Machine Learning Interpretability Application to Optimize Well Completion in Montney

Author(s):  
Yousef Sheikhi Garjan ◽  
Mehdi Ghaneezabadi
2019 ◽  
Author(s):  
Paolo Dell'Aversana ◽  
Raffaele Servodio ◽  
Franco Bottazzi ◽  
Carlo Carniani ◽  
Germana Gallino ◽  
...  

2021 ◽  
Vol 73 (04) ◽  
pp. 41-41
Author(s):  
Doug Lehr

In the 2020 Completions Technology Focus, I stated that digitization will forever change how the most complex problems in our industry are solved. And, despite another severe downturn in the upstream industry, data science continues to provide solutions for complex unconventional well problems. Casing Damage Casing collapse is an ongoing problem and almost always occurs in the heel of the well. It prevents passage of frac plugs and milling tools. Forcing a frac plug through the collapsed section damages the plug, predisposing it to failure, which leads to more casing damage and poor stimulation. One team has developed a machine-learning (ML) model showing a positive correlation between zones with high fracturing gradients and collapsed casing. The objective is a predictive tool that enables a completion design that avoids these zones. Fracture-Driven Interactions (FDIs) Can Be Avoided in Real Time Pressurized fracturing fluids from one well can communicate with fractures in a nearby well or can intersect that well-bore. Such FDIs can occur while fracturing a child well and can negatively affect production in the parent well. FDIs are caused by well spacing, depletion, or completion design but, until recently, were not quickly diagnosed. Analytics and machine learning now are being used to analyze streaming data sets during a frac job to detect FDIs. A recently piloted detection system alerts the operator in real time, which enables avoidance of FDIs on the fly. Data Science Provides the Tools Analyzing casing damage and FDIs is a complex task involving large amounts of data already available or easily acquired. Tools such as ML perform the data analysis and enable decision making. Data science is enabling the unconventional “onion” to be peeled many layers at a time. Recommended additional reading at OnePetro: www.onepetro.org. SPE 199967 - Artificial Intelligence for Real-Time Monitoring of Fracture-Driven Interactions and Simultaneous Completion Optimization by Hayley Stephenson, Baker Hughes, et al. SPE 201615 - Novel Completion Design To Bypass Damage and Increase Reservoir Contact: A Middle Magdalena, Central Colombian Case History by Rosana Polo, Oxy, et al. SPE 202966 - Well Completion Optimization in Canada Tight Gas Fields Using Ensemble Machine Learning by Lulu Liao, Sinopec, et al.


2021 ◽  
Vol 73 (04) ◽  
pp. 42-43
Author(s):  
Chris Carpenter

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201699, “Predicting Trouble Stages With Geomechanical Measurements and Machine Learning: A Case Study of Southern Midland Basin Horizontal Completions,” by Eric Romberg, SPE, Keban Engineering; Aaron Fisher, Tracker Resources; and Joel Mazza, SPE, Fracture ID, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, 5–7 October. The paper has not been peer reviewed. Unexpected problems during completion create costs that can cause a well to be outside its planned authorization for expenditure, even uneconomic. These problems range from experiencing abnormally high pressures during treatment to casing failures. The authors of the complete paper use machine-learning methods combined with geomechanical, wellbore-trajectory, and completion data sets to develop models that predict which stages will experience difficulties during completion. Field Modeling and Well Planning The operator’s acreage is in the southeastern portion of the Midland Basin. In this area of the basin, the Wolfcamp B and C intervals often contain a significant amount of slope sediments and carbonate debris flows because of the proximity of the eastern shelf. These intervals cause significant drilling and completion issues. During the past 5 years, the operator acquired and licensed approximately 130 sq mile of 3D seismic data. In addition, the operator cored three wells, drilled six pilot wells with complete log suites, licensed 40 wells with a triple/quad combination, acquired data and surveys on 112 existing horizontal wells, and has 347 vertical wells with formation tops for depth control. This rich data set yielded a robust 3D reservoir model that was used to map a sequence of stacked, high-quality landing targets. Model-Aided Well-Completion Strategy. The operator often encountered difficult stages in the form of high breakdown pressures, high pumping pressures, and the inability to place proppant. On a few occasions, drilling out all plugs was not possible because of casing obstructions possibly related to fault activation during the stimulation. The operator began analyzing curvature and similarity volumes for potential fault/fracture identification near the difficult completion stages and compromised casing intervals. Drillbit geomechanics data collection was planned for all lateral wells. The geomechanical properties recorded were used to reduce risks during completions further by informing the plug and perforation stage design. Stages were planned to reduce variation in minimum horizontal stress (Shmin) within each stage. The geomechanical data also identified carbonate debris flows within the well path, allowing completion engineers to bypass rock considered unproductive. Completion Issues and Other Factors Contributing to Casing Deformation. From February 2017 through November 2019, the operator drilled and completed 28 Wolfcamp horizontal wells. The plug-and-perforation completion technique was used on all 28 wells. While drilling out composite fracturing plugs, casing obstruction was encountered in six of 28 wells. These obstructions limited the working internal diameter of the production casing and either prevented or inhibited access beyond the obstruction. In two of the Phase 1 wells, conventional drillout assemblies were not able to pass the obstructions.


Author(s):  
S.I. Gabitova ◽  
L.A. Davletbakova ◽  
V.Yu. Klimov ◽  
D.V. Shuvaev ◽  
I.Ya. Edelman ◽  
...  

The article describes new decline curves (DC) forecasting method for project wells. The method is based on the integration of manual grouping of DC and machine learning (ML) algorithms appliance. ML allows finding hidden connections between features and the output. Article includes the decline curves analysis of two well completion types: horizontal and slanted wells, which illustrates that horizontal wells are more effective than slanted.


SPE Journal ◽  
2020 ◽  
Vol 25 (03) ◽  
pp. 1241-1258 ◽  
Author(s):  
Ruizhi Zhong ◽  
Raymond L. Johnson ◽  
Zhongwei Chen

Summary Accurate coal identification is critical in coal seam gas (CSG) (also known as coalbed methane or CBM) developments because it determines well completion design and directly affects gas production. Density logging using radioactive source tools is the primary tool for coal identification, adding well trips to condition the hole and additional well costs for logging runs. In this paper, machine learning methods are applied to identify coals from drilling and logging-while-drilling (LWD) data to reduce overall well costs. Machine learning algorithms include logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGBoost). The precision, recall, and F1 score are used as evaluation metrics. Because coal identification is an imbalanced data problem, the performance on the minority class (i.e., coals) is limited. To enhance the performance on coal prediction, two data manipulation techniques [naive random oversampling (NROS) technique and synthetic minority oversampling technique (SMOTE)] are separately coupled with machine learning algorithms. Case studies are performed with data from six wells in the Surat Basin, Australia. For the first set of experiments (single-well experiments), both the training data and test data are in the same well. The machine learning methods can identify coal pay zones for sections with poor or missing logs. It is found that rate of penetration (ROP) is the most important feature. The second set of experiments (multiple-well experiments) uses the training data from multiple nearby wells, which can predict coal pay zones in a new well. The most important feature is gamma ray. After placing slotted casings, all wells have coal identification rates greater than 90%, and three wells have coal identification rates greater than 99%. This indicates that machine learning methods (either XGBoost or ANN/RF with NROS/SMOTE) can be an effective way to identify coal pay zones and reduce coring or logging costs in CSG developments.


2021 ◽  
Author(s):  
Lin Liang ◽  
◽  
Ting Lei ◽  
Matthew Blyth ◽  
◽  
...  

Logging-while-drilling (LWD) dipole sonic tools have been introduced to the industry as a supplement to monopole and quadrupole measurement because they can provide shear slowness anisotropy, which is essential for formation characterization and well completion applications. Due to the presence of the collar, which acts as a strong waveguide, the recorded formation signal is significantly affected at low frequencies. Consequently, an automated interpretation of LWD dipole sonic data re-mains a challenge. The traditional dispersive semblance-based method requires accurate estimates of parameters such as borehole size and/or mud slowness to avoid bias in the dispersion model used in the processing. Recently, a frequency-slowness domain inversion scheme has been developed that can invert for both the formation shear slowness and mud slowness by minimizing the guidance-mismatch cost function. However, this method uses an isotropic dispersion model and requires selecting narrow-band dispersion data in the low-frequency range with good-quality, which can limit the range of applicability of the method and also requires user input through-out the process. We have previously developed a physics-driven machine learning-based method to enhance the interpretation of wireline dipole sonic data. However, the LWD scenario introduces additional complexity. This work extends the method to support the interpretation of LWD dipole sonic. An anisotropic root-finding mode-search algorithm is first used to generate extensive synthetic formation flexural dispersion curves that can match dispersion measurements in strong anisotropic formations in high-angle and horizontal wells, with a known tool model. Special care needs to be taken to pick the formation flexural mode from several co-existing modes arising from the strong coupling between tool and formation. After quality control and verification, this comprehensive synthetic dataset is used to train a neural network model. We then develop an inversion-based algorithm, taking advantage of this efficient neural network model and combining it with a clustering algorithm, to reliably label and ex-tract the formation flexural mode, processed from either the modified Prony’s method, or a broadband dispersion analysis algorithm. The extraction around the formation flexural kick-in frequency is used for developing a quality control method. The strongest collar arrival, on the other hand, can be confidently removed due to the fundamental difference in its dispersion characteristics from the formation flexural mode. This novel method can automatically and efficiently label the formation flexural mode and simultaneously invert it for formation shear slowness together with other relevant parameters such as mud slowness without user intervention. Since this method is built upon an anisotropic model, it can be applied to the full frequency range of the data spectrum without the traditional isotropic model assumption. Additionally, the regression analysis of the inverted mud slownesses can further provide physical constraint to reduce uncertainties in the inverted shear slowness. The algorithm has been tested on field data showing good performance. It makes edge deployment possible so that LWD telemetry can be optimized to transmit the processed data to the surface in real-time, which is essential to leverage the advantages of the conveyance method.


2021 ◽  
Author(s):  
Sohrat Baki ◽  
Cenk Temizel ◽  
Serkan Dursun

Abstract Unconventional reservoirs, mainly shale oil and natural gas, will continue to significantly help meet the ever-growing energy demands of global markets. Being complex in nature and having ultra-tight producing zones, unconventionals depends on effective well completion and stimulation treatments in order to be successful and economical. Within the last decade, thousands of unconventional wells have been drilled, completed and produced in North America. The scope of this work is exploring the primary impact of completion parameters such as lateral length, frac type, number of stages, proppant and fluid volume effect on the production performance of the wells in unconventional fields. The key attributes in completion, stimulation, and production for the wells were considered in machine learning workflow for building predictive models. Predictive models based on Neural Networks, Support Vector Machines or Decision Tree Based ensemble models, serves as mapping function from completion parameters to production in each well in the field. The completion parameters were analyzed in the workflow with respect to feature engineering and interpretation. This analysis resulted in key performance indicators for the region. Then the optimum values for the best production performing completions were identified for each well. Predictive models in the workflow were analyzed in accuracy and best model is used to understand the impact of completion parameters on the production rates. This study outlines an overall machine learning workflow, from feature engineering to interpretation of the machine learning models to quantify the effects of completion parameters on the production rate of the wells in unconventional fields


2020 ◽  
Author(s):  
Lulu Liao ◽  
Gensheng Li ◽  
Hongbao Zhang ◽  
Jiangpeng Feng ◽  
Yijin Zeng ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document