ABSTRACT Deep-learning algorithms have become powerful tools for denoising, leveraging their automatic learning and feature extraction abilities to detect and remove noise from images while preserving fine details and textures. These methods reduce reliance on manual adjustments and parameter tuning, improving efficiency and consistency. In geophysics, many researchers have applied various deep-learning algorithms for seismic denoising; however, most applications have focused on prestack shot/receiver gathers or poststack seismic data. The use of deep learning to denoise offset- or angle-domain prestack seismic gathers remains rare. Prestack seismic gathers are essential in exploration geophysics, playing key roles in velocity model building, amplitude-variation-with-angle analysis, and imaging complex geologic structures. Given the strong performance of deep-learning algorithms in denoising and the importance of seismic gathers, this study applies deep learning to denoise angle-domain prestack seismic gathers (angle gathers). Specifically, we use a conditional generative adversarial network known as Pix2Pix to denoise PP-wave angle gathers. Tests on synthetic and field data demonstrate its feasibility, with results showing that the trained model effectively removes random and correlated noise in angle gathers while filling in missing signals. Its application to synthetic time-lapse data further demonstrates strong performance, which is particularly significant for carbon capture and storage (CCS) monitoring, where high-fidelity time-lapse seismic data are crucial for tracking subsurface migration and storage integrity. The study examines the network’s ability to capture physical relationships inherent in angle gathers. Specifically, the Zoeppritz equation is incorporated into the training data synthesis, ensuring that the model learns the angular dependence of reflection coefficients. This integration of domain knowledge enhances the denoising process, making it a valuable tool for advancing CCS monitoring and evaluation efforts.

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 223867, “Hybrid Optimization Framework for Well Placement Using Gradient‑Free Algorithm and Physics‑Informed Artificial Intelligence,” by Kheireddine Redouane, SPE, and Ashkan Jahanbani Ghahfarokhi, SPE, Norwegian University of Science and Technology. The paper has not been peer-reviewed. _ Accurate well placement plays an essential role in increasing field recovery and storage while reducing operational costs. This task is complex, requiring robust solutions that can handle optimization problems efficiently. Despite numerous existing solutions, a need remains for a fast, highly accurate, computer-aided optimization tool. In this paper, an auto-adaptive workflow is described that leverages a complex interplay between machine learning (ML), physics of fluid flow, and a gradient-free algorithm to enhance solving well-placement problems. Introduction The study’s main objective is to develop a novel surrogate-based hybrid intelligent system to handle real well-placement problems in realistic field conditions while overcoming the drawbacks of conventional approaches. The authors’ contribution is the development of a self-adaptive optimization routine (SAOR), a physics-informed artificial intelligence (AI) framework designed to enhance the accuracy and efficiency of the optimization process. This is achieved by directing computational resources to regions of interest identified through surrogate interaction with physics-based simulations by means of the proposed adaptive approach. This approach integrates technically constrained design of experiments (DoE) and a genetic algorithm (GA) optimizer to iteratively enrich the database in the regions where optimization is most critical, ensuring that the surrogate models are continuously refined, leading to faster convergence and higher accuracy. The SAOR in this paper is configured with different types of DoE, two adaptive sampling methods, and two ML models—Gaussian process (GP) and an adaptive neuro-fuzzy inference system (ANFIS). This enables the evaluation of multiple physics-informed AI optimization paradigms that allow the SAOR to explore the “no free lunch” theorem in the context of well placement, demonstrating that the effectiveness of an optimization framework depends on its ability to adapt to the specific characteristics of the problem. To the best of the authors’ knowledge, this is the first study presenting a comparison of direct GA with self-adaptive GP and ANFIS for a well-placement optimization problem. The SAOR demonstrated its capability to design efficient hybrid optimizers tailored to the specific challenges of well placement, achieving high accuracy within reasonable central-processing-unit (CPU) time. Moreover, SAOR ensures that solutions not only are optimal but also adhere to the underlying physics and technical constraints of the reservoir system. Methodology To aid in solving complex well-placement challenges for improved reservoir management, the SAOR has been designed to simultaneously consider the following factors: - Reduction of optimization time by minimizing the number of required simulations, particularly by using DoE and adaptive enrichment techniques - Approximation of the expensive numerical model with a surrogate model that offers faster response times - Dynamic exploration of irregular search spaces, avoiding local optimal results that might lead to suboptimal decisions, and refining the global optimum once it is found

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 226368, “Leveraging Data Analytics To Design an Overarching Flaring-Reduction Strategy,” by Farras Sailendra and Bugi Setiadi, SPE, BP. The paper has not been peer-reviewed. _ This paper details a data-driven methodology applied at the Tangguh liquefied natural gas (LNG) facility in Indonesia to enhance flare-emission visibility and enable targeted reduction strategies. By integrating real-time process data with engineering models, flare contributions were quantified at the valve level. A phased implementation roadmap was developed to reduce flaring through three strategic focus areas: optimizing startup sequences, rationalizing purge-gas consumption, and adjusting operating parameters during LNG loading and dryer-bed changeover. Introduction Tangguh LNG, in Bintuni Bay, Indonesia, is a major facility with a total production capacity of 11.4 million tons per year across three processing trains. Traditionally, flare monitoring at Tangguh relied on flowmeters installed at the knockout drums of each major flare system (dry, wet, and tankage headers). While this provided a high-level summary of flare volumes, it offered little insight into the specific equipment or events contributing to flare load. As a result, opportunities for flare reduction remained largely reactive and untargeted. A 2022 internal audit revealed that over 60% of flare volume originated from preventable sources. Because of the lack of granular, real-time data, however, plant teams were often forced to conduct manual retrospective investigations. Methodology and Analytical Framework This section of the complete paper outlines the structured analytical approach used to transform raw process data into actionable flare-reduction strategies. The methodology follows the data-information-knowledge-insight-wisdom framework. This hierarchy ensures a systematic progression from unstructured information to strategic action. From Data to Information: Tagging and Labeling Flare Contributors. Initially, these valves existed only as tag numbers, unstructured and without clear contextual meaning. The first transformation occurred when each valve was systematically labeled based on its function and flaring behavior. This was achieved through process knowledge, operator input, and pattern analysis from historian trends. The result was the creation of an information layer. Each valve now carried a functional identity. The labels were categorized into the following operational flare types: - Safety-related flaring - Routine flaring - Nonroutine flaring - Startup and shutdown flaring To support this transformation, the flare network was visualized as a node-link diagram in which each was represented as a node and color-coded based on its assigned flare category. From Information to Knowledge: Grouping Contributors into Functional Categories. Once each valve in the flare network had been labeled based on its flaring behavior, the next step was to organize this information into structured, functional categories. This involved grouping the identified valves into “rooms” that represented distinct flare contributors correlating with the four previously described operational flare types. By visualizing each group of flare contributors as a contained cluster, determination of which valves collectively contributed to a specific type of flaring event was possible.

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 226792, “Reshaping the Oil and Gas Industry: The Rise of the Digital Petroleum Engineer,” by Babak Moradi, SPE, Lasse Hermansson, and Tor Ellingsen, THREE60 Energy, et al. The paper has not been peer-reviewed. _ This paper explores the evolving role of the digital petroleum engineer (DPE), examines the core technologies they use, assesses the challenges they face, and projects future industry trends. Emphasis is placed on the synergy between traditional domain knowledge and digitalization as a key driver of success. The study focuses on the expanding role of the DPE, including the application of artificial intelligence (AI)-driven analytics, cloud-based platforms, and advanced automation. Advantages of Embracing the Digital Shift: Case Study in Accelerating Reservoir Simulation The complete paper details a list of benefits accrued in embracing the shift toward digitization, including talent attraction, sustainability, and heightened operational efficiency (Fig. 1). As an example, the complete paper provides the following case study in the discipline of reservoir simulation. In modern petroleum engineering, the ability to simulate reservoir behavior quickly and accurately has become critical for efficient field development and real-time decision-making. Thanks to advances in computational power, parallel processing, and AI, DPEs can perform studies that would have been computationally infeasible just a decade ago. The evolution of computational capability has allowed engineers to build and simulate models with unprecedented resolution. In 1977, it was estimated that black-oil reservoir models could reach 100 million grid cells by 2017. This threshold was far surpassed, however, when engineers successfully ran a trillion-cell simulation in 2016. The transition from traditional central-processing-unit (CPU)-based calculations to parallel CPU and graphics-processing-unit processing also has revolutionized simulation workflows. CPU parallelization distributes tasks across multiple cores and processors, dramatically reducing run times. In parallel, data-driven modeling techniques have emerged as complementary tools to physics-based simulators. These methods use machine learning (ML) and statistical algorithms to model complex reservoir behavior based on historical field data. Some techniques allow engineers to evaluate multiple development scenarios quickly, even in mature or data-sparse fields. Combining physics-based models with data-driven methods offers the best of both approaches. The Role of the DPE Oil and gas operations now generate enormous volumes of data. Making timely and actionable use of this data requires engineers with skills in advanced analytics, ML, and data interpretation. Real-time decision-making also is becoming standard in field operations. A DPE also must integrate geoscience, reservoir engineering, economics, and operational strategy with digital tools and platforms. This convergence of disciplines requires a new kind of versatility in technical roles. Remote operations are another driving force. This capability proved essential during the pandemic. Additionally, the industry faces a generational shift as a large portion of the experienced workforce approaches retirement. Preserving this institutional knowledge by embedding it into AI systems and training a new generation of digitally fluent engineers is vital for long-term resilience and continuity.

_ Are we in an AI bubble? The question may seem academic to petroleum engineers who are already capitalizing on the momentum of digitalization across the industry, yet any engineer, regardless of their career stage, could be forgiven for feeling overwhelmed by the sheer scope of specialized skills now demanded in this rapidly evolving digital landscape. Paper SPE 226792 articulates the core skills and technologies needed on the journey to becoming a digital petroleum engineer. Of course, a foundation in classical petroleum engineering skills is indispensable. What really sets this synoptic paper apart from the ever-growing din of digital noise is its approach to recapping petroleum engineering history, the current exploitation of large language models and, most importantly, looking ahead to anticipated future challenges such as solution scalability and opportunities such as quantum computing. Not every problem demands a machine-learning or artificial-intelligence solution. Fit-for-purpose analytics solutions, which can be easily and sequentially followed on a process flowchart, remain relevant and are relatively cheap to develop and deploy. Paper SPE 226368 provides an elegant template for those seeking to understand how to correctly frame problems to be addressed through large-scale data analytics. Well placement for optimizing reservoir performance is a tough problem. Paper SPE 223867 reports an 80% reduction in required simulations within the design-of-experiments stage. This approach delivers a notable improvement over conventional global-search methods or proxy models that apply constraint penalties after sampling, often wasting computational effort on invalid scenarios. Summarized Papers in This January 2026 Issue SPE 2267792 - Rise of the Digital Petroleum Engineer Reshapes the Oil and Gas Industry by Babak Moradi, SPE, Lasse Hermansson, and Tor Ellingsen, THREE60 Energy, et al. SPE 226368 - Data Analytics Leveraged in Designing Flaring-Reduction Strategy by Farras Sailendra and Bugi Setiadi, SPE, BP SPE 223867 - Well-Placement Optimization Workflow Blends Gradient-Free Algorithm, Physics-Informed AI by Kheireddine Redouane, SPE, and Ashkan Jahanbani Ghahfarokhi, SPE, Norwegian University of Science and Technology Recommended Additional Reading at OnePetro: www.onepetro.org. SPE 228097 - Building EnergyLLM: A Domain-Specific Large Language Model Trained on SPE Content by J. Eckroth, i2k Connect, et al. SPE 227088 - Unsupervised Machine-Learning Workflow for Identifying Microresistivity Borehole Image Features by G. Keretchashvili, SLB, et al. SPE 227244 - Integrating Machine-Learning Clustering Into PVT-Based Reservoir-Fluid-Characterization Workflows by S. Bestman, Saudi Aramco, et al.

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 222116, “BOP Pressure-Chart Analysis Using Computer Vision Technology,” by Ali Mohamed S. Al Hosani, SPE, and Geetha Selvamoorthy, ADNOC, and Koushik Kumar, Trust Technical Services. The paper has not been peer-reviewed. _ Blowout-preventer (BOP) systems require inspection at least every 14 days, with a pressure test conducted to ensure their condition. This test currently involves manually decoding pressure-chart data from a mechanical pressure-chart recorder, a time-consuming process. The solution described in this paper automates this process by capturing the image of the pressure chart and decoding it using artificial-intelligence (AI) image analysis. Computer Vision for Pressure Readings In BOP pressure-chart analysis, computer vision transforms the traditionally manual and error-prone task of decoding pressure-chart data into an automated and precise operation. The system integrates various computer vision techniques to ensure accurate and efficient data extraction from pressure charts, streamlining the entire process. BOP Pressure Chart. The BOP pressure chart is a critical tool used in drilling operations to monitor pressure changes over time. The chart recorder, an essential component of this tool, features two or three pens driven by sensors that plot traces on a rotating paper disk (Fig. 1). The disk, typically predrawn with a green template, includes the following components: - Radial arcs representing time intervals - Concentric circles indicating quantity levels - Names of quantities (axes), which are labels for the data being measured - Quantity values, which are markings for ranges on concentric lines - Time marks denoting hours and weekday names Steps Required To Read the Pressure Chart Using Computer Vision Cropping Out the Pressure Chart From the User-Uploaded Image. The process of cropping the pressure chart involves isolating the relevant contour from the image and then cropping the image based on this contour. This process involves, in order, detecting contours, selecting the largest contour, finding the largest circle inside the contour, creating and applying the mask, and cropping the image. By following these steps, the process efficiently isolates and extracts the pressure chart from the user-uploaded image, setting the stage for subsequent analysis and processing. Changing Orientation of the Image To Align the Starting Point. Aligning the pressure chart’s orientation is crucial for accurate data extraction. This step involves correcting the image’s orientation by aligning the chart’s starting point based on the detected inner circle and using the histogram-projection method (HPM) to detect and align text within the circle. This process involves the following series of steps: 1. Detecting the inner circle 2. Extracting text from the circle 3. Applying HPM, used to align the image based on the detected text 4. Horizontal projection 5. Vertical projection 6. Aligning the starting point

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 222449, “Development of a Risk-Based Modeling Framework for Integrity Assessment of Legacy Wells in CO2-Storage Applications,” by Saeed Ghanbari, Morteza H. Sefat, SPE, and David Davies, Heriot-Watt University, et al. The paper has not been peer-reviewed. _ Demonstrating the integrity of plugged and abandoned legacy wells during CO2 storage projects is a crucial requirement for regulators, stakeholders, and operators. The corrosive nature of CO2 may affect the integrity of such wells, jeopardizing the long-term containment of the CO2. This study illustrates the new capabilities, tailored for CO2 storage applications, of a modeling framework that provides a quantitative, risk-based assessment of the long-term integrity of legacy plugged and abandoned wells. Three new modeling modules are added to this integrated framework to account for key concerns. Introduction The plugging and abandonment (P&A) framework translates all well-flow paths into a numerical, spatially discretized, grid-based, digital representation. Multiple grid cell types represent the various well components (e.g., intact and impaired cement, casing, tubing, reservoir formation, and fluid-filled annular space). Appropriate fluid-flow properties are allocated to each cell. The model solves the cell-to-cell flow from source to sink for the entire well P&A system over long periods (typically 3,000 years). Short- and mid-term transient, unsteady flows as well as long-term, steady-state flow rates are then calculated using a finite-difference flow simulator as the back engine. The modeling framework has been field-tested successfully in risk-based P&A designs and integrity assessments. These studies assumed no major chemical or physical interactions between the fluids and P&A components. This is a reasonable assumption for wells in a depleted hydrocarbon field with noncorrosive reservoir fluids, where major geomechanical interactions from pressure and temperature changes in the well are not expected. Extending the framework for carbon capture, use, and storage (CCUS) applications required new workflows to capture the chemical and physical reactions resulting from recharging the reservoir system with CO2. These reactions include the following: - Geochemical interaction of CO2 with water, cement, and near-wellbore formation - Geomechanical effects caused by pressure and temperature changes during the CCUS project - Casing corrosion in the presence of CO2 CO2/Cement Geochemistry Modeling Within the P&A Framework Portlandite [Ca(OH) 2] and calcium silicate hydrate (CSH) are the most important minerals in oilwell cement. Carbonic acid is formed when CO2 dissolves in water. It is a weak acid whose reaction with cement is well-documented in the literature. Cement defects typically have a much greater effective permeability than the cement matrix. Interaction of carbonated water with cement along a cement defect can have significant effect on flow properties, depending on the flow conditions and defect properties. Initially, the carbonated water dissolves a portion of the cement minerals from the face of the defect. The dissolved minerals are transported and, depending on flow and defect-size characteristics, may subsequently precipitate within the defect. Under favorable conditions, the defect size [hydraulic diameter (HD)] may decrease continuously until it is fully blocked. This behavior is called self-sealing. Alternatively, continuous dissolution may dominate if the residence time is insufficient for precipitation to occur. This behavior, called self-degradation, is characterized by the cement defect’s increased hydraulic diameter. Previously published work confirms the authors’ decision not to consider further the effect of carbonated water on the cement matrix because this interaction is relatively small. The vast majority of any leakage occurs through cement defects.

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 225550, “Wellbore-Integrity Challenges in the Era of Energy Transition,” by Taofik H. Nassan, SPE, and Mohammed Amro, SPE, TU Bergakademie Freiberg. The paper has not been peer-reviewed. _ The energy transition introduces novel challenges to well integrity that remain largely unexplored and unaddressed. In this paper, wellbore integrity from a traditional standpoint is revisited and examined against emerging requirements driven by energy-transition applications. The work consolidates common wellbore-integrity issues across various energy-transition applications, offering a unified perspective based on a literature review and the authors’ extensive laboratory experience. It highlights critical challenges and proposes future research directions to address the evolving needs of subsurface operations in the energy-transition era. Energy Transition and New Wellbore Fluids New challenges exist for wellbore integrity because of the unique properties of newly introduced fluids and developing operational aspects. Hydrogen (H2). H2 is the smallest molecule in nature and is highly penetrative and reactive. Compared with CO2 and methane, it can more easily diffuse through most geological formations. Only rock salt and crystalline rock formations can effectively contain hydrogen. At standard conditions, hydrogen has a density eight times lower than that of methane, allowing it to rise quickly in the atmosphere. In terms of reactivity, H2 reacts with a wide range of materials, including water, oxygen, and metals. This high reactivity increases the vulnerability of infrastructure exposed to hydrogen, raising the risks of corrosion, leakage, and other integrity issues. Moreover, hydrogen is more flammable than methane, with the potential for fires that are more difficult to extinguish. Another problem associated with H2 is metal embrittlement. Molecular H2 can dissociate into hydrogen ions, which then diffuse into the alloy, accumulating at defects or grain boundaries. CO2. CO2 has been commonly used in enhanced oil recovery (EOR) for decades. However, most CO2 currently injected for EOR purposes is sourced from natural deposits. In contrast, CO2 from anthropogenic sources may contain different impurities. CO2 also reacts with elastomers used in well completions. Underground CO2 storage is a relatively new technology and is expected to serve as a key bridge toward a net-zero emissions future. Water. Water is the primary fluid used in geothermal energy extraction. However, emerging trends in geothermal technologies are exploring greater depths and novel concepts, which may alter water composition and worsen traditional issues such as mineral precipitation and corrosion. Wellbore Integrity in Emerging Geoenergy and Geostorage Systems Geological Carbon Storage (GCS). In the presence of water, CO2 is reactive and can form carbonic acid, which can degrade both cement and casing materials. Additionally, CO2 is expected to be stored permanently in various geological formations. Consequently, the standards for casing and cement materials currently used in the petroleum industry may need to be revised. Extensive testing is required to assess their suitability for long-term GCS applications.

_ Two critical facets of the discipline of well control and wellbore integrity—decarbonization and groundbreaking automation of essential processes—are highlighted in the three primary paper selections presented at SPE and SPE-affiliated conferences during the past year. These authors advance studies dedicated to heightening safety and embracing the energy transition, two goals that must be interwoven as the industry enters the second quarter of the century. In paper SPE 221116, the authors describe a methodology for analyzing blowout-preventer (BOP) pressure charts that leverages computer vision to decode image captures of data in conjunction with a BOP-inspection application. Their findings indicate that an average of 90 minutes can be saved per test while the accuracy of readings is simultaneously improved. Additionally, the operator deploying the technology has reported a 300% return on investment. Plugged and abandoned (P&A) wells are already playing a crucial role in CO2-storage projects, but they pose special challenges because of the potential for corrosion and decreased casing thickness once they are exposed to CO2. The authors of paper SPE 222449 present a modeling framework that provides a quantitative, risk-based assessment of the long-term integrity of P&A wells. The framework includes three newly developed modules (centered on geochemical, geomechanical, and corrosion concerns) customized for CO2 storage. The authors of paper SPE 225550 adopt a broad scope in addressing the discipline’s ability to accommodate and enhance the energy transition. The paper summarizes their findings from laboratory experiments conducted in recent years on wellbore integrity for geothermal projects, geological CO2 storage, and hydrogen storage. A wide range of concerns, approaches, and options are reviewed, from existing salt-cavern-storage projects to methods of drilling new wells that better accord with net-zero goals. Summarized Papers in This January 2026 Issue SPE 225550 - Study Explores Wellbore-Integrity Challenges in the Era of Energy Transition by Taofik H. Nassan, SPE, and Mohammed Amro, SPE, TU Bergakademie Freiberg. SPE 222449 - Modeling Framework Developed To Assess Integrity of Legacy Wells for CO2 Storage by Saeed Ghanbari, Morteza H. Sefat, SPE, and David Davies, Heriot-Watt University, et al. SPE 222116 - BOP Pressure-Chart Analysis Approach Uses Computer Vision Technology by Ali Mohamed S. Al Hosani, SPE, and Geetha Selvamoorthy, ADNOC, and Koushik Kumar, Trust Technical Services. Recommended Additional Reading at OnePetro: www.onepetro.org. SPE 224896 - Innovative Recovery Strategies for High-Risk Well-Integrity Failures by M. Al Mahrooqi, Petroleum Development Oman, et al. SPE 225047 - Cutting-Edge Wellhead Support To Secure Subsiding and Unstable Onshore Wells: Casing and Structural Rehabilitation Methodology by A.M. Darwish, ADNOC, et al. SPE 225724 - Success Story: A Cost-Effective and Systematic Approach To Restore Integrity in Wells Having Sustained Annulus Pressure Using Rigless Intervention by A.Y. Sulaiman, ADNOC, et al. OTC 35769 - Fingerprinting Burst and Collapse Events in Well Casings Using Electromagnetic Logs by Huiwen Sheng, Halliburton, et al.

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 224936, “Block 61 Condensate-Decline-Management Strategy,” by Rovshan Mollayev, Mohammed Al Harrasi, and Ruqia Al Shidhani, BP, et al. The paper has not been peer-reviewed. _ Block 61 is a gas field in southwest Oman. The Khazzan Phase 1 project was initiated in 2017; the Khazzan Phase 2 project began in 2020. The Phase 2 project initially targeted high condensate/gas ratio (CGR) wells. At the start of the project, large volumes of condensate were introduced, prompting operation of wells at higher production rates. However, these higher production rates were observed to cause high condensate decline. A structured evaluation program was launched to identify the optimal gas production rate per well, aiming to maximize cumulative condensate recovery while sustaining reservoir pressure. Introduction Because of the field’s significant gas reserves and strategic importance in meeting Oman’s domestic gas demand and export commitments, a phased development approach was adopted. Phase 1 focused on the northern part of the field, characterized by a low CGR and predominantly lower-productivity-index wells. In contrast, Phase 2 was designed to develop the southern part of the reservoir, which features a higher CGR and greater well productivity. The primary objective of Phase 2 was to increase the gas-export capacity of the block from 1 to 1.5 Bscf/D. With the commencement of Phase 2 production, a significant increase in condensate production was observed because of the higher CGR in the southern wells. This led to an operational inclination to produce wells at higher gas rates (20–30 MMscf/D) to maximize short-term condensate output. However, field observations indicated that these higher production rates resulted in an accelerated decline in CGR, raising concerns regarding long-term condensate recovery and reservoir management. While condensate is a byproduct in a predominantly gas-driven development, excessive condensate loss posed several challenges, including a reduction in estimated ultimate recovery (EUR) per well, a negative effect on cumulative condensate production per well, and potential acceleration of condensate banking. These concerns underscored the need for a structured engineering assessment to optimize well-operating conditions. The objective was to develop a robust well-operating workflow and strategy that could be applied systematically in field operations to balance short-term production targets with long-term reservoir performance. Methodology and Evaluation The engineering team initiated a comprehensive well-operating strategy assessment to determine optimal gas-production rates, ensuring a balance between short-term production gains and sustainable reservoir management. Given the complexities associated with tight gas reservoirs and condensate behavior, a multifaceted approach was adopted, incorporating reservoir simulations, well-testing operations, and historical production-data analysis. This methodology provided a robust framework for developing an effective condensate-decline-management strategy while maximizing overall hydrocarbon recovery.