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.

Machine Learning Microservice for Identification of Accident Predecessors

Drilling accidents prediction is the important task in well construction. Drilling support software allows observing the drilling parameters for multiple wells at the same time and artificial intelligence helps detecting the drilling accident predecessor ahead the emergency situation. We present machine learning (ML) algorithm for prediction of such accidents as stuck, mud loss, fluid show, washout, break of drill string and shale collar. The model for forecasting the drilling accidents is based on the "Bag-of-features" approach, which implies the use of distributions of the directly recorded data as the main features. Bag-of-features implies the labeling of small parts of data by the particular symbol, named codeword. Building histograms of symbols for the data segment, one could use the histogram as an input for the machine learning algorithm. Fragments of real-time mud log data were used to create the model. We define more than 1000 drilling accident predecessors for more than 60 real accidents and about 2500 normal drilling cases as a training set for ML model. The developed model analyzes real-time mud log data and calculates the probability of accident. The result is presented as a probability curve for each type of accident; if the critical probability value is exceeded, the user is notified of the risk of an accident. The Bag-of-features model shows high performance by validation both on historical data and in real time. The prediction quality does not vary field to field and could be used in different fields without additional training of the ML model. The software utilizing the ML model has microservice architecture and is integrated with the WITSML data server. It is capable of real-time accidents forecasting without human intervention. As a result, the system notifies the user in all cases when the situation in the well becomes similar to the pre-accident one, and the engineer has enough time to take the necessary actions to prevent an accident.

Wellbore Strengthening Design Criteria and Enhanced Fracture Gradient for Widening Stable Mud Weight Window and Enabling Safe and Efficient Drilling in Depleted Reservoirs

Drilling in permeable formations, especially depleted reservoirs, can particularly benefit from simultaneous wellbore shielding and strengthening functionalities of drilling mud compounds. The ability to generate simultaneous wellbore shielding and strengthening in reservoirs has potential to widen stable mud weight windows to drill such reservoirs without the need to switch from wellbore strengthening compound to wellbore shielding compound, and vice-versa. Wellbore shielding and strengthening experiments were conducted on three outcrop sandstones with three mud compounds. The wellbore shielding stage was conducted by increasing the confining and borehole pressures in 4-5 steps until both reached target pressures. CT scan images demonstrate consistency of the filtration rates with observed CT scanned mud cakes which are dependent on the sandstone pore size and mud compound particle size distributions. In wellbore strengthening stage, the borehole pressure was increased until fracture was initiated, which was detected via borehole pressure trend and CT scan imaging. The fractures generated were observed to be plugged by mud filter solids which are visible in the CT scan images. The extent of observed fracture solid plugging varies with rock elastic properties, fracture width and mud compound particle size distribution. Based on the laboratory test data, fracture gradient enhancement concept was developed for the mud compounds. In addition, the data obtained and observations from the tests were used to develop optimal empirical design criteria and guidelines to achieve dual wellbore strengthening and shielding performance of the mud compounds. The design criteria were validated on a well which was treated with one of the mud compounds based on its mud loss events during drilling and running casing.

Drilling Problems Forecast Based on Neural Network

This paper poses and solves the problem of using artificial intelligence methods for processing big volumes of geodata from geological and technological measurement stations in order to identify and predict complications during well drilling. Big volumes of geodata from the stations of geological and technological measurements during drilling varied from units to tens of terabytes. Digital modernization of the life cycle of well construction using machine learning methods contributes to improving the efficiency of drilling oil and gas wells. The clustering of big volumes of geodata from various sources and types of sensors used to measure parameters during drilling has been carried out. In the process of creating, training and applying software components with artificial neural networks, the specified accuracy of calculations was achieved, hidden and non-obvious patterns were revealed in big volumes of geological, geophysical, technical and technological parameters. To predict the operational results of drilling wells, classification models were developed using artificial intelligence methods. The use of a high-performance computing cluster significantly reduced the time spent on assessing the probability of complications and predicting these probabilities for 7-10 minutes ahead. A hierarchical distributed data warehouse has been formed, containing real-time drilling data in WITSML format using the SQL server (Microsoft). The module for preprocessing and uploading geodata to the WITSML repository uses the Energistics Standards DevKit API and Energistic data objects to work with geodata in the WITSML format. Drilling problems forecast accuracy which has been reached with developed system may significantly reduce non-productive time spent on eliminating of stuck pipe, mud loss and oil and gas influx events.

Elimination of complications occurred by hydrodynamic pressure during running casing

The elimination of complications occurred due to the hydrodynamic pressure at the wellbore during the running of the casing is one of the hardest tasks. During the well drilling in geologically complicated fields using drilling mud with 1800–2000 kg/m3 density, hydraulic fracture of the reservoir takes places while running of the casing due to the hydrodynamic pressure at the wellbore. This, in its turn, leads to the intensive drilling mud loss. To prevent such cases, it is necessary to have ra<0.65 coefficienct in well structure selection. This rate is the relation of well outer diameter to the wellbore diameter.

Earthquakes and very deep groundwater perturbation mutually induced

We report unique observations from drilling and hydraulic stimulation at a depth of approximately 4.3 km in two Enhanced Geothermal System (EGS) wells at the Pohang EGS site, South Korea. We surveyed drilling logs and hydraulic stimulation data, simulated pore pressure diffusion around the fault delineated by seismic and drilling log analyses, conducted acoustic image logging through the EGS wells, observed significant water level drops (740 m) in one of the two EGS wells, and obtained hydrochemical and isotopic variation data in conjunction with the microbial community characteristics of the two EGS wells. We discuss the hydraulic and hydrochemical responses of formation pore water to a few key seismic events near the hypocenter. We focused on how the geochemistry of water that flowed back from the geothermal wells changed in association with key seismic events. These were (1) a swarm of small earthquakes that occurred when a significant circulation mud loss occurred during well drilling, (2) the MW 3.2 earthquake during hydraulic stimulation, and (3) the MW 5.5 main shock two months after the end of hydraulic stimulation. This study highlights the value of real-time monitoring and water chemistry analysis, in addition to seismic monitoring during EGS operation.