. In order to get better understanding of the results, a comprehensive overview is carried out. The effect of lithology layers, TOC (total organic content), porosity, brittle mineral content, well trajectory is analyzed. Results show that the production logging result is consistent with the geology understanding, and fractures in the favorable layers make more gas contribution. Rate contribution shows positive correlation with TOC, the higher the TOC, the greater the rate contribution per stage. For wells with higher TOC, the rate contribution difference per stage is relatively smaller, but for wells with lower TOC, it shows huge rate contribution variation, fracture stages with TOC lower than 2% contribute very little, and there exist one or several dominant fractures which contributes most gas rate. Porosity and brittle minerals also show positive effect on rate contribution. The gas rate contribution per fracture stage increases with the increase of porosity and brittle minerals. The gas contribution of the front half lateral and that of latter half lateral are relatively close for the "upward" or horizontal wells. However, for the "downward" wells, the latter half lateral contribute much more gas than the front half lateral. It is believed that the liquid loading in the toe parts reduced the gas contribution in the front half lateral. The overview research is important to get a compressive understanding of production logging and different fractures’ contribution in shale gas production. It is also useful to guide the design of horizontal laterals and fractures scenarios design.

Investment optimization model for freshwater acquisition and wastewater handling in shale gas production

AIChE Journal ◽  
2015 ◽  
Vol 61 (6) ◽  
pp. 1770-1782 ◽  
Author(s):  
Linlin Yang ◽  
Ignacio E. Grossmann ◽  
Meagan S. Mauter ◽  
Robert M. Dilmore
Keyword(s):  
Shale Gas ◽  
Optimization Model ◽  
Gas Production ◽  
Investment Optimization

scholarly journals Technoeconomic analysis of shale gas gross split contract in Naintupo Formation, Tarakan Basin

2018 ◽  
Vol 67 ◽  
pp. 01003
Author(s):  
Wike Widyanita ◽  
Nelson Saksono
Keyword(s):  
Shale Gas ◽  
Supply And Demand ◽  
Economic Indicator ◽  
High Flow ◽  
Low Flow ◽  
Flow Profile ◽  
Gas Field ◽  
Field Development ◽  
Technoeconomic Analysis ◽  
Decline Curves

The deficit of natural gas supply and demand could be minimized by discovering new reserves in conventional or unconventional reservoir. Shale gas potential in Indonesia was estimated 574 TCF and Naintupo Formation in Tarakan Basin had 5 TCF of technically recoverable reserve with 35 TCF risked gas-in-place. This study would discuss technoeconomic aspect of shale gas field development in Naintupo Formation, Tarakan Basin using gross split contract scheme. Three flow profiles would be developed by using Arps hyperbolic decline curves, consist of low flow profile with initial production (qi) of 150 mmcf/mo, medium (qi = 250 mmcf/mo) and high flow profile (qi = 350 mmcf/mo). Costs estimation were based on benchmarking cost of developed shale gas field in United States and nearby oil/gas field development in Tarakan Basin. Economic analysis showed that medium and high flow profile gave positive economic indicator marked by positive NPV and IRR>10%. Sensitivity analysis showed that flow profile gave more effect in NPV and IRR increased than the gas price. In order to develop positive NPV with discount rate of 10%, it is required to sell shale gas at $6.52/MMBTU in high flow profile or $8.42/MMBTU in medium flow profile.

scholarly journals Productivity Prediction of Fractured Horizontal Well in Shale Gas Reservoirs with Machine Learning Algorithms

2021 ◽  
Vol 11 (24) ◽  
pp. 12064
Author(s):  
Tianyu Wang ◽  
Qisheng Wang ◽  
Jing Shi ◽  
Wenhong Zhang ◽  
Wenxi Ren ◽  
...  
Keyword(s):  
Neural Network ◽  
Relative Error ◽  
Shale Gas ◽  
Gas Production ◽  
Average Relative Error ◽  
Maximum Relative Error ◽  
Gas Reservoirs ◽  
Lstm Network ◽  
Relative Errors ◽  
Productivity Prediction

Predicting shale gas production under different geological and fracturing conditions in the fractured shale gas reservoirs is the foundation of optimizing the fracturing parameters, which is crucial to effectively exploit shale gas. We present a multi-layer perceptron (MLP) network and a long short-term memory (LSTM) network to predict shale gas production, both of which can quickly and accurately forecast gas production. The prediction performances of the networks are comprehensively evaluated and compared. The results show that the MLP network can predict shale gas production by geological and fracturing reservoir parameters. The average relative error of the MLP neural network is 2.85%, and the maximum relative error is 12.9%, which can meet the demand of engineering shale gas productivity prediction. The LSTM network can predict shale gas production through historical production under the constraints of geological and fracturing reservoir parameters. The average relative error of the LSTM neural network is 0.68%, and the maximum relative error is 3.08%, which can reliably predict shale gas production. There is a slight deviation between the predicted results of the MLP model and the true values in the first 10 days. This is because the daily production decreases rapidly during the early production stage, and the production data change greatly. The largest relative errors of LSTM in this work on the 10th, 100th, and 1000th day are 0.95%, 0.73%, and 1.85%, respectively, which are far lower than the relative errors of the MLP predictions. The research results can provide a fast and effective mean for shale gas productivity prediction.

scholarly journals Integrated Approach to Drilling Project in Unconventional Reservoir Using Reservoir Simulation

2018 ◽  
Vol 35 ◽  
pp. 01002
Author(s):  
Jerzy Stopa ◽  
Rafał Wiśniowski ◽  
Paweł Wojnarowski ◽  
Damian Janiga ◽  
Krzysztof Skrzypaszek
Keyword(s):  
Shale Gas ◽  
Reservoir Simulation ◽  
Integrated Approach ◽  
Gas Production ◽  
Full Scale ◽  
Production Optimization ◽  
Unconventional Reservoir ◽  
Gas Field ◽  
Technical Factor ◽  
Drilling Project

Accumulation and flow mechanisms in unconventional reservoir are different compared to conventional. This requires a special approach of field management with drilling and stimulation treatments as major factor for further production. Integrated approach of unconventional reservoir production optimization assumes coupling drilling project with full scale reservoir simulation for determine best well placement, well length, fracturing treatment design and mid-length distance between wells. Full scale reservoir simulation model emulate a part of polish shale – gas field. The aim of this paper is to establish influence of technical factor for gas production from shale gas field. Due to low reservoir permeability, stimulation treatment should be direct towards maximizing the hydraulic contact. On the basis of production scenarios, 15 stages hydraulic fracturing allows boost gas production over 1.5 times compared to 8 stages. Due to the possible interference of the wells, it is necessary to determine the distance between the horizontal parts of the wells trajectories. In order to determine the distance between the wells allowing to maximize recovery factor of resources in the stimulated zone, a numerical algorithm based on a dynamic model was developed and implemented. Numerical testing and comparative study show that the most favourable arrangement assumes a minimum allowable distance between the wells. This is related to the volume ratio of the drainage zone to the total volume of the stimulated zone.

Induced or triggered? The deadly February 2019 Rongxian-Weiyuan ML 4.9 earthquake in the shale gas field in Sichuan, China

2020 ◽  
Author(s):  
Hongfeng Yang ◽  
Pengcheng Zhou ◽  
Nan Fang ◽  
Gaohua Zhu ◽  
Wenbin Xu ◽  
...  
Keyword(s):  
Shale Gas ◽  
Fault Injection ◽  
Stress Transfer ◽  
Gas Production ◽  
Failure Stress ◽  
Injection Wells ◽  
Gas Field ◽  
Coulomb Failure Stress ◽  
Coulomb Failure Stress Change ◽  
Coulomb Failure

<p>Coinciding with the extensive hydraulic fracturing activities in the southern Sichuan basin, seismicity in the region has surged in the past a few years, including a number of earthquakes with magnitudes larger than 5. On 25 February 2019, an M<sub>L</sub>4.9 earthquake struck the Rongxian County, Sichuan, China and caused 2 fatalities and 12 injuries, the first deadly earthquake associated with shale gas production. The earthquake was preceded by two foreshocks with magnitudes of M<sub>L</sub>4.7 and M<sub>L</sub>4.3 within two days. We relocated the earthquake sequence using local and regional seismic network, and obtained the focal depths of the mainshock and two foreshocks at 1 and 3 km, respectively, much shallower than the report from catalogue. Most other smaller quakes were located at 2-6 km. The mainshock had also been well captured by InSAR images, which confirmed the shallow depth of ~1 km. Both seismic and geodetic data yielded thrust faulting mechanism for the mainshock, consistent with the mapped Molin fault in the region. The two foreshocks, however, occurred on an unmapped fault that has different orientation than the Molin fault. Injection wells are found in the vicinity of the two foreshocks and the fracking depth (~2.7 km) coincides with their focal depths, suggesting a possible causal relationship. The mainshock is located in the region with positive Coulomb failure stress caused the two foreshocks. The value of Coulomb failure stress change is 0.03 bar, smaller than the typical static triggering threshold. Therefore, the mainshock is likely caused by fracking by poroelastic stress transfer.</p>

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.

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