Impact of Well Placement in the Fractured Geothermal Reservoirs Based on Available Discrete Fractured System

Well placement in a given geological setting for a fractured geothermal reservoir is necessary for enhanced geothermal operations. High computational cost associated with the framework of fully coupled thermo-hydraulic-mechanical (THM) processes in a fractured reservoir simulation makes the well positioning a missing point in developing a field-scale investigation. To enhance the knowledge of well placement for different working fluids, we present the importance of this topic by examining different injection-production well (doublet) positions in a given fracture network using coupled THM numerical simulations. Results of this study are examined through the thermal breakthrough time, mass flux, and the energy extraction potential to assess the impact of well position in a two-dimensional reservoir framework. Almost ten times the difference between the final amount of heat extraction is observed for different well positions but with the same well spacing and geological characteristics. Furthermore, the stress field is a strong function of well position that is important concerning the possibility of high-stress development. The objective of this work is to exemplify the importance of fracture connectivity and density near the wellbores, and from the simulated cases, it is sufficient to understand this for both the working fluids. Based on the result, the production well position search in the future will be reduced to the high-density fracture area, and it will make the optimization process according to the THM mechanism computationally efficient and economical.

A Novel Workflow for Geosteering a Horizontal Well in a Low Resistivity Contrast Anisotropic Environment: A Case Study in Semoga Field, Indonesia

This paper describes the combination of strategies deployed to optimize horizontal well placement in a 40 ft thick isotropic sand with very low resistivity contrast compared to an underlying anisotropic shale in Semoga field. These strategies were developed due to previously unsuccessful attempts to drill a horizontal well with multiple side-tracks that was finally drilled and completed as a high-inclined well. To maximize reservoir contact of the subject horizontal well, a new methodology on well placement was developed by applying lessons learned, taking into account the additional challenges within this well. The first approach was to conduct a thorough analysis on the previous inclined well to evaluate each formation layer’s anisotropy ratio to be used in an effective geosteering model that could better simulate the real time environment. Correct selections of geosteering tools based on comprehensive pre-well modelling was considered to ensure on-target landing section to facilitate an effective lateral section. A comprehensive geosteering pre-well model was constructed to guide real-time operations. In the subject horizontal well, landing strategy was analysed in four stages of anisotropy ratio. The lateral section strategy focused on how to cater for the expected fault and maintain the trajectory to maximize reservoir exposure. Execution of the geosteering operations resulted in 100% reservoir contact. By monitoring the behaviour of shale anisotropy ratio from resistivity measurements and gamma ray at-bit data while drilling, the subject well was precisely landed at 11.5 ft TVD below the top of target sand. In the lateral section, wellbore trajectory intersected two faults exhibiting greater associated throw compared to the seismic estimate. Resistivity geo-signal and azimuthal resistivity responses were used to maintain the wellbore attitude inside the target reservoir. In this case history well with a low resistivity contrast environment, this methodology successfully enabled efficient operations to land the well precisely at the target with minimum borehole tortuosity. This was achieved by reducing geological uncertainty due to anomalous resistivity data responding to shale electrical anisotropy. Recognition of these electromagnetic resistivity values also played an important role in identifying the overlain anisotropic shale layer, hence avoiding reservoir exit. This workflow also helped in benchmarking future horizontal well placement operations in Semoga Field. Technical Categories: Geosteering and Well Placement, Reservoir Engineering, Low resistivity Low Contrast Reservoir Evaluation, Real-Time Operations, Case Studies

Utilizing NMR Workflow to Optimize Power Water Injector Placement in the Presence of Tar Barriers

The objective of this work is to optimize the placement of horizontal power water injector (PWI) wells in stratified heterogeneous carbonate reservoir with tar barriers. The key to successful reservoir navigation is a reliable real-time petrophysical analysis that resolves rock quality variations and differentiates tar barriers from lighter hydrocarbon intervals. An integrated workflow has been generated based on logging-while drilling (LWD) triple combo and Nuclear Magnetic Resonance (NMR) logging data for fluid identification, tar characterization and permeability prediction. The workflow has three steps; it starts with the determination of total porosity using density and neutron logs, the calculation of water-filled porosity from resistivity measurements and an additional partitioning of porosity into bound and free fluid volumes using the NMR data. Second, the total and water-filled porosity, the NMR bound fluid and NMR total porosity are used as inputs in a hydrocarbon compositional and viscosity analysis of hydrocarbon-bearing zones for the recognition of tar-bearing and lighter hydrocarbon intervals. Third, in the lighter hydrocarbon intervals, NMR logs are further analyzed using a multi-cutoff spectral analysis to identify microporous and macroporous zones and to calculate the NMR mobility index. The ideal geosteering targets are highly macroporous rocks containing no heavy hydrocarbons. In horizontal wells, the method is validated using formation pressure while drilling (FPWD) measurements. The procedure has been utilized in several wells. The original well path of the first injector was planned to maintain a safe distance above an anticipated tar-bearing zone. Utilizing the new real-time viscosity evaluation, the well was steered closer to the tar zone several feet below the original plan, setting an improved well placement protocol for subsequent injectors. In the water- or lighter hydrocarbon-bearing zones, spectral analysis of NMR logs clearly accentuated micro- and macroporous carbonate intervals. The correlation between pore size and rock quality has been corroborated by FPWD mobility measurements. In one well, an extremely slow NMR relaxation may indicate wettability alteration in a macroporous interval. An integrated real-time evaluation of porosity, fluid saturation, hydrocarbon viscosity and pore size has enhanced well placement in a heterogeneous carbonate formation where tar barriers are also present. The approach increased well performance and substantially improved reservoir understanding.

Unconventional Engineering Toward Efficient Geosteering and Well Placement - Logging-While-Drilling in an Oil-Based Mud Environment

This paper presents a success story of deploying new technology to improve geosteering operations in an unconventional horizontal well. A new-generation logging-while-drilling (LWD) imaging tool, that provides high resolution resistivity and ultrasonic images in an oil-based mud environment, was tested while drilling a long lateral section of an unconventional horizontal well. In addition to improving the geosteering operations, this tool has proven the ability to eliminate the wireline image log requirements (resistivity and ultrasonic), hence reducing rig time significantly. The LWD bottomhole-assembly (BHA) included the following components: gamma ray (GR), density, neutron, resistivity, sonic, density imager, and the newly deployed dual imager (resistivity and ultrasonic). The dual imager component adds an additional 15-ft sub to the drilling BHA, which includes four ultrasonic sensors orthogonal to each other, and two electromagnetic sensors diametrically opposite to each other (reference figure 1). This new technology was deployed in an unconventional horizontal well to help geosteer the well in the intended zone, which led to an improvement in well placement, enhanced the evaluation of the lateral facies distribution, and allowed better identification of natural fractures. The dual images provided the necessary information for interpreting geological features, drilling induced features, and other sedimentological features, thus enhancing the multistage hydraulic fracturing stimulation design. In addition, an ultrasonic caliper was acquired while drilling the curve and lateral section, providing a full-coverage image of the borehole walls and cross-sectional borehole size. The unique BHA was designed to fulfill all the directional drilling, formation evaluation and geosteering requirements. A dynamic simulation was done to confirm the required number of stabilizers, and their respective locations within the BHA, to reduce shock and vibration, borehole tortuosity and drilling related issues, thereby improving over-all performance. Real-time drilling monitoring included torque and drag trending, back-reaming practices and buckling avoidance calculations, which were implemented to support geosteering, and for providing a smooth wellbore for subsequent wireline and completion operations run in this well. A new generation dual-image oil-based mud environment LWD tool was successfully deployed to show the multifaceted benefits of enhanced geo-steering/well placement, formation evaluation, and hydraulic fracturing design in an unconventional horizontal well. Complexities in the multifunctioning nature of the BHA were strategically optimized to support all requirements without introducing any significant risk in operation.

Geology-Driven EUR Forecasting in Unconventional Fields

EUR (Estimated Ultimate Recovery) forecasting in unconventional fields has been a tough process sourced by its physics involved in the production mechanism of such systems which makes it hard to model or forecast. Machine learning (ML) based EUR prediction becomes very challenging because of the operational issues and the quality of the data in historical production. Geology-driven EUR forecasting, once established, offers EUR forecasting solutions that is not affected by operational issues such as shut-ins. This study illustrates the overall methodology in intelligent fields with real-time data flow and model update that enables optimization of well placement in addition to EUR forecasting for individual wells. A synthetic but realistic model which demonstrates the physics is utilized to generate input data for training the ML model where the spatially-distributed geological parameters including but not limited to porosity, permeability, saturation have been used to describe the production values and ultimately the EUR. The completion is given where the formation characteristics vary in the field that lead to location-dependent production performance leading to well placement optimization based on EUR forecasting from the geological parameters. The algorithm not only predicts the EUR of an individual well and makes decision for the optimum well locations. As the training model includes data of interfering wells, the model is capable of capturing the pattern in the well interference. Even though a synthetic but realistic reservoir model is constructed to generate the data for the aim of assisting the ML model, in practice, it is not an easy task to (1) obtain the input parameters to build a robust reservoir simulation model and (2) understanding and modeling of physics of fluid flow and production in unconventionals is a complex and time-consuming task to build real models. Thus, data-driven approaches like this help to speed up reservoir management and development decisions with reasonable approximations compared to numerical models and solutions. Application of machine learning in intelligent fields is also explained where the models are dynamically-updated and trained with the new data. Geology-driven EUR forecasting has been applied and relatively-new in the industry. In. this study, we are extending it to optimize well placement in intelligent fields in unconventionals beyond other existing studies in the literature.

Advanced Multi-Disciplinary Simulation Workflow for Well Placement, Hydraulic Fracturing and Production Optimization Applied to the Diyab Unconventional Play in Onshore UAE

The Diyab play is an emerging unconventional play in the Middle East. Up to date, reservoir characterization assessments have proved adequate productivity of the play in the United Arab Emirates (UAE). In this paper, an advanced simulation and modeling workflow is presented, which was applied on selected wells located on an appraisal area, by integrating geological, geomechanical, and hydraulic fracturing data. Results will be used to optimize future well landing points, well spacing and completion designs, allowing to enhance the Stimulated Rock Volume (SRV) and its consequent production. A 3D static model was built, by propagating across the appraisal area, all subsurface static properties from core-calibrated petrophysical and geomechanical logs which originate from vertical pilot wells. In addition, a Discrete Fracture Network (DFN) derived from numerous image logs was imported in the model. Afterwards, completion data from one multi-stage hydraulically fracked horizontal well was integrated into the sector model. Simulations of hydraulic fracturing were performed and the sector model was calibrated to the real hydraulic fracturing data. Different scenarios for the fracture height were tested considering uncertainties related to the fracture barriers. This has allowed for a better understanding of the fracture propagation and SRV creation in the reservoir at the main target. In the last step, production resulting from the SRV was simulated and calibrated to the field data. In the end, the calibrated parameters were applied to the newly drilled nearby horizontal wells in the same area, while they were hydraulically fractured with different completion designs and the simulated SRVs of the new wells were then compared with the one calculated on the previous well. Applying a fully-integrated geology, geomechanics, completion and production workflow has helped us to understand the impact of geology, natural fractures, rock mechanical properties and stress regimes in the SRV geometry for the unconventional Diyab play. This work also highlights the importance of data acquisition, reservoir characterization and of SRV simulation calibration processes. This fully integrated workflow will allow for an optimized completion strategy, well landing and spacing for the future horizontal wells. A fully multi-disciplinary simulation workflow was applied to the Diyab unconventional play in onshore UAE. This workflow illustrated the most important parameters impacting the SRV creation and production in the Diyab formation for he studied area. Multiple simulation scenarios and calibration runs showed how sensitive the SRV can be to different parameters and how well placement and fracture jobs can be possibly improved to enhance the SRV creation and ultimately the production performance.

Artificial Intelligence for Infill Well Placement and Design Optimization in Multi-layered/stacked Reservoirs Under Subsurface Uncertainty

Effective well placement and design planning accounts for subsurface uncertainties to estimate production and economic outcomes. Reservoir modelling and simulation workflows build on ensemble approaches to manage uncertainties for production forecasting. Ensemble generation and interpretation requires a higher degree of automation analytics and artificial intelligence for fast value extraction and decision support. This work develops practical intelligent workflow steps for a robust infill well placement and design scenario in multi-layered/stacked reservoirs under uncertainty. Potential well targets are classified by an opportunity index defined by a combination of rock and hydrocarbon flow properties as well as connected volumes above a minimum economic volume. Unsupervised learning techniques are applied to automate the search for alternative target areas, so-called hotspot regions. Supervised machine/learning models are used to predict infill well performance based on simulated and/or past production experience. A stochastic evaluation including all ensemble cases is used to capture uncertainty. Vertical, deviated, horizontal and multilateral wells are proposed to optimally target single or connect to multiple hotspot regions under technical and economic constraints. A structured workflow design is applied to a multi-layered/stacked reservoir model. Subsurface uncertainties are described and captured by multiple model realizations, which are constrained in areas of historical wells. An infill well program for a multi-layered/stacked reservoir is defined for incremental production increase under economic constraints. This work shows how robust well location and design builds on the full ensemble of cases with a high degree of automation using analytics and machine-learning techniques. Both production and economic targets are calculated and compared to a reference case for robust solution verification and probability of success. In conclusion, an overall reservoir-driven field development strategy is required for efficient execution. However, automation is well applicable to repetitive workflow steps which includes hotspot search in an ensemble of validated reservoir models. This work presents an integrated, intelligent solution for informed decision making on infill drilling locations and refined well design. Higher degree of automation with embedded intelligence are discussed from case generation to hotspot identification. Aspects of model calibration in a producing field environment are addressed.

Automated Geosteering Optimization Using Machine Learning

Geosteering is an important area and its quality determines the efficiency of formation drilling by horizontal wells, which directly affects the project NPV. This paper presents the automated geosteering optimization platform which is based on live well data. The platform implements online corrections of the geological model and forecasts well performance from the target reservoir. The system prepares recommendations of the best reservoir production interval and the direction for horizontal well placements based on reservoir performance analytics. This paper describes the stages of developing a comprehensive system using machine-learning methods, which allows multivariate calculations to refine and predict the geological model. Based on the calculations, a search for the optimal location of a horizontal well to maximize production is carried out. The approach realized in the work takes into account many factors (some specific features of geological structure, history of field development, wells interference, etc.) and can offer optimum horizontal well placement options without performing full-scale or sector hydrodynamic simulation. Machine learning methods (based on decision trees and neural networks) and target function optimization methods are used for geological model refinement and forecasting as well as for selection of optimum interval of well placement. As the result of researches we have developed the complex system including modules of data verification and preprocessing, automatic inter-well correlation, optimization and target interval selection. The system was tested while drilling hydrocarbons in the Western Siberian fields, where the developed approach showed efficiency.