Prediction of Rate of Penetration for wells at Nam Con Son basin using Artificial Neural Networks models

The rate of penetration (ROP) is an important parameter that affects the success of a drilling operation. In this paper, the research approach is based on different artificial neural network (ANN) models to predict ROP for oil and gas wells in Nam Con Son basin. The first is the process of collecting and evaluating drilling parameters as input data of the model. Next is to find the network model capable of predicting ROP most accurately. After that, the study will evaluate the number of input parameters of the network model. The ROP prediction results obtained from different ANN models are also compared with traditional models such as the Bingham model, Bourgoyne & Young model. These results have shown the competitiveness of the ANN model and its high applicability to actual drilling operations.

A novel alkali-surfactant for optimization of filtercake removal in oil–gas well

The main objective of this study is to improve the oil-based filtercake removal at the wellbore second interface through chemical method. The reductions in near-well permeability, bonding strength at wellbore second interface and acidizing treatment are the critical problems in oilfield upstream operations. One of the major causes has been identified as drilling fluid filtrate invasion during the drilling operations. This as result leads to near-well reduction in-flow capacity due to high drawdown pressure and wellbore instability. A number of chemical methods such as enzymes, acids, oxidizers, or their hybrids, have been used, however, due to the presence of a number of factors prior to its removal, there are still many challenges in cleaning oil-based filtercake from the wellbore surface. There is a need for development an effective method for improving oil-based filtercake removal. This study presents a novel Alkali-Surfactant (KV-MA) solution developed in the laboratory to optimize the filtercake removal of oil–gas wellbore. The Reynold number for KV-MA solution was found to be 9,068 indicating that turbulent flow regime will dominate in the annulus which enhances the cleaning efficiency. The wettability test established that, contact angle of 14° was a proper wetting agent. The calculated cleaning efficiency was 86.9%, indicating that it can effectively remove the oil-based filtercake. NaOH reacts with the polar components in the oil phase of the oil-based filtercake to produce ionized surface-active species; hence reducing the Interfacial Tension. Surfactant quickens the diffusion of ionized species from the interface to the bulk phase.

Choosing a methodological base for forming and accounting for the construction costs of a gas well at the final stage of field development

The article introduces the problems of choosing a methodological base for forming and accounting costs during well construction, taking into account the intensification of gas inflow at the final stage of field development. The main methods that allow taking into account the costs of drilling operations are outlined. The main costs that need to be taken into account when designing fields with hard-to-recover gas reserves are identified and characterized. The main stages of cost formation by construction phases of a gas-producing well are shown, as well as the factors influencing the level and structure of the cost price during the construction of a well are highlighted. The authors consider the classification of cost accounting methods in the context of the comparison criterion and present the possible results of the correct choice of the cost formation method during the construction of a gas well at the final stage of its development.

CAD-BASED AUTOMATED G-CODE GENERATION FOR DRILLING OPERATIONS

The automated generation of G-code for machining processes is a valuable tool at the hands of every engineer and machinist. Nowadays, many software systems exist that provide automated functions related to G-code generation. Most free software require the import of a Drawing Exchange Format (DXF) file and cannot work directly on a 3D part. On the contrast, the equivalent commercially-available software systems are feature-rich and can provide a variety of automated processes, but are usually highly priced. The presented application aims to supplement the existing free Computer Aided Manufacturing (CAM) systems by providing a way of generating G-code for drilling operations, using already owned commercial 3D Computer Aided Design (CAD) systems such as SolidWorksTM. Thus, in the case of 3D part drilling, a standard 3D CAD system is sufficient since the code can be adopted by most modern CAD software with minor changes. Moreover, no specialized CAM software is required. In order to achieve this automation, the Application Programming Interface (API) of SolidWorks™ 2018 was utilized, which allows for the design of visualized User Interfaces (UI) and the development of code under the Visual Basic for Applications (VBA™) programming language. The available API methods are employed to recognize the features that were used to design the part, as well as extract the geometric parameters of each of these features. In addition, an embedded calculator automatically defines the cutting conditions (cutting speed, feed and tool) based on the user selections. Finally, the application generates the Computer Numerical Control (CNC) code for the summary of the discovered holes according to the standardized G-code commands; the output can be a typical TXT or NC file that can be easily converted to the preference of the user if necessary.

Thermal Management of Bone Drilling Based on Rotating Heat Pipe

Bone drilling is a common surgical operation, which often causes an increase in bone temperature. A temperature above 47 °C for 60 s is the critical temperature that can be allowed in bone drilling because of thermal bone osteonecrosis. Therefore, thermal management in bone drilling by a rotating heat pipe was proposed in this study. A new rotating heat pipe drill was designed, and its heat transfer mechanism and thermal management performance was investigated at occasions with different input heat flux and rotational speed. Results show that boiling and convection heat transfer occurred in the evaporator and film condensation appears in the condenser. The thermal resistance decreases with the increase of the rotational speed at the range from 1200 to 2000 rpm and it decreases as the input heat flux rises from 5000 to 10,000 W/m2 and increases at 20,000 W/m2. The temperature on the drill tip was found to be 46.9 °C with an input heat flux of 8000 W/m2 and a rotational speed of 2000 rpm. The new designed rotating heat pipe drill showed a good prospect for application to bone drilling operations.

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

Development of New Models to Determine the Rheological Parameters of Water-based Drilling Fluid Using Artificial Intelligence

It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works present valid and reliable results, they are expensive and time consuming. On the other hand, continuous and regular determination of the rheological mud properties can perform its essential functions during well construction. More uncertainties in planning the drilling fluid properties meant that more challenges may be exposed during drilling operations. This study presents two predictive techniques, multiple regression analysis (MRA) and artificial neural networks (ANNs), to determine the rheological properties of water-based drilling fluid based on other simple measurable properties. While mud density (MW), marsh funnel (MF), and solid% are key input parameters in this study, the output functions or models are plastic viscosity (PV), yield point (YP), apparent viscosity (AV), and gel strength. The prediction methods were demonstrated by means of a field case in eastern Iraq, using datasets from daily drilling reports of two wells in addition to the laboratory measurements. To test the performance ability of the developed models, two error-based metrics (determination coefficient R2 and root mean square error RMSE) have been used in this study. The current results of this study support the evidence that MW, MF, and solid% are consistent indexes for the prediction of rheological properties. Both mud density and solid content have a relative-significant effect on increasing PV, YP, AV, and gel strength. However, a scattering around each fit curve is observed which proved that one rheological property alone is not sufficient to estimate other properties. The results also reveal that both MRA and ANN are conservative in estimating the fluid rheological properties, but ANN is more precise than MRA. Eight empirical mathematical models with high performance capacity have been developed in this study to determine the rheological fluid properties based on simple and quick equipment as mud balance and marsh funnel. This study presents cost-effective models to determine the rheological fluid properties for future well planning in Iraqi oil fields.

Novel Analytical Solution and Type-Curves for Lost-Circulation Diagnostics of Drilling Mud in Fractured Formation

Loss of circulation is a major problem that often causes interruption to drilling operations, and reduction in efficiency. This problem often occurs when the drilled wellbore encounters a high permeable formation such as faults or fractures, leading to total or partial leakage of the drilling fluids. In this work, we present a novel semi-analytical solution and type-curves that offer a quick and accurate diagnostic tool to assess the lost-circulation of Herschel-Bulkley fluids in fractured media. Based on the pressure and mud loss trends, the tool can estimate the effective fracture conductivity, the cumulative mud-loss volume, and the leakage period. The behavior of lost-circulation into fractured formation can be assessed using analytical methods that can be deployed to perform flow diagnostics, such as the rate of fluid leakage and the associated fracture hydraulic properties. In this study, we develop a new semi-analytical method to quantify the leakage of drilling fluid flow into fractures. The developed model is applicable for non-Newtonian fluids with exhibiting yield-power-law, including shear thickening and thinning, and Bingham plastic fluids. We propose new dimensionless groups and generate novel dual type-curves, which circumvent the non-uniqueness issues in trend matching of type-curves. We use numerical simulations based on finite-elements to verify the accuracy of the proposed solution, and compare it with existing analytical solutions from the literature. Based on the proposed semi-analytical solution, we propose new dimensionless groups and generate type-curves to describe the dimensionless mud-loss volume versus the dimensionless time. To address the non-uniqueness matching issue, we propose, for the first time, complimentary derivative-based type-curves. Both type-curve sets are used in a dual trend matching, which significantly reduced the non-uniqueness issue that is typically encountered in type-curves. We use data for lost circulation from a field case to show the applicability of the proposed method. We apply the semi-analytical solver, combined with Monte-Carlo simulations, to perform a sensitivity study to assess the uncertainty of various fluid and subsurface parameters, including the hydraulic property of the fracture and the probabilistic prediction of the rate of mud leakage into the formation. The proposed approach is based on a novel semi-analytical solution and type-curves to model the flow behavior of Herschel-Bulkley fluids into fractured reservoirs, which can be used as a quick diagnostic tool to evaluate lost-circulation in drilling operations.

Autonomous Directional Drilling Planning and Execution Using an Industry 4.0 Platform

Today, directional drilling is considered a mix between art and science only performed by experts in the field. In this paper, we present an autonomous directional drilling framework using an industry 4.0 platform that is built on intelligent planning and execution capabilities and is supported by surface and downhole automation technologies to achieve consistently performing directional drilling operations accessible for easy remote operations. Intelligent planning builds on standard planning activities that are needed for directional drilling applications and advances them with rich data pipelines that feed predictive and prescriptive machine-learning (ML) models; this enables more accurate BHA tendencies, operating parameters, and trajectory plans that ultimately reduce executional risk and uncertainty. Intelligent execution provides technologies that facilitate decision-making activities, whether they be from the wellsite or town, by leveraging the digital-drilling program that is generated from the intelligent planning activities. The program connects planning expectations, real-time execution data from the surface and downhole equipment, and generates insights from data analytics, physics-based simulations, and offset analysis to achieve consistent directional drilling performance that is transparent to all stakeholders. This new framework enables a self-steering BHA for directional drilling operations. The workflow involves an automated evaluation of the current bit position with respect to the initial plan, automated evaluation of the maximum dogleg capability of the BHA, and the capability to examine the health of the BHA tools and, if needed, an automated re-planning of an optimized working plan. This is accomplished on a system level with interdependencies on the different elements that make up the complete workflow. This new autonomous directional drilling framework will minimize operational risk and cost-per-foot drilled; maximize performance, procedural adherence, and establish consistent results across fields, rigs, and trajectories while enabling modern remote operations.

A Machine-Learning Based Workflow for Predicting Overpressure in a Stiff Dolomitic Formation

Overpressure refers to the abnormally high subsurface pressure that may exceed hydrostatic pressure at a given depth. Its characterization is an important part of subsurface characterization as it allows to complete drilling operations in a safe and optimal way. In dolomitic formations, however, the prediction of such overpressure is especially challenging because of (1) the high degree of lateral variability of the formations, (2) the limited effect of overpressure on tight rocks elastic parameters, and (3) the complexity of physical processes involved to form overpressure. In addition to these factors, existing experimental models generally used to relate elastic parameters to pressure are often not well calibrated to carbonate rocks. The alternative to existing purely physical approaches is a data-driven model that leverages data from offset wells. We show that due to the complexity of the characterization question to be solved, an end-to-end machine learning based approach is deemed to fail. Instead of a fully automated approach, we show a semi-supervised workflow that integrates seismic, geological data, and overpressure observations from previously drilled wells to map overpressure regions. Attribute maps are first extracted from a 3D seismic data set in an overpressured geological formation of interest. An auto-encoder is then used to learn a more compact representation of data, resulting in a reduced number of latent attributes. Then, a hand-tailored semi-supervised approach is applied, which is a combination of clustering method (here based on DBSCAN algorithm) and Bayesian classification to determine overpressure risk degree (no risk, mild, or high risk). The approach described in this study is compared to direct end-to-end models and significantly outperforms them with an error on a blind well prediction of around 25%. The overpressure probability maps resulting from the models can be used later for the optimization of drilling processes and to reduce drilling hazards.