Surface Drilling Data Research Articles

Geomechanical Parameter Derivation while Drilling in Unconventional Plays: a Combination of Surface Drilling Data, Gamma Ray Data, and Machine Learning Techniques

Geomechanical Parameter Derivation while Drilling in Unconventional Plays: a Combination of Surface Drilling Data, Gamma Ray Data, and Machine Learning Techniques

Machine Learning Solution for Predicting Vibrations while Drilling the Curve Section.

The downhole vibration is one of the most crucial factors that affect downhole equipment performance and failure, besides wellbore instability. Downhole tool failure, hole problems, mechanical energy loss, and ineffective drilling performance are commonly associated with drillstring high vibration levels. The high vibration level will lead to more complications while drilling that might cause nonproductive time and extra cost. Meanwhile, the downhole sensors for detecting the drillstring vibrations add more cost to the operation. Consequently, the new solutions based on technology capabilities provide a powerful tool to integrate and interpret the drilling data for the best use of @@the data for operation performance enhancement. This study provides a successful application for utilizing the surface drilling data to automate drillstring vibration detection during the drilling curve section employing machine learning (ML) techniques. The axial, torsional, and lateral vibration modes are detected through testing four ML techniques named the @@adaptive neuro-fuzzy inference system (ANFIS), radial basis function (RBF), functional networks (FN), and support vector machines (SVMs) with real field data. The models' development was achieved by comprehensive study starting from data gathering, wrangling, statistical analysis, developing the ML models, evaluating the model prediction accuracy, and reporting the high accuracy results. The developed models were evaluated, and results showed that ANFIS and SVM models provided the highest accuracy with a coefficient of correlation (R) ranging from 0.9 to 0.99 followed by the RBF and FN models through model training and testing (R ranging from 0.82 to 0.96). Validating the models over unseen data confirmed the high accuracy prediction for the three vibration modes. Generally, the developed models provided technically accepted accuracy with R higher than 0.93 and AAPE less than 2.8% for SVM and ANFIS models while FN and RBF showed R between 0.82 and 0.95 and AAPE less than 5.7% between actual readings and predictions. Based on these results, the developed ML algorithm might be utilized as an intelligent solution to autodetect downhole vibration while drilling from surface sensor data only, which will save the downhole tool cost.

Derivation of rock's geomechanical parameters while drilling by combining surface drilling data, gamma ray data, and machine learning techniques in carbonate formations

This work presents a field application of an effective and reliable methodology for geomechanical parameters evaluation, using surface logging drilling data (rate of penetration, torque, weight on bit, stand pipe pressure, rotation per minute, flow rates) and well log data (sonic log, bulk density log, gamma ray log) to feed a model characterised by the combination of different machine learning algorithms (multiple linear regression, support vector regression, random forest, artificial neural network, and XGBoost). To expand the range of application to those cases where the downhole tools are not technically feasible or economically viable, a flexible workflow has been developed to derive a Synthetic Gamma Ray (SGR) from X-ray Fluorescence (XRF) analysis performed on cuttings. The methodology, applied to a dataset of 11 wells drilled in the same geological units but in different fields in Kuwait, proves its successful use to derive several geomechanical parameters, among which the results associated with Young’s Modulus, density, and Poisson’s Ratios are here presented.

Machine Learning Models for Acoustic Data Prediction During Drilling Composite Lithology Formations

Abstract The rock acoustic data that provide important information about the formation petrophysics and geomechanics are highly needed to design the wells drilling programs, in addition to, reservoir stimulation and field development plans. The acoustic data acquisition through the conventional methods in the petroleum industry either by logging or lab measurements has many drawbacks as the cost of well log operations and the job time. Determining the acoustic data through the common correlations did not provide high accuracy, and there are limitations for using these correlations. The new trend in the petroleum industry with the fourth industrial revolution is to employ machine learning for such problems to provide effective solutions. Therefore, this study utilized the machine learning for developing prediction models for acoustic compressional and shear slowness using adaptive neuro-fuzzy inference system and support vector machines tools. The study presents novel contributions for predicting acoustic slowness from only the surface drilling data while drilling different formations of composite lithology (limestone, sandstone, shale, and carbonate). The study utilized real field data (2800 data points) to build and test the two models through deep sensitivity analysis, in addition, further testing for the models by another 2800 data points from the same field for the validation phase. The obtained results ensured the capability of machine learning for predicting the acoustic slowness with high accuracy as adaptive neuro-fuzzy inference system (ANFIS) achieved a correlation coefficient (R) higher than 0.98 and error less than 1.43% as average absolute percentage error (AAPE) between the actual and predicted acoustic values. Adaptive neuro-fuzzy inference model showed the highest accuracy during the model training as R was 1.0; in addition, R for testing results showed 0.98, and AAPE ranged from 0.25% to 0.92%. The validation phase ensured the high performance for the acoustic prediction of the developed models as R is higher than 0.98 and AAPE lower than 1.46%. The machine learning applications through the developed models for the acoustic data will provide cost and time savings for acoustic data acquisition for the field applications.

Application of machine learning and well log attributes in geothermal drilling

As the world is moving towards carbon-neutral energy resources, geothermal has potential to become one of the most compatible avenues. The geothermal exploration requires drilling operations in high-temperature environments and has hard-rock formations. The traditional drilling systems have been designed for optimal performance in oil and gas reservoirs. However, the unique characteristics of the geothermal reservoirs require a fresh look on the available systems. One approach can be to use the data generated during field operations and then design, test, and validate the operational processes. Here, we have applied machine learning algorithms and different architectures of deep-learning to capture the patterns in the operational parameters and understand its implications on the real-time monitoring systems. Also, a robust framework is proposed for building classification predictors for lost circulation problems.In this paper, we used the FORGE well logging data and synthesize the evolution of dynamic data. We used the surface drilling data as input for supervised machine learning algorithms such as k-Nearest Neighbors, Random Forest, Decision tree, Gradient Boosting, and Deep Learning models with hidden layers. Finally, we identified hazardous zones using the classification models. Additionally, the log attribute based on change-point analysis, is used as an additional input parameter to train these classification models. Result analysis and comparative studies of these models show that the random forest and gradient boosting outperforms the other classifiers. Based on this study, we developed and demonstrated the suitability of a incremental learning framework for real-time monitoring systems.

Intelligent Model for Predicting Downhole Vibrations Using Surface Drilling Data During Horizontal Drilling

Abstract Drillstring vibration is a major concern during drilling wellbore, and it can be split into three types: axial, torsional, and lateral. Many problems associate with the high drillstring vibrations as tear and wear in downhole tools, inefficient drilling performance, loss of mechanical energy, and hole wash-out. The high cost for the downhole measurement of the drillstring vibrations encourages machine learning applications toward downhole vibration prediction during drilling. Consequently, the objective of this paper is to develop an artificial neural network (ANN) model for predicting the drillstring vibration while drilling a horizontal section. The ANN model uses the surface drilling parameters as model inputs to predict the three types of drillstring vibrations. These surface drilling parameters are flowrate, mud pumping pressure, surface rotating speed, top drive torque, weight on bit, and rate of penetration. The study utilized a data set of 13,927 measurements from a horizontal well that was used to train the ANN model. In addition, a different data set (9284 measurements) was employed to validate the developed ANN model. Correlation coefficient (R) and average absolute percentage error (AAPE) are statistical metrics that are used to evaluate the model accuracy based on the difference between the actual and predicted values for the axial, torsional, and lateral vibrations. The results of the optimized parameters for the developed model showed a high correlation coefficient between the predicted and the actual drillstring vibrations that showed R higher than 0.95 and AAPE below 3.5% for all phases of model training, testing, and validation. The developed model proposed a model-based equation for real-time estimation for the downhole vibrations.

Machine-Learning Work Flow Identifies Brittle, Fracable, Producible Rock Using Drilling Data

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201456, “Machine-Learning Work Flow To Identify Brittle, Fracable, and Producible Rock in Horizontal Wells Using Surface Drilling Data,” by Ngoc Lam Tran, SPE, Ishank Gupta, SPE, and Deepak Devegowda, SPE, University of Oklahoma, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, Colorado, 5–7 October. The paper has not been peer reviewed. The complete paper demonstrates the application of an interpretable machine-learning work flow using surface drilling data to identify fracable, brittle, and productive rock intervals along horizontal laterals in the Marcellus shale. The results are supported by a thorough model-agnostic interpretation of the input/output relationships to make the model explainable to users. The methodology described here can be generalized to real-time processing of surface drilling data for optimal landing of laterals, placing of fracture stages, optimizing production, and minimizing frac hits. Modeling Approach In a multiwell field development, the ability to improve recovery efficiency per rock volume depends on well spacing, stacking, completion strategy, and avoidance of frac hits. In this study, the authors use surface drilling data to predict mechanical rock facies (generated using Poisson’s ratio and Young’s modulus) using several supervised classification methods. The objective is to use the trained model to predict the mechanical facies in real time using surface drilling data in future wells to optimize the well trajectory and placement of fracture stages. The highlighted modeling approach is conducted as follows: - Data preparation. Preliminary univariate (histograms, box plots) and bivariate analyses (cross plots) are performed, outliers removed, and missing values handled. The clean data are scaled, and a few additional derived variables such as mechanical specific energy (MSE) are added (also called feature engineering) that may boost predictive efficiency. - Unsupervised clustering. Clusters are generated using the derived variables of Young’s modulus and Poisson’s ratio. The number of clusters is optimized by silhouette width and within the sum of squares. The clusters group points of similar brittleness/fracability and are referred to as a geomechanical facies in this paper. - Supervised classification. A classifier is built using K-nearest neighbors, gradient boosting, random forests, and neural networks to identify geomechanical facies from surface drilling data. Seventy-five percent of the data are used for training and 25% for testing. Tenfold cross validation is performed on the training data to prevent overfitting. In 10-fold cross validation, the training data is subdivided randomly into 10 parts. The model is trained on nine parts and then validated on the remaining part. This process is repeated multiple times for each machine-learning technique. Only those models are averaged to provide the final model that gives good results for the validation data.

Artificial intelligence models for real-time synthetic gamma-ray log generation using surface drilling data in Middle East Oil Field

Gamma-ray logging (GR) is one of the most crucial elements in petroleum engineering that would assist evaluate oil and gas reservoirs and identify the formation lithology. GR can be measure for a retried core or by running the logging while drilling (LWD) tools. Both methods require a long time and high cost besides the complexity of applying them. In this study, three artificial intelligence (AI) techniques were assessed for their ability to produce accurate models for predicting a synthetic GR log using surface drilling parameters. These techniques were artificial neural networks (ANN), adaptive network-based fuzzy logic (ANFIS), and functional networks (FN). A total of 4609 data entries from three wells in the Middle East were used to train and test the models and later validate them. The data from wells 1 and 2 were used to develop the AI models, while well 3 was used to validate these models. The obtained results showed the accuracy of predicting the GR using ANN and ANFIS. The correlation coefficient (CC) values of the prediction and measured GR were 0.96, 0.96, and 0.83 for ANN, ANFIS, and FN, respectively. Also, the average absolute percentage error (AAPE) values for the three models in the same order were 3.69%, 3.72%, and 6.7% which shows slightly less accuracy of the FN model. For the validation data set, the ANN and ANFIS models were able to capture all changes in the GR log trend with depth and accurately predict the GR values. A new empirical correlation for GR prediction was developed based on the weight and biases of the optimized ANN model. The new correlation can be used to predict the GR log with high accuracy as a function of the drilling surface data with no need to run AI methods.

Deep Learning and Time-Series Analysis for the Early Detection of Lost Circulation Incidents During Drilling Operations

Drilling operations consist of breaking the rock to deepen a wellbore for oil or gas extraction. A drilling fluid, circulating from the surface through the drill pipe and from the annulus to the surface, is used to remove rock cuttings and maintain hydrostatic pressure. Drilling fluid lost circulation incidents (LCIs) are major sources of non-productive time (NPT) in drilling operations. These incidents occur due to preexisting natural fractures (vugs, caverns, etc.) and/or drilling-induced hydraulic fractures. The initiation of an LCI could lead to other hazardous drilling phenomena, such as formation influx or kick/blowout, stuck pipe incidents, among others. LCIs are typically monitored at the rig site by observing drilling fluid levels in the fluid tanks. This manual process incurs missing the occurrence or late detection of LCIs. Machine learning (ML) and deep learning (DL) classification algorithms are powerful in processing time-series data and achieving early detection of such temporal phenomena. In this study, we performed a large-scale analysis of the surface drilling and rheology data obtained from historical wells with LCIs. This analysis includes primary and secondary preprocessing steps including, aggressive sampling, feature engineering, and window normalization to derive generalizable DL models for real-time operations. Focal loss was utilized to account for data class imbalance and train robust and generalizable models. The results obtained from different ML/DL algorithms showed that one-dimensional convolutional neural network models resulted in the best performance with state-of-the-art precision, recall, and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> scores of 87.34%, 73.40%, and 79.77%, respectively, on unseen test drilling data.

Application of Interpretable Machine-Learning Workflows To Identify Brittle, Fracturable, and Producible Rock in Horizontal Wells Using Surface Drilling Data

Summary In this study, we demonstrate the application of an interpretable (or explainable) machine-learning workflow using surface drilling data to identify fracturable, brittle, and productive rock intervals along horizontal laterals in the Marcellus Shale. The results are supported by a thorough model-agnostic interpretation of the input/output relationships to make the model explainable to users. The methodology described here can easily be generalized to real-time processing of surface drilling data for optimal landing of laterals, placing of fracture stages, optimizing production, and minimizing fracture hits. In practice, this information is rarely available in real time and requires tedious and time-consuming processing of logs (including image logs), core, microseismic data, and fiber-optic-sensor data to provide post-job validation of fracture and well placement. Post-completion analyses are generally too late for corrective action, leading to wells with a low probability of success and increasing risk of fracture hits. Our workflow involves identifying geomechanical facies from core and well-log data. We verify that the geomechanical facies derived using core and well-log data have characteristically different brittleness, fracturability, and production characteristics. We test and investigate several different supervised classifiers to relate surface drilling data to the geomechanical facies. The data were divided into training and test data sets, with supervised classification techniques being able to accurately predict the geomechanical facies with 75% accuracy on the test data set. The clusters predicted on test well (unseen data) were qualitatively verified using the microseismic interpretation. The use of Shapley additive explanations (SHAP) helps explain the predictive models, rank the importance of various inputs in the prediction of the facies, and provides both local and global sensitivities. Our study demonstrates that pre-existing natural-fracture networks control both the hydraulic-fracture geometry as well as the production. Natural fractures promote the formation of complex fracture networks with shorter half-lengths, which increase well productivity while minimizing fracture hits and neighboring-well interactions. The natural-fracture network is itself controlled by the geomechanical properties of the rock. The ability of the surface drilling data to reliably predict the geomechanical rock facies provides a powerful tool for real-time optimization of wellbore trajectory and completions.

Application of artificial neural network to predict the rate of penetration for S-shape well profile

The rate of penetration (ROP) is defined as the required speed to break the drilled rock by the bit action. The existing established models for estimating the rate of penetration include the basic mathematical correlation that have many limitations. The objective of this paper is to implement an artificial neural network (ANN) technique to predict the rate of penetration for the S-shape well profile from the surface drilling data. The data used to build the ANN model is based on real field data of more than 7900 data points obtained from two wells. The data from well A and B was used to train and test an ANN model, while 4000 unseen data points from well C were used for validation. More than 30 sensitivity analyses were performed and the results showed that ANN-ROP model has a high performance with an average correlation coefficient of around 0.93 and a root mean square error (RMSE) of 6.2%. The best ANN parameter combination was with 1 layer, 29 neurons, tan-sigmoid as the transfer function, and trainlm as the training function. The model was then validated by the data from well C which was unseen by the model during the training and testing stage with a correlation coefficient of 0.92 and an RMSE of 6.7%. To enable ROP prediction in real time, an empirical correlation was developed based on the optimized ANN model weights and biases.

Looking Ahead of the Bit Using Surface Drilling and Petrophysical Data: Machine-Learning-Based Real-Time Geosteering in Volve Field

SummaryPetroleum reservoirs are often associated with multiple target zones or a single zone adjacent to nonproductive intervals. Real-time geosteering therefore becomes important to remain in zone or to dynamically steer toward a target. This requires knowledge of the petrophysical/rock mechanical properties of the rock surrounding the bit. Although logging while drilling can provide this information, a cost-effective and almost-real-time solution is lacking. In general, there is a depth lag, and therefore, a time delay, between what the logging-while-drilling sub relays to the surface and the bit performance. This study focuses on relating drill-bit- and drillstring-performance data in a machine-learning (ML) workflow to predict the lithology at the bit while drilling. The method we are proposing offers several advantages in terms of cost and time savings for real-time geosteering applications, where going out of zone requires costly intervention.In this study, we have used a public data set from Volve Field on the Norwegian continental shelf. Within our proposed workflow, as a first step, logs sensitive to lithology [such as density, gamma ray (GR), and sonic] are grouped into three electrofacies. We also had access to core data, which helped us interpret the electrofacies in terms of mineralogy. The three electrofacies corresponded to quartz-rich (sandstone/siltstone), clay-rich (shale), and carbonate-rich (limestone) lithologies.The next step is to predict the electrofacies using various measurement-while-drilling (MWD) variables, such as rate of penetration (ROP), weight on bit (WOB), and several others that are monitored in real time. Supervised classification algorithms were used to relate real-time surface measurements to lithology. The algorithms were able to predict lithology in test wells with more than 80% accuracy. These results, although encouraging, constitute a small step toward drilling-automation/advisory systems. The development of such systems can prevent costly out-of-zone drilling and minimize rig time and equipment use, thereby potentially reducing capital expenditures. This study was specifically performed in Volve Field in the North Sea using petrophysical and surface drilling data from vertical wells. However, the workflow has a potential to be extended to other formations in other fields in different well types.

Surface Drilling Data Can Help Optimize Fracture Treatment in Real Time

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 189810, “Real-Time Completion Optimization of Fracture Treatment Using Commonly Available Surface Drilling and Fracking Data,” by Mohit Paryani, Djamel Sia, Bhavina Mistry, Drew Fairchild, and Ahmed Ouenes, FracGeo, prepared for the 2018 SPE Canada Unconventional Resources Conference, Calgary, 13–14 March. The paper has not been peer reviewed. The objective of optimizing a fracture design is to spend the least amount of money and get the most productivity out of the reservoir by stimulating and contacting as much reservoir rock as possible. This paper presents a unique work flow that addresses in real time the challenges of perforation and fracture-treatment design while accounting for the lithologic and stress variability along the wellbore and its surroundings. Surface Drilling Data in Fracturing Design and Analysis Existing fracturing-design tools often make simplistic assumptions because of a lack of input data. These designs still use layer-cake models and sometimes rely on the data of a nearby well rather than data measured at the considered well. As cost-cutting efforts accelerate in unconventional wells, expecting a log at every well will not be feasible. Because the outcome from the fracturing design heavily depends on specific geomechanical properties, stresses, and surrounding natural fractures, changing the current industry practice of using a nearby well and assuming all subsurface properties to be the same for all stages is imperative. The software used by the authors is able to derive a 3D distribution of the rock properties required as input in the fracturing design. This fracturing-design input available at any well is made possible by using surface drilling data to compute geomechanical logs, pore pressure, stresses, porosity, and natural fractures. Transforming Drilling Data Into Log Properties. The surface drilling data commonly available on any rig includes weight on bit, rate of penetration, rotational speed, and torque. Transforming this data into valuable input for fracturing design starts by computing the corrected mechanical specific energy (CMSE), which removes frictional pressure losses along the drillstring to obtain accurate estimations at the bit. Confined compressive strength (CCS) is then estimated. When the CMSE and the CCS are estimated correctly, the drilling efficiency is computed. Deviations from expected pore pressures are recognized by comparing a hydrostatic trendline with the drilling-efficiency data. The methodology to derive pore pressure from drilling data is based on the concept that the energy spent at the bit to remove a unit volume of the rock is a function of the differential pressure to which the rock is subjected while drilling. The differential pressure provides useful information for deriving the pore pressure. Feeding the rock-strength and pore-pressure information to the real-time geomechanical model helps identify the differential stress variability along the wellbore. Other rock mechanical properties also can be estimated using various lithologies derived from rock-strength information.

Real-Time Analytics Improves Process Safety in a Drilling-Contractor Operations Center

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper OTC 28824, “Operational and Safety Improvements of Applying Real-Time Analytics in a Drilling-Contractor RTOC,” by A.L.F. Madaleno, S.L.S. Neto, L.A. dos Santos, and C.A.L. de Oliveira, QGOG Constellation, prepared for the 2018 Offshore Technology Conference, Houston, 30 April–3 May. The paper has not been peer reviewed. Copyright 2018 Offshore Technology Conference. Reproduced by permission. Digitally enabled collaboration between operators and service companies is driving significant improvements in process safety in the upstream oil and gas industry. However, drilling contractors generally have been considered only the owners of the systems that generate part of the surface drilling data and, as such, have not been engaged in the development of real-time analytics tools. The role of a real-time operations center (RTOC) with a drilling contractor is itself a new factor. This paper explains how an ultradeepwater drilling contractor is applying real-time analytics and machine learning to leverage its RTOC to improve process safety and performance. Introduction The increasing availability of digital drilling data and the emergence of reliable communication between offshore rigs and well operators’ offices enabled a proliferation of RTOCs in the 2000s, spawning a collaborative environment between well operators and service companies that enabled enhanced support and optimized use of expert resources to improve process safety and operational efficiency. Drilling contractors generally were not engaged in developing the real-time analytics tools for the RTOCs. One consequence is that the majority of RTOC software solutions are only appropriate for wellsite information transfer standard markup language (WITSML) data visualization, or they feature embedded tools for well operators and service companies focused on drilling efficiencies but the data and models they use are not accessible to the rig operators. Essential aspects of process safety and operational performance, such as procedural discipline and critical equipment health, can be addressed by drilling contractors in real time if appropriate resources are in place. The contractor’s RTOC has enhanced process safety and surveillance after moving to an approach in which drilling engineers, assisted by software developers with robust experience in real-time data analytics, began writing automated operation-identification and problem-detection algorithms. The cooperative work allowed the creation of tools adapted to the company’s needs and provided a streamlined process to implement, customize, and repair these tools over time. During the planning phase, it was agreed that initial developments should be grouped into six important dimensions that strongly influence the overall process safety and operational performance of the rig. The contractor applied real-time analytics and machine learning to identify and alert RTOC engineers of abnormal situations automatically. The detection of the abnormal situations by RTOC engineers and the comparison of the standard by which various drilling crews react to them feeds a lessons-learned data base, which, in turn, sustains the procedural advancement initiatives, ensuring continuous improvement.

Real-Time Advisory System Mitigates Drilling Vibrations in a Lateral Section

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 188942, “Mitigating Drilling Vibrations in a Lateral Section Using a Real-Time Advisory System,” by J.R. Bailey, SPE, and G.S. Payette, SPE, ExxonMobil; M.T. Prim, J. Molster, and A.W. Al Mheiri, Zakum Development; P.G. McCormack, SPE, Halliburton; and K. LeRoy, Pason Systems, prepared for the 2017 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 13–16 November. The paper has not been peer reviewed. A real-time drilling-data analysis and recommendation system that leverages surface drilling data was deployed in a lateral section of a well from an artificial island in Abu Dhabi. A key objective of this technology was to provide the driller with an easy-to-use display of a novel drilling-performance map. This paper presents data from the demonstration run, illustrates the features of this technology, and provides general observations on optimization of drilling parameters in this hole section. Background A research prototype drilling advisory system (DAS) was set up on an island rig with a wellsite information transfer specification (WITS) data feed from the mud-logger cabin over a fiber-to-serial cable to the computer in the driller’s cabin. The DAS helps the driller make decisions for managing controllable drilling parameters to enable a high rate of penetration (ROP) and low vibrations. The system uses surface data only (i.e., data obtained from sensors instrumented on surface equipment) from the mud logger. The platform then analyzes the data and displays basic surveillance information, including heat maps that vary with the controllable drilling parameters. On the basis of this data and processing algorithms, the system makes recommendations for the controllable parameter values. Data from three intervals were analyzed, and two of the three saw contrasting trends of stick/slip [torsional severity estimate (TSE)] and mechanical specific energy (MSE) with increasing rotary speed, whereas one interval did not see such a tradeoff. The complex interplay between these dysfunctions requires an application-specific real-time advisory system to provide optimal drilling performance. The control variables for this drilling application were ROP and rotary speed. Intervals for Data Analysis The 8½-in. drilling interval started from the 9 5/8-in. casing shoe at 13,916 ft, and section total depth was called at 23,974 ft. Drilling data from this interval was subdivided into seven intervals corresponding to drilling tests, with parameter recommendations from the DAS analysis. Examples of Drilling-Parameter Optimization To initiate the DAS, the driller conducts a drilling test by starting the calibration mode on the advisory system to reset the system memory. Parameter set-point values then must be maintained relatively constant for a time sufficient to determine good average values for the drilling response. The advisory system requires a statistically significant sampling of data at stationary parameter values. When several such calibration points have been recorded, the system then provides automated analysis of the drilling test for the span of operating parameters. If the formation changes over this interval substantially, the calibration process should be repeated.

Using surface drilling data for a geologically and geomechanically constrained 3D planar frac simulator and fast reservoir simulation — application to engineered completions and well interference prevention

Modelling unconventional reservoirs requires increasingly complex physics to describe the phenomena that affect the performance and efficiency of horizontal wells. For example, poroelastic geomechanical simulation (Ouenes et al., 2017b) is needed to model frac hits and well interferences resulting from the presence of stress and pressure dependent natural fractures and other geologic factors that see their permeability increase during stimulation. Recent field observations related to stress relaxation required the introduction of viscoelasticity (Peterson et al., 2018) to better understand the effect of timing during fracing. Lastly, the importance of interfaces and their impact on fracture height growth required the introduction of 3D damage mechanics (Aimene et al., 2018) to model the propagation of hydraulic fractures in a more realistic rock volume that considers the layering of the various lithologies and the resulting weak interfaces that will in turn interact with hydraulic fractures. This increasing complexity in physics is also combined with the need to provide solutions very quickly, if not in real time As the physics of unconventional reservoirs becomes more complex, the data available at each well to correctly model that physics is dwindling at an alarming rate. The introduction of the continuum multiscale approach (Ouenes et al., 2017b) and the use of surface drilling data provide the unique opportunity to address both the lack of data and the increasingly complex physics. In the absence of wireline logs and seismic data, surface drilling data collected at each well is used at different scales ranging from wellbore to reservoir scale. In this process called ‘Inverse Design and Validation’, the information contained in the surface drilling data is used 1) during the drilling to optimize the landing zone and geosteering, 2) during the design of the completion to geoengineer the stages to account for the variability of the rock, and 3) to build 3D models that will allow the correct estimation of petrophysical, geomechanical properties and stresses needed in 3D planar frac simulators as well as fluid flow simulation. These various applications are examined in the next sections.

Technology Update: Data Aggregation and Distribution With Human Factors Incorporated

Technology Update To optimize the value of surface and downhole drilling data, an operator, a software company, and a service company have combined forces to develop a system to seamlessly integrate all forms of data, file types, and communication protocols. To ensure that people on the rig and in the office are able and willing to act on the data, the system includes human-factors engineering. The upstream oil and gas industry first began to gather and stream data from rig sensors in the 1970s and introduced real-time data centers in the early 1980s. Since those early efforts, the industry has made only incremental progress in using available drilling data. These changes typically are limited to improving visual displays, upgrading relational databases for easier reporting, and providing web services for integration. Today, the widespread use of drilling data remains limited by technology deficiencies in rig sensor measurement quality, sensor accuracy and reliability, rig-site aggregation methods, data sampling rates, and the inability to easily share data between proprietary vendor systems. In addition, drilling electronic data recorder (EDR) technology has not kept pace with the technology of other industries or that of other oil and gas sectors. Typical service company aggregation systems use outmoded software and hardware. Consequently, these systems are costly to alter, require long cycle times to add features, cannot process the high-frequency data that are collected by modern sensors, and cannot easily accommodate different back-end engines provided by third parties. In addition, because they have closed architecture, changing user interfaces is notoriously difficult. To address these shortcomings, an operator has built a system that easily accommodates back-end engines, enables rapid prototyping and deployment of new ideas, and separates the back-end from user interfaces. Unlike traditional systems that use only rig data, this new system also uses well information from other data sources. The design basis was to provide users with higher frequency (up to 100 Hz) and higher-quality data than traditional data-aggregation and -distribution systems. This allows drillers to access real-time models and algorithms for analyses, such as stick/slip mitigation, stuck pipe avoidance, optimal tripping practices, hole cleaning, geosteering, and other drilling methods.

Geomechanical modelling using poro-elasticity to prevent frac hits and well interferences

Modelling unconventional reservoirs requires a continuum multi-scale approach to represent the dominant physics occurring at each scale (Figure 1). In the most common far field studies (thousands of feet around the wellbore), geophysics used in conjunction with processes such as facies constrained extended seismic elastic inversion (Kiche et al., 2016) provide dynamic geomechanical properties throughout the entire reservoir volume – properties, which are critical to the optimal selection of landing zones and completions of unconventional wells. It is also in the far field that the combination of geomechanical properties with continuous natural fracture models (Jenkins et al., 2009) are used as input in a robust reservoir geomechanics workflow (Aimene and Ouenes, 2015) that is able to simulate the interaction between the regional stresses and the three major sources of stress gradients affecting hydraulic fracturing: variable geomechanical properties, geologic discontinuities, and pore pressure variability. The resultant locally varying differential stress distribution provides the initial reservoir stress conditions before fracking and the correct input for the estimation of strain during and after hydraulic fracturing, which should be validated with a predicted microseismicity (Aimene and Ouenes, 2015). This strain provides coupling between the far and mid-field studies and creates the unique opportunity to constrain the mid-field frac design (Paryani et al., 2016) and reduce the uncertainties of multiple frack design parameters by constraining lateral stress gradients and imposing the symmetric frac lengths that the earth will allow. The behaviour of the mid-field hydraulic fracturing is also affected by multiple near field effects such as type of completion (Peterson et al., 2017) and its effects on the near wellbore geomechanics. The near field effects could be estimated at any well using surface drilling data (Jacques et al., 2017) which could provide the necessary information required in the mid and far fields in the inverse design and validation process in situations where there is lack of data.

Technology Focus: Drilling Technology (February 2013)

Technology Focus When searching OnePetro (www.onepetro.org) for papers dealing with “real time,” “drilling,” and “optimization,” one notices that 85% of SPE papers dealing with these three terms have been written over the last decade (2002–12). Indeed, there is an accelerated trend strongly supported by drilling companies (especially operators and service companies) of reducing nonproductive time, speeding up the learning curve, and optimizing drilling efficiency to reduce drilling cost and avoid unexpected failures. If drilling optimization is not new and dates back mainly to the 1960s, drilling-optimization departments and engineers today have an increasing number of tools (software and hardware) to facilitate their optimization tasks. Moreover, this can be achieved with a limited amount of investment compared with the huge cost reduction that can be gained. Depending on each application and purpose, many levels of drilling optimization in real time or almost real time can be achieved. From surface drilling data alone, mechanical-specific-energy surveillance can already be monitored to optimize rate of penetration, better detect the moment to pull the bit, and detect bit balling or vibration. Many companies have set up this process and have obtained very good results in drilling efficiency. Simultaneously having surface and downhole data (measured close to the bit or distributed along the drillstring) enables better assessment of the amount of energy transferred downhole (from friction loss along the drillstring) and enables better detection of downhole conditions (e.g., vibrations and hole cleaning). Predictive physical models coupled with surface and downhole data enable the addition of even more value because one can better detect ongoing or future abnormal conditions. For example, visualizing predictive critical rotation speeds (taken from dynamic modal analysis software), current rotation speed, and downhole vibration indicators on the same plot helps drillers adjust the rotation speed accordingly to avoid harmonic vibrations. Torque-and-drag and hydraulics calculation software is also very useful to detect deteriorating hole-cleaning conditions. However, if the tools exist to acquire, process, and visualize the drilling data, the most difficult part consists of understanding and interpreting the data to make a decision and provide the driller with a solution to mitigate the problem or optimize the drilling efficiency. This part is a real challenge because managing and analyzing downhole drilling data require highly skilled experts and analysts, to take full advantage of this information. Some of the papers selected for this month’s feature are good illustrations of the latest developments and applications in real-time drilling optimization. Recommended additional reading at OnePetro: www.onepetro.org. SPE 151176 The Effect of Equivalent-Diameter Definitions on Frictional-Pressure-Loss Estimation in an Annulus With Pipe Rotation by Olatunbosun Anifowoshe, University of Oklahoma, et al. SPE/IADC 151389 Drilling Performance Is a Function of Power at the Bit and Drilling Efficiency by Rolf Pessier, Baker Hughes, et al. SPE/IADC 151248 Directional-Drilling Tests in Concrete Blocks Yield Precise Measurements of Borehole Position and Quality by Ed Stockhausen, Chevron, et al. SPE/IADC 156445 BHA Modeling Coupled With High-Frequency Downhole Vibration Data Provides Unique Solution to Inconsistent Drilling Performance in South Iraq Oil Field by Paul Teasdale, Alwaha Petroleum, et al.

Fluctuating Arctic Sea Ice Thickness Changes Estimated by an In Situ Learned and Empirically Forced Neural Network Model

Abstract Sea ice thickness (SIT) is a key parameter of scientific interest because understanding the natural spatiotemporal variability of ice thickness is critical for improving global climate models. In this paper, changes in Arctic SIT during 1982–2003 are examined using a neural network (NN) algorithm trained with in situ submarine ice draft and surface drilling data. For each month of the study period, the NN individually estimated SIT of each ice-covered pixel (25-km resolution) based on seven geophysical parameters (four shortwave and longwave radiative fluxes, surface air temperature, ice drift velocity, and ice divergence/convergence) that were cumulatively summed at each monthly position along the pixel’s previous 3-yr drift track (or less if the ice was &amp;lt;3 yr old). Average January SIT increased during 1982–88 in most regions of the Arctic (+7.6 ± 0.9 cm yr−1), decreased through 1996 Arctic-wide (−6.1 ± 1.2 cm yr−1), then modestly increased through 2003 mostly in the central Arctic (+2.1 ± 0.6 cm yr−1). Net ice volume change in the Arctic Ocean from 1982 to 2003 was negligible, indicating that cumulative ice growth had largely replaced the estimated 45 000 km3 of ice lost by cumulative export. Above 65°N, total annual ice volume and interannual volume changes were correlated with the Arctic Oscillation (AO) at decadal and annual time scales, respectively. Late-summer ice thickness and total volume varied proportionally until the mid-1990s, but volume did not increase commensurate with the thickening during 1996–2002. The authors speculate that decoupling of the ice thickness–volume relationship resulted from two opposing mechanisms with different latitudinal expressions: a recent quasi-decadal shift in atmospheric circulation patterns associated with the AO’s neutral state facilitated ice thickening at high latitudes while anomalously warm thermal forcing thinned and melted the ice cap at its periphery.