bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reservoir Pressure Prediction Using Combined AI and Material Balance Approach
Authors: Badmaev Dorzhi , Ilyasov Ilnur , Braik Fahed 
Published: November 2024

Abstract
Monitoring reservoir pressure with time is an important part of reservoir management. The conventional approach using hydrodynamic modeling to predict reservoir pressure may not always match the surveillance data due to long history matching and model updating process, too large timesteps, not perfect match at well level, etc.
The objective of this study is to develop a combined AI and physics driven method for fast and robust procedure to estimate reservoir pressure in space and time, based on a hybrid approach using production injection data, machine learning and material balance.
The workflow consists of the following steps:
Production injection and pressure data preparation and cleansing. Wells clustering and material balance calculations using optimization algorithm to match surveillance data. Pressure interpolation in time using ML methods including surveillance data per well (at least 5 tests), production injection data and material balance prediction. Pressure interpolation in space based on interpolated pressure in time for each well considering the radius of influence of a well estimated by its cumulative production or injection.
ML methods are very efficient in interpolation mode. However, in extrapolation mode (forecasting) ML methods usually are not reliable due to lack of physics as it is shown in the study.
Material balance is a fundamental reservoir engineering tool for analysis of reservoir performance; however, it has some limitations, one of which is an uncertainty in well drainage volumes at well level
In this study it is presented how to use a combined method which takes advantages from both approaches and significantly increases accuracy and reliability in reservoir pressure prediction and short-term forecasting.
The developed method was applied to a variety of carbonate reservoirs across Abu Dhabi fields. Reservoir pressure was predicted with high accuracy in wells with enough available pressure tests. An uncertainty score using test frequency, reservoir quality and production capability can be used to identify areas where pressure testing will add more value.
The interpolated pressure maps were compared with the conventional surveillance pressure maps and with maps from simulation models. The predicted maps look quite similar to simulation model, which proved applicability and efficiency of developed workflow
The spatiotemporal interpolation of reservoir pressure considering simultaneous production injection data presented in this paper is an innovative and practical approach improving from previous methods available in literature. This method is particularly useful to run monthly when new production and injection data is available, and reservoir pressure needs to be estimated to optimize production and injection targets.
Successful Implementation of Advanced Inversion in a New Field Achieved 100% Reservoir Exposure in a Complex Heterogeneous Reservoir – An Integrated Solution Comprising Near and Far Field LWD Measurements
Authors: Ahmad Fazeel , Pacheco Ferran , Radman Velimir , Jalalh Ali Ahmed , Mumtaz Asim , Potshangbam Sanathoi , Augustine Anthony , Soliman Ahmed , Deng Lichuan 
Published: November 2024

Abstract
In a new field in the UAE offshore, the field development plan was initiated by drilling the first lateral oil producer in a low-resistivity reservoir characterized by structural uncertainty, reservoir heterogeneity, and the presence of high-permeability streaks that can act as conduits for water, potentially compromising the well's productivity. To achieve the navigation objective, two target reservoir subzones, separated by an impermeable dense zone, each with independently associated water risks, need to be exposed.
To maximize reservoir exposure, maintain a safe distance from denser layers, and stay close to the top of the second target zone to avoid the permeable lower part, any changes in the water level must be mapped accurately. A feasibility study assessed the mapping results of an Extra Deep Azimuthal Resistivity (EDAR) inversion in this environment. This hybrid inversion method uses adaptive parameter scanning and efficiently computes numerous intermediate models to enable accurate high-definition multiple boundary mapping. As a result of this study, the EDAR tool was identified as an optimal solution and integrated into a triple combo BHA.
The advanced inversion from the EDAR tool enhanced the understanding of the geological setting around the well area. EDAR inversions consistently mapped the reservoir boundaries, and integration with near-wellbore measurements aided in optimizing well placement for the most favorable reservoir properties. The confident mapping of the second target top ensured that the trajectory stayed close to it, thus avoiding exposure to the permeable zone in the lower part. The EDAR inversion was used to update the structural changes along the wellbore in real-time. The information gathered from EDAR advanced inversion established that no elevated water zone was observed within the thickness of the target reservoir subzones. 3D visualization of acquired information from EDAR inversion, such as distribution of resistivity and structural variations, gives a better insight into the reservoir architecture. Integrating petrophysical analysis with EDAR inversion summary will help optimize the completion design and can be considered an immediate after-the-drilling service application. Integrating the inversion information into the 3D model reduces uncertainty and aids field development planning.
Innovative Placement of 3D Shaped Cutters for Hard and Abrasive Formations
Authors: Reboul S. , AI-Khayari M. , Hajri B. , Busaidi Y. , Almaawali S. , Alsiyabi K. , AI-Shaqsi A. , Tissot V. 
Published: November 2024

Current well designs that aim to efficiently drill long heterogeneous wellbore sections in a single run make cutter wear mitigation and overall performance increasingly important. However, in these harsh conditions, the durability of PDC bits and their high rate of penetration (ROP) in many formations are often compromised by cutter damage, limiting total footage and penetration rates. These limitations frequently necessitate using multiple bits to complete the section, increasing the overall cost for operators to drill to Total Depth (TD).
Therefore, mitigating PDC cutter wear while improving indentation and shearing action, especially in hard rock formations, is crucial for the effective application of PDC technology in modern wellbores. To address these issues, a Bit company has developed a new generation of tools with an innovative cutter arrangement and specific cutter shapes.
These bits pre-fracture the rock before shearing begins, achieving higher ROPs and more stable boreholes, particularly in hard rock formations. The PDC bit designs presented in this paper result from extensive research aimed at overcoming historical performance challenges in long, hard, and interbedded formations. The design configuration combines multiple rows of cutters with different geometries. Cutters with force-concentrating tip profiles initially weaken and fracture the rock, followed by another set of specially shaped cutters that shear the weakened rock more efficiently.
Decarbonization and Successful Zonal Isolation of a Challenging Well Using a High-Performance Low-Carbon Emission System Containing Less than 30% Portland Mass
Authors: Iskander Mirette , De Barros Adelson Jose Calleia , De Melo Alexandre Bezerra , Benberber Mohammed Rebbou , Gupta Shruti , Aghayarova Nargiz , Manga Patrick 
Published: November 2024

Abstract
The implementation of a novel technology that reduced Portland cement content by up to 70% was pumped in a complex 9 5/8-in. casing section of an offshore well, which resulted in a significant reduction to carbon footprint. As the oil and gas industry faces the challenges of decarbonization, one of the focus areas that remains is carbon emissions generated by the cement industry.
The innovative and tailored system described in this study is designed to deliver high performance at a lower density than conventional systems for improved barrier dependability. Proven laboratory results show that the system enables higher compressive strength at a lower density, increases ductility, and reduces permeability and corrosion resistance. The main engineering and operational advantage of this system is its capability for deployment without the requirement for specialized or additional equipment, which provides an operationally efficient and sustainable barrier.
The primary challenge cementing this section was to reduce the equivalent circulating density (ECD) caused by the elevated risk of losses predicted by offset wells. This required the use of a specialized low-density slurry system with durable mechanical properties. Another challenge was to lower carbon emissions associated with large slurry volumes because conventional high-Portland content cement systems do not align with sustainability goals. Modern technology was applied, and the well was successfully delivered with all achieved objectives. This case demonstrated that sustainable technology is not only efficient to reduce carbon footprint, but also to successfully achieve zonal isolation without downgrading the final cement quality compared to conventional cement systems.
Cement production accounts for approximately 8% of global anthropogenic carbon dioxide (CO2) emissions annually (Abdelaal et al.). The benefits of the application of this innovative technology in future wells are discussed, which can help reduce carbon footprint while slurry performance to achieve the zonal isolation.
The Used of an Intelligent Data Assimilation Protocol for Plume Characterization of CO2 Sequestration in Saline Aquifers
Authors: Sun Qian , Zhang Miao 
Published: November 2024

Abstract
The gas plume characterization of CO2 sequestration processes is a typical inverse problem, the solution of which could include inevitable non-unique solutions and uncertainties. This work aims at structuring a plume characterization protocol via coupling deep neural network models and ensembled Kalman Filter algorithm by analyzing injection and monitoring well bottomhole pressure data. Considering the multiple sequestration mechanisms, the output of the model includes the spatiotemporal evolution of free gas plume and mineralization profiles. In this study, one inverse model and two types of forward-looking models are developed. The inverse model predicts geological characteristics using field pressure data as input. The forward-looking models aim to simulate pressure responses and the evolution of the gas plume. Initially, input data is processed through the inverse model to estimate the spatial distribution of geological properties. The forward-looking models couple the ensemble Kalman filter to refine the predictions made by the inverse model. After the predictions are aligned with the field data, these models proceed to forecast the distribution of the gas plume in different form of presences. The proposed methodology was evaluated using an ideal case and a field case using the geological data collected from a real aquifer. The primary source of uncertainty in gas plume characterization stems from the inherent non-uniqueness of solutions to inverse problems. This methodology utilizes the heterogeneities in petrophysical properties, as predicted by the inverse model, as an intermediary variable. The incorporation of an AI-assisted data assimilation protocol substantially reduces this uncertainty by refining the outcomes from the inverse model. The findings indicate that deep neural networks models adapted from an auto encode architecture derived from the U-net are effective for image-to-image regression predictions using static heterogeneity property distributions as inputs. This approach leads to the training of an expert system capable of forecasting the varied spatiotemporal dynamics of carbon species within saline aquifers. After injection ceases and the pressure transient spreads, the gas plume evolution becomes less sensitive to changes in the injection well bottomhole pressure. It highlights the importance of strategically placing monitoring wells to effectively oversee long-term gas migration and plume characteristics. The integration of expert systems with ensemble Kalman filters successfully delineates the spatial and temporal evolution of CO2 plumes by analyzing pressure data from injection and monitoring wells. This method, in contrast to traditional CO2 plume inversion techniques, demands fewer and more affordable data inputs. As a result, it offers cost-effective and precise CO2 plume characterization considering different forms of presences.
Novel Approach for Increasing Stimulation Performance in Gas Reservoirs With Eco-Friendly Solutions
Authors: Ahmad Zeeshan , Albadi Mohamad Mubarik , Ahmad Mubashir , Elabrashy Manar Maher Mohamed , Pangestu Yohannes Fisher , Ahmed Sami Ullah Bashir , Shinde Kaustubh Dhananjay , Samad Bachar , Hadziavdic Ina 
Published: November 2024

Abstract
Comprehensive study was conducted to optimize acid stimulation techniques, addressing problems like restoring flow in blocked wellbores, effectively removing loose formation debris, managing flaring limits during flow back, and enhancing low productivity in reservoirs affected by high skin factor and low pressure. Innovative stimulation methods utilizing dynamic pressure pulsing have been introduced to advance fluid injections into formations and reduce carbon emissions simultaneously. The application of a dynamic fluid pulse method during an acid stimulation operation on well#X1 significantly enhanced the diffusion of acid by adjusting the quantity utilized. This technology operates under various conditions, administering acid via coiled tubing and optimizing for different pressure scenarios and dosing rates. These initiatives aimed to improve access and stimulation for open-hole treatments were subjected to rigorous assessments. Using cutting-edge Dynamic fluid pulse technology achieved complete accessibility and featured adaptable nozzles creating a differential pressure between 700 and 800 psi in the open-hole area. Simulations revealed that dynamic fluid pulses facilitated better wormhole development, allowing meticulous control over the volume of acid deployed, thereby elevating matrix acidizing procedures’ efficiency and gas production gains. The approach activated the well, increasing flow rates to 25 MMSCF/D as confirmed by well testing. A gelled fluid system was implemented, which boosted lift efficiency throughout the cleanup phase and effectively optimized both flaring activities and greenhouse gas emissions, accounting for an estimated reduction of 280 mtCO2e.
For the depleted gas well#X2, flowback the liquid acid system imposed significant challenges, necessitating thorough clean-up operations to off-load the liquids from the lateral reservoir sectors. To meet these stimulation objectives while minimizing flaring, a strategy involving nitrified acid was implemented. Additionally, well#X3 which was a gas-producing well went through a stimulation design with solid acid sticks, leading to an increase in production without the necessity for subsequent flow back procedures as per simulation studies.
In summary, technologies such as Dynamic fluid pulse, Nitrified foam Acid systems, and Solid Acid Sticks offer strategic solutions to overcome the operational difficulties encountered in extended horizontal wells containing gas, considering the well architecture and the need to mitigate frictional forces.
Surface Well Testing Automation – Case Study and Lessons Learned
Authors: Kaipov Y. , Pham T. , Al-Hazmi W. , Denegri R. , Qoqandi A. , Alawagi A. 
Published: November 2024

Abstract
The paper discusses a case study that focuses on enhancing safety standards for surface well testing. To achieve this, the study involves upgrading the surface well testing equipment and revising the service delivery procedure to minimize safety risks while still meeting the well test objectives.
Surface well testing is a well-established process that involves the fluid processing and disposal to acquire data while flowing the well. However, when dealing with sour fluid, the associated risks become much more severe and thus creating much higher HSE risks. It was necessary to re-evaluate existing safety protocols and upgrade the equipment by automating some operations and changing the data acquisition methodology. These measures reduces the likelihood and severity of any potential hazards to an acceptable level, allowing for the safe acquisition of necessary data while minimizing risk to personnel and the environment.
A new concept for surface well testing has been introduced, which has significantly improved the efficiency of unlocking reserves from sour reservoirs. This was achieved through a combination of material and seal upgrades, as well as automation. The automated choke manifold played a key role by allowing remote control of flow instead of requiring an operator to manually change choke beans. This eliminated personnel exposure to high-pressure hazards. The traditional hydraulically operated emergency shutdown system was replaced with an electrical one, allowing for 13 times faster well shutdown. It can also automatically shut-in the well thanks to integration with an H2S gas detection system. The inline gas analyzer has enabled real-time monitoring of H2S, reducing the need for frequent manual sampling and minimizing personnel exposure and gas release into the environment. Overall, these upgrades have significantly improved safety and efficiency in surface well testing, enabling the safe extraction of valuable resources from challenging environments.
For the first time, an automated surface well testing package was introduced. The paper will discuss about lessons learnt and recommendation on new developments for further improvement of the system. This is first the milestone towards fully autonomous surface well testing.
Generative AI and Large Language Model Assisted Causal Discovery and Inference for Driving Process Improvements
Authors: Rai P. , Jain A. , Anand A. 
Published: November 2024

Data-driven process management coupled with machine learning have been successful in driving commercial value to oil and gas operators by offering insights into process disruptions and their root causes. One frequently used approach is to analyze causes of process disruptions exclusively from historical data. In general, specific insights in the form of high correlation between certain process performance indicators and a well-defined measure of production inefficiency is often confounded as responsible causal factors. While this may yield some insights, the complexity of processes, measured in terms of number of entities involved and their interrelationships, requires a more nuanced approach that must include the context of the specific process. Thus, data analysis must be augmented with significant inputs from experts. Causal Inference provides a conceptual framework and tools for doing such analysis.
In causal analysis, we embed this specific knowledge of subject matter experts using causal graphs consisting of process features (nodes) and their dependency (directed edges). For complex processes however, constructing causal graphs could be non-trivial due to ambiguity over which nodes to include and the plausible direction of their relationships. With the advent of foundational Large Language Models (LLM), there is an opportunity to mitigate this problem by utilizing the enormous information it encodes. Tools and technologies now exist to customize the response of LLM using retrieval of information from a corpus of specific high-quality knowledge in the form of related literature and data. It can therefore be used to assist the domain expert in building and finetuning the causal graph, and in simpler cases, can completely automate this step.
In this work, we propose a two-step approach to combine the power of LLMs and Causal Analysis for analyzing inefficiencies in production processes. In the first step, we implement a Retrieval Augmented Generation (RAG) enhanced LLM prompting on a curated dataset designed to answer specific questions on relationship between process performance indicators. The outcome of this step is a directed acyclic graph encoding dependency of process performance indicators. Domain experts can validate or potentially refine the LLM-generated causal graph based on their domain knowledge for eliminating spurious hallucinations. In the second step, we use an appropriate causal inference method on the refined causal diagram and historical production data to estimate the causal effect of process variable contributing to disruptions or inefficiencies. Thus, by combining human expertise with machine learning, this framework offers a comprehensive approach for optimizing production processes.
Proppant Transport in Rough Fractures of Unconventional Reservoir– A Case Study from China
Authors: Li Zhou , Zhou Wei , Kong Xiangshuai , An Weiqing , Wang Xiaoguang , Xu Hanbing , Qian Chuanchuan , Hu Yongjing , Li Shuai , Lian Liming 
Published: November 2024

Abstract
A generative method for fractures with rough surfaces was established based on fractal interpolation theory, encompassing interactions among particles, particle-wall, and particle-fluid. Consequently, a coupled computational fluid dynamics (CFD) and discrete element method (DEM) model was developed to simulate the two-phase flow of proppant and fracturing fluid within these fractures. Validated by experimental data, the model effectively matches the transport and accumulation processes of the proppant in rough fractures. Comparative numerical simulations across various scenarios revealed that, when compared to smooth planar fractures, the transport and deposition of proppants in rough fractures are significantly affected by the wall roughness. The greater the surface roughness, the faster the proppant particles settle near the fracture inlet and the shorter their horizontal transport distance, resulting in a tendency to accumulate near the fracture entry and rapidly form sand plugs within the crack. The roughness of fracture walls can control the fluid’s flow path to some extent, altering how the proppant fills the fractures. On one hand, the elevated roughness lifts the trajectory of proppant movement, causing more proppant to exit the fracture and thereby reducing coverage; on the other hand, the fracturing fluid is more likely to redirect its flow near the contact points of the rough surface protrusions, potentially expanding the coverage area of the proppant.