bluuebunny/crossref_metadata_2025_split huggingface dataset:
Successful Geostopping Using a Recently Developed LWD Look-Ahead Ultra-Deep EM Resistivity Tool
Authors: Ma Jin , Riofrio Karol , Clegg Nigel , Sinha Supriya , Wu Hsu-Hsiang , Kotwicki Artur , Skorve Torstein , Bolstad Johansen Yngve 
Published: October 2022

Abstract
Geostopping is a critical operation due to wellbore-stability, well-control and HSE issues, requiring accuracy of a few meters. Reservoir boundary detection has previously been done with seismic applications and at-bit resistivity or near-bit gamma. However, seismic uncertainty can be up to 20m TVD and at-bit resistivity, and near-bit gamma requires penetrating the target. Early, accurate boundary detection, in vertical or near-vertical wells, requires technology that allows anticipation of boundaries ahead of the bit, enabling geostopping at a safe distance. A recently developed LWD ultra-deep electromagnetic (EM) tool was successfully deployed to geostop a wellbore at a target position. The system successfully predicted the top of the target formation 15m TVD ahead of the transmitter in real time, which was confirmed by gamma ray measurements. A subsequent run that drilled through the formation top confirmed the predicted top and formation resistivity. This is a game-changing technology for well construction, allowing for proactive and efficient wellbore placement.
The look-ahead, ultra-deep, EM LWD tool incorporates a deep-transmitting antenna 2.8m from the bit. To achieve a real-time look-ahead capability in near-vertical wells, anisotropic resistivity measurements are incorporated into the transmitting antenna collar, providing resistivity, anisotropy, and dip around and above the transmitter antenna while drilling. The reconstructed formation profile from the anisotropy measurement is combined with the deep measurement, enabling formation determination ahead of the bit. In a favourable condition, the integrated system can detect a formation boundary 30m or more ahead of the bit.
The EM tool was tested in challenging well conditions. The distance from the existing casing to the reservoir top was short and the tool was in a conductive formation (less than 0.6 Ω·m). This provided a very small depth interval in which to accumulate the necessary real-time EM measurements, and a relatively short depth of detection (DOD).
Despite the suboptimal conditions, the system was able to resolve the target formation top 15m TVD ahead of the EM transmitter while drilling. This was confirmed by gamma measurements after penetrating the formation, which was used as the primary technique to geostop the wellbore. The new EM look-ahead system now offers a more proactive solution to assist operators not only to geostop a wellbore at the planned position, but also to optimize the drilling parameters prior to any unexpected geological variations. This look-ahead EM technique provides more economic and efficient drilling solutions to optimize wellbore placement compared to existing techniques.
This new service makes it possible to proactively identify potential risks in vertical or low-inclination wellbores, helping to avoid drilling hazards and optimize casing shoe placement and coring operations.
Real-Time Compressional Sonic Log Prediction from Drilling and Mud Gas Data Using Machine Learning
Authors: Afifi Ruba M. , Anifowose Fatai A. , Mezghani Mokhles M. 
Published: October 2022

Abstract
Sonic logs are important for deriving elastic moduli of rocks, which can be useful in calculating in-situ stresses, estimating safe drilling mud weight, controlling wellbore stability, and constructing velocity models for seismic processing. Practically, determining the geomechanical information of the subsurface in real-time can alleviate operational risks and improve formation evaluation. Since sonic logs are not acquired in real-time, machine learning can be utilized to estimate them in real-time using drilling parameters and mud gas data.
This study uses Random Forest machine learning technique to predict compressional wave slowness in real-time by utilizing surface drilling parameters and mud gas data. Out of a total of five wells, the regression model is trained with data from four wells. The input parameters for each depth point include conventional drilling parameters (rate of penetration, torque, weight on bit, etc.) and mud gas data. Various preprocessing techniques were applied on the input parameters prior to model training to ensure good quality. Validation was performed on wells with existing sonic logs that were not included in the training. Model performance is measured by the correlation coefficient and the mean absolute percentage error.
Results show that predicting compressional sonic logs in real-time is feasible using machine learning. First, a model was tested to observe the effect of excluding certain depth intervals with high uncertainty in data values on model performance. The model's performance was enhanced and gave better correlation coefficient and lower mean absolute error. Therefore, cleaning data of uncertain intervals before running the model can improve sonic log prediction. Second, we investigated the effect of adding mug gas data as input features on model performance. Improvement in sonic log prediction was observed in some cases, and not in others. A sensitivity analysis was conducted on the developed models to determine the relative importance of the various input parameters on compressional sonic log prediction.
Valuable information can be extracted from sonic logs in real-time to reduce operational risks associated with drilling. This study demonstrates the effectiveness of utilizing real-time data for compressional wave slowness prediction, saving significant time and cost. Through the use of machine learning, data cleaning, and model fitting, prediction can be automated. This allows for scaling up the analysis on all the data available. We plan to apply additional preprocessing techniques, include more wells, and perform feature selection on the data to improve the prediction accuracy.
Machine Learning Look-Ahead Application for Geosteering with Uncertainty Estimation
Authors: Miotti Fabio Marco , Sreekantan Jaijith , Meunier Kara 
Published: October 2022

Abstract
Horizontal well placement activities aim to keep the wellbore within the hydrocarbon pay zone to maximize the production from wells. This novel data-driven solution provides well engineers with a tool for updating the petrophysical properties ahead of the bit from log measurements (offset, horizontal wells) and geophysical attributes (i.e. Amplitude Versus Offset properties). The goal is to automate the reservoir model update while drilling by exploiting the machine learning capabilities integrated in DataOps architecture.
The data-driven approach consumes well log data and geophysical attributes (AVO) to provide an updated estimation of the petrophysical properties ahead of the bit in geosteering scenarios. In the initial stage, the system represents the current state of knowledge of the reservoir as the machine learning algorithm is trained on the available static data (offset wells, geophysical attributes, seismic interpretation). While drilling, new data is assimilated by the system to increase the state of knowledge of the reservoir. The new data set is then interpreted and used to retrain the neural network to update the estimated reservoir properties.
The solution produces a continuous update of the reservoir rock properties ahead of the bit in terms of porosity, water saturation and clay. These rock properties are estimated by machine learning, and they support the decision-making of the drilling engineers as they are linked to the formations encountered by the bit while drilling.
The rock properties distribution is visually represented by a 3D spatial distribution that reflects the geometry of the reservoir. In essence, the 3D grid is the same used for defining the AVO elastic attributes such as acoustic impedance and Vp/Vs ratio. The solution also provides as outcome the confidence of the prediction (uncertainty assessment), as for each rock property the algorithm estimates a probability density function.
This machine learning solution is integrated in a DataOps architecture to enable straightforward scalability in E&P organizations to guarantee data hygiene for both input and output data and to enable overall easy access to data and metadata associated with the reservoir model. This system enables geosteering look-ahead of the bit via a machine learning application that uses well log measurements and geophysical attributes.
Delineation of Multi-Phase Wellbore and Formation Fluid Distribution in Abu Dhabi's Onshore Horizontal Wells
Authors: Kim Yonghwee , Abdel Aziz Ahmed Saber 
Published: October 2022

Abstract
Abu Dhabi's onshore oil fields are generally mature and contain carbonate reservoirs with light oil and gas. Various well types have been employed, such as vertical, highly deviated, and horizontal wells with cased hole or openhole completions. Surveillance of wellbore and formation fluid attributes is essential for reservoir management decisions and hydrocarbon production enhancement. This paper discusses surveillance techniques to understand multi-phase flow characteristics and reservoir saturation.
Formation saturation monitoring and production profiling have been performed actively using pulsed neutron logging (PNL) and array production logging (APL) techniques. Challenges in employing the APL method in Abu Dhabi onshore fields include (1) many wells are horizontal with barefoot completions, (2) horizontal sections extend typically more than 2000-ft long with irregular and undulating trajectories, and (3) wells contain asphaltene, debris, and other materials preventing optimal spinner flowmeter functionality. After reviewing each well condition, well environment-specific combinations of APL, nuclear production logging applications for holdups and water velocity calculations, and an advanced three-phase formation saturation analysis technique were determined. This approach overcame several challenges to deliver surveillance objectives.
We demonstrate four case examples, delineating well-based production and formation saturation profiles in various conditions. Two nuclear measurements exhibiting different sensitivities to oil and gas were combined to compute three-phase formation saturation. When a horizontal openhole wellbore was severely under-gauged, the pulsed neutron-based holdup application was used to avoid an APL tool deployment that might result in tool damage and unsatisfactory data acquisition. Additionally, for wells with good wellbore conditions, pulsed neutron-based and APL-based holdup data sets were acquired, and analysis results were compared. A stationary water velocity calculation method when water cut was high was also adopted to identify downhole water sources, and in-situ water production profiles from APL and nuclear applications were compared.
An effort to evaluate production profile and in-situ saturation effectively from highly deviated- and horizontal wellbores to improve hydrocarbon production is described. The delineation of production profiles and formation fluid distribution allowed operators to determine reservoir and production management strategies.
Halite Pore Space Plugging Evaluation Based on the Logging Data for the Cambrian Ara Group Intra-Salt Tight Carbonate Reservoirs (South Oman Salt Basin)
Authors: Smirnov Dmitrii , Hashmi Mohammed , Al Hadhrami Abdullah , AL Isaee Omar , Fanis Khayrutdinov , Agrawal Pankaj , Kumar Kamlesh , Kaabi Saqer 
Published: October 2022

Abstract
There has been a string of exploration discoveries in Cambrian Ara Group intra-salt carbonate reservoirs in the South Oman. Some of the reservoirs failed to produce at expected rates due to halite presence in the pore space, which is one of the highest risks for hydrocarbon exploration in this area. The objective of this study was to define novel quantitative algorithm to estimate halite volumes in the pore space.
Although halite cementation is known as a major risk for hydrocarbon production, a limited number of studies have focused on the impact of halite cementation on productivity, well integrity and ultimate recovery. The quantitative halite volume evaluation based on the logging data required an integrated approach to open hole and cased hole data collection and analysis. The open hole data included: thin section and XRD core analysis, density, neutron, sonic, resistivity, formation pressure and sigma capture-cross section. Net pay cut-off based on calculated halite volume was defined. Cased hole production logging was used to confirm net cut-off definition. The integrated logging data analysis and the developed quantitative halite volume evaluation algorithm mainly based on sigma log was successfully implemented in a few ongoing development projects. The evaluation results were successfully used for hydrocarbon volume calculations, well placement and perforation interval selection to improve production performance and reduce field development uncertainty in recoverable volumes. Understanding of consistent pattern for halite distribution allow improve exploration success. Avoiding perforation of intervals with high halite content in the pore space reduced production deferment due to surface equipment and tubing plugging by salt. Appreciation for the role of halite plugging in the reservoirs properties distribution and deterioration significantly improve history match for hydrodynamic models. The evaluation algorithm for quantitative halite volume estimation in the pore space have been developed and introduced for the first time and benefits from its implementation are expected for the upcoming exploration and development projects for the salt encased carbonate reservoirs.
Uncertainty Analysis for Seismic Salt Interpretation by Convolutional Neural Networks
Authors: Jiang Fan , Osypov Konstantin , Toms Julianna 
Published: October 2022

Abstract
One of the significant challenges in seismic interpretation is to accurately delineate subsurface features and quantify the uncertainty of the interpretation results due to the non-unique nature of seismic processing and imaging. Salt interpretation usually has limited resolution and relies upon an interpreter's experience with a limited set of geological concepts. In seismic interpretation, especially salt interpretation, researchers have focused on improving the accuracy of pixel predictions by developing various neural network architectures, such as Dense U-Net, Attention U-Net, Residual U-Net, etc. Studying uncertainty quantification of point predictions is important in assessing prediction quality. In this paper, we implemented Monte-Carlo dropout analysis in the variational inference setting with a Bayesian Neural network (BNN) to analyze the aleatoric and epistemic uncertainty of the salt classification. Our approach helps to analyze the posterior distribution from the variational inference and quantitively measure the range of predictive probability distribution.
Advanced 3D Seismic Studies Delineate Paleo Geomorphology and Facies Distribution at the End of the Thamama Depositional History, Dubai Offshore
Authors: Muhammad Adnan , Goddyn Xavier , Moreira Luis , D'Cruz Melroy Anthony , Freile Juan Pablo , Mehrotra Nagendra , Storey Simon Payne , Han Christopher 
Published: October 2022

Abstract
Exploration activities across the world's mature oil basins face increasing challenges as usually the remaining plays include subtle traps with strong stratigraphic components, situated in areas with well-established oil sources, timing and migration paths.
The advancement of seismic acquisition and processing coupled with inversion, frequency enhancement and rock modeling provides sharper images of different seismic events, ultimately related to changes in the studied rock. These resulting images, illuminated the potential trapping mechanisms and the identification of new exploration prospects when incorporated with sedimentological, tectonic and paleo-geomorphological models based on well data.
Recent exploration success and subsequent field development in the offshore Dubai study area (Figure 1) has demonstrated a highly complex setup in both structural and sedimentological terms. This new play is a fringing reef belonging to a late Thamama cycle located on the flank of one of Dubai's major fields. In this area the legacy seismic from the 90's had defined a potential field closure which has proven to be more complex under the light of the latest 2019 seismic acquisition campaign and initial development results. Regional faulting shows sealing capacity over a specific direction making additional entrapment possible and providing strong exploration potential. On the reservoir side, the rock facies clearly control the water saturation profiles; increasing the thickness of the transition zone by tens of feet towards the flank as seen in the development wells. Additionally, karsts and presence of open fractures created challenges for geosteering horizontal wells close to the reservoir roof and within a slim oil column.
This paper illustrates the seismic interpretation of this subtle and complex reefal carbonate play, incorporating advanced seismic inversion products like Ji-Fi inversion (Joint Impedance – Facies Inversion), high definition frequency blends and advanced seismic attributes, combined with geological results. It will explain how these modern technologies helped in understanding this complex field in terms of its geomorphological setup and their impacts on field development strategies.
Overburden Drilling Challenges & Their Possible Mitigations Using Integrated Seismic Structure Characterization in a Giant Carbonate Field in Middle East
Authors: Ahmad Jamal , Mohamed Naeema Ahmed , Alhammadi Sarra Husain , Almazrouei Khulood Ahmed 
Published: October 2022

AbstractThe ability to identify subtle structural and paleo geomorphological features within carbonates may significantly reduce wide range of drilling challenges including stuck pipe, breakouts, casing failure, lost circulation, hole collapse, washouts, etc. Current study emphasis on integrated approach to discuss drilling challenges, their possible mitigations and subsurface risk assessment.This study is a multi-disciplinary application of integrated borehole and advanced seismic attributes to identify hazards, mainly linked with drilling problems. In this regards, paleo structure mapping is initially utilized to broadly outline areas in the field that may be associated with potential karst collapse features development. These karst collapse features are mainly responsible for dynamic mud losses and may also cause other problems (e.g., casing failure, etc.). One of the main challenges observed to confidently characterize karst collapse and other subtle features is that seismic data contains a lot of overburden noise hence an improved approach is proposed to eliminate seismic noise and enhance S/N ratio. Advanced seismic attribute volumes are then produced to capture full field lateral as well as vertical distribution of subtle features. Integrated results are calibrated with borehole image, core, logs, PLT, mud losses, Hall plot and CUM injection in vicinity. A novel approach is introduced to calculate unmonitored injection area of influence and other associated subsurface risks assessment.An improved Integrated approach used in this paper shows promising results. Full field distribution of karst collapse features is outlined confidently and results are validated with dynamic data and diffraction seismic imaging. Vertical extent of karstified zone is clearly captured in cross sectional view, which ties with petrophysical zonation, PLT, core and dynamic mud losses. Furthermore, the novel approach of Injection area of influence calculation improves subsurface understanding and highlight other challenges e.g. pressure anomalies or future disposal wells planning, etc. Two examples are presented in this paper for reference.An approach defined in this paper improves confidence in subsurface hazards identification, well plan optimization and minimizing unexpected costs associated with mud losses, sidetracks, etc.
Longest Extended Reach Drilling Well Drilled in Middle East Geosteered Within 5 Ft Stratigraphic Thickness Target Zone
Authors: Abbas Nashat , Al Nokhatha Jamal , Haider Syed , Radwan Khaled , Al Hashmi Mustafa , Al Nayadi Khaloud , Alqaydi Nouf , Fares Wael , Aki Ahmet , M.K. Abdou Eslam 
Published: October 2022

Abstract
In order to develop a giant mature carbonate field offshore Abu Dhabi, four environmental Islands were built in shallow water. Due to the large areal extension of the field, most wells are Extended Reach. This paper presents a case study of the longest well drilled in the UAE. Planning to drill such an Extended Reach well with maximum reservoir contact, starts long before execution. Not only, competent rigs, capable of delivering enough mechanical and hydraulic energy to drill such well, but also placing and navigating the lateral section in multilayered anticlinal reservoir with layer cake geometry. Significant geologic uncertainties of complicated carbonate facies heterogeneities is a major challenge. Detailed study of the reservoir behavior according to pre-existing nearby wells was carried out, as well as understanding the geologic structural setting of the area since the target zone consists of 5ft thick limestone layer that overlays a 3 ft. thick dolomitic limestone with excellent permeability. The crucial success factors were; firstly, to steer in such a thin reservoir for long distances, using LWD tools to differentiate dolomite from the nearby limestone layer. Secondly, to account for possible lateral facies change, the sensors needed to be as close as possible to the bit. Considering +28600 ft. horizontal section, remarkable challenges arose related to drilling tool capability and well path smoothness. The drilling team was concerned about using nuclear source density tools to navigate the well path and needed to avoid high dogleg to prevent any fishing jobs associated with source tools, especially in fractured areas. The well was drilled to record depth of more than 45300 ft. with a horizontal section of 27,500 ft. inside reservoir. Utilizing Rotary Steerable System in all hole sections helped to minimize hole tortuosity along the well and with the help of LWD tools the well was accurately steered within target zone boundaries. Utilizing advance mud telemetry made it possible to receive data from 45,300 ft. away from surface. Successful well placement was the main factor to achieve positive overall performance in the field. This success has opened the door to drill more wells that are more challenging. In addition, it practically proved that proper planning and execution can push the limits further and gave confidence to drill even deeper. As a result, we are now aiming to drill deeper wells up to 50,000 ft.