bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unlocking Commingled Production Using Geochemical Production Allocation
Authors: Mejia S. , Markano N. , Marquez L. , Bonfanti B. , Cortez O. , Paladines A. , Fernandez E. , Piñeiros S. , Alba E. , Mendoza M. , Molina E. , Reyes J. 
Published: February 2024

Abstract
In Ecuador commingled production is being used as a strategy to fulfill the government's objective of increasing production. A key challenge in commingled production is how efficiently quantify the contribution of individual reservoirs to support production allocation to the zones meeting the regulation requirement. Geochemical fingerprinting is a fast, viable, and cost-efficient method for production back-allocation. The application has been successfully applied and technically validated for the first time in Ecuador, opening a vast business opportunity.
The feasibility, testing and implementation in a major field is covered in this paper. The geochemical allocation method compares the oil fluid composition of representative samples acquired from each reservoir (end members). The method seeks sufficient compositional information at zone level to calculate the zone contributions from the commingled well production composition. This analysis requires high-resolution gas chromatography and geochemistry interpretation, supported by a specialized software.
A production allocation feasibility study was conducted initially to define if end members collected from seven reservoirs in 17 fields could be differentiated in mixtures based on a chemical distance analysis. The chemical distance analysis shows that oils from some reservoirs are very distinctive, while others are very similar, indistinguishable from each other. One advantage is that the sensitivity of the geochemical analysis allows the differentiation of even the very similar oils in mixtures, enabling the calculation of end-member contributions.
Production was back-allocated in the commingled wells and was compared with the results obtained by traditional production logging tool (PLT) measurements in a cross-validated case study. The technology was successfully validated against traditional approaches such as PLT. The outstanding results show 2% difference between the technologies, which resulted in the approval by the client and raised the interest of the Ecuadorian regulation entity to use this technology as a complementary tool of the traditional technologies.
The cost of the proposed alternative represents about 5% of the frequently used methods price. Therefore, all the existing commingled wells in the oilfield are frequently monitored using geochemical production allocation without impacting the oil production. Commingled production has maximized the field value by increasing production revenue while optimizing costs.
Virtual Flow Metering as an Emerging Flow Measurement Technology
Authors: Gryzlov A. , Arsalan M. 
Published: February 2024

Abstract
The accurate measurement of multiphase rates of produced oil, water and gas has always been a significant challenge to the oil and gas industry. Over the last few decades the traditional approach driven by the use of test separators has gradually been replaced by multiphase flow meters, which offer continuous monitoring of multiphase rates in real-time, without separating phases. Currently, there are multiple technologies available on the market, but multiphase flow meters generally are incapable to handle all ranges of fluid properties and operating conditions with satisfactory accuracy in addition to being expensive to purchase and maintain. The last point has motivated the oil and gas industry to seek other alternative metering technologies. In particular, the emergence of cheap sensors and their widespread installation in production systems has led to an exponential increase in data availability obtained from a well. These data combined with a dedicated mathematical algorithm can facilitate the extraction of actionable information relevant to multiphase flow rates. This methodology, which is also known as a Virtual Flow Metering (VFM), is a promising tool which is capable to mitigate the disadvantages of conventional multiphase flow metering and provides the functionality which was not previously available, such as an estimation of downhole flow rates. The objective of this review paper is to outline the current challenges within multiphase flow measurements and to introduce the most promising digital techniques, which have the potential to become a digital successor of a multiphase flow meters.
Assessing the Performance of Thermally Active Polymer as an In-Depth Conformance Technology in a Mature Waterflooded Reservoir: A Recent Field-Scale Case Study Utilizing Interwell Tracers in Argentina's Largest Oil Producing Field
Authors: Kazempour Mahdi , Santamaria Rodrigo , Lizarazo Leonel , Gomez Javier , Iuliano Alberto , Martinez Christian , Fernandez Diego Damian 
Published: February 2024

Abstract
Waterflooding has been a widely practiced method globally for numerous years, and it has been proven in the field to effectively enhance oil recovery in oil-bearing reservoirs, surpassing primary production methods. Despite its considerable potential for boosting oil recovery, waterflooding faces several challenges that can impede its performance, including reservoir heterogeneities, reservoir structural complexities, and unfavorable mobility ratios. Over the past five years, an extensive multidisciplinary effort has been undertaken to address these waterflood challenges and enhance waterflood sweep efficiency in Argentina. As a result, a cutting-edge conformance technology called Thermally Active Polymer (TAP), also known as BrightWater®, has been successfully implemented in Cerro Dragon, one of Argentina's largest oil-producing fields, yielding encouraging outcomes.
To assess the efficacy of TAP as a reservoir-triggered conformance agent, two sets of interwell tracer tests (IWTT) were conducted: one before TAP injection and another after TAP injection when an oil response was detected. These IWTTs represented the first instance of flow pattern change identification within the targeted segment of the reservoir. Additionally, they enabled the quantification of transit time changes subsequent to the in-depth conformance treatment. The study also revealed the establishment of new communication paths while the previously existing paths became less active following TAP placement.
The results obtained from the IWTT tests aligned remarkably well with the expectations derived from conceptual numerical modeling. Furthermore, a distinct correlation was observed between the changes in sweeping profiles observed through tracers and the oil and water production responses on the production side. This confirmed that the thermally active polymer reduced the flow capacities of thief zones, thereby allowing previously unswept segments of the reservoir to come into greater contact with the injected water.
The findings of this study hold significant implications for the effective execution and design of in-depth conformance treatment programs. Moreover, the study equips end users with quantitative tools for more detailed post-monitoring performance evaluation. TAP has emerged as one of the most widely practiced in-depth conformance technologies for both onshore and offshore applications due to its short execution times, small treatment slug sizes, and ease of injection under various reservoir conditions.
Application of a Bayesian Ensemble Algorithm for Automated Production Diagnostic of Gas Wells with Plunger-Lift
Authors: Pan Y. , Bi R. , Yang S. , Lyu Z. , Ju X. 
Published: February 2024

Abstract
Remotely monitoring plunger lift systems enables operators to streamline the cycling of the motor valve in response to plunger arrival at the wellhead, line pressures, liquid levels or pressure differentials. Man-hours and the need for routine in-person field visits to manually fine-tune the plunger system have been diminished. However, identifying the underperforming plunger lift wells with the underlying issues requires application-specific domain knowledge. Continuously monitoring and diagnosing the anomalies and failures in a large data stream is time-consuming, the critical decision-making may be interfered or delayed with inconsistent interpretations during various case analyses. This study presents a Bayesian ensemble time-series decomposition algorithm for continuously deriving the nonlinear dynamics of plunger lift operations (detecting the abrupt change, periodic change and trend) and further facilitating the identification of corresponding root causes.
As a Bayesian ensemble algorithm, it quantifies the importance of each pre-defined time-series decomposition model, leveraging all the models via the Bayesian average. The online surveillance data from gas wells equipped with plunger-lift is assessed with expert diagnoses, and the time span of taking the recommended action is recorded and compared with ensemble predictions. The unforeseen benefits of using the proposed algorithm are carried out when the correct diagnoses have been consistently discerned such that a considerable number of similar wells can be analyzed and identified in far less time. We demonstrate the applicability and efficacy of the method in such kind by showcasing several plunger lift anomaly case studies such as liquid loading, tubing rupture, stuck motor valve, line pressure increase, etc.
The successful applications suggest that the proposed ensemble algorithm is one of the key components of automated production diagnostic of plunger lift gas wells. It leads to fast delivery to operators for dynamically tracking plunger lift anomalies and rapidly taking actions for field-wise optimization. The outcomes are threefold: 1) The interpretation of anomalies is identified and validated using four case studies with credible uncertainty measures; 2) Typical plunger lift abnormal signatures are correctly identified and conform to expert diagnoses; 3) Problem troubleshooting is guaranteed in consistency and requires far less time than manpower with domain knowledge.
Deep Learning Applications for Well Log Correlation
Authors: Fu J. , Li R. , Gao T. , Zhang P. , Wang C. 
Published: February 2024

Abstract
This paper presents a novel approach to well-log correlation analysis, leveraging the capabilities of autoencoders within a deep learning framework. We explore the application of both single and dual-log data inputs to autoencoders, aiming to enhance the correlation analysis of geological layers in oil and gas exploration. Our methodology involved the utilization of Log ASCII Standard (LAS) files, containing various log types, such as Gamma Ray from Resistivity (GRR), Resistivity Deep (RD), Bulk Density (ZDEN), and Compensated Neutron Porosity Sandstone (CNC). The research was conducted across two datasets, each presenting different combinations of these logs.
Key findings demonstrate that while single-log autoencoder models provide valuable insights, the integration of dual-log data offers a valuable alternative in scenarios where the most suitable log type for a particular geographical context is unknown. Furthermore, we present a comparative analysis of the correlation accuracy between different log types and their combinations. This analysis highlights the importance of log selection based on geological context and underscores the variability in performance among different logs.
In summary, our research contributes to the field of geological analysis by proposing an autoencoder-based approach that effectively utilizes the power of multiple well-log data types. This methodology not only streamlines the well-log correlation process, but also provides geological insights for geoscientists. They can utilize both single-log and dual-log data inputs to enhance the accuracy, depth, and richness of geological interpretations in oil and gas exploration.
First Long-Term Polymer Injectivity (LTPI) Test in Magwa Field Targeting Medium Quality Formation in Wara, Great Burgan Field
Authors: Al-Qattan Abrar , Al-Murayri Mohammed , Almahmid Narjas , Alkhuzam Sara , Sanaseeri Abbas , Alajmi Naser , Hassan Abrahim , Kakade Shweta , Banerjee Anirban , Mascagnini Carlos , Adrianov Alexey 
Published: February 2024

Abstract
The Greater Burgan field Kuwait is the second largest oil field in the world and the largest sandstone field. Greater Burgan is divided into Ahmadi, Magwa, and Burgan fields. This paper describes the result of the first Long Term Polymer Injecticity Test (LTPIT) in Magwa field targeting wara reservoir with inferior rock quality comparing with the previous LTPI tests implemented in Burgan Field. These multiple LTPI tests located in different areas of the Greater Burgan Field are aimed to de-risk fast-tracked phased commercial polymer-flooding deployment in pursuit of production acceleration, reserves growth and cost optimization.
An existing water injector in Magwa field was selected and connected to existing effluent water plant. Down-hole pressure and temperature gauges were installed for real-time surveillance monitoring of the pilot operation. Effluent water was treated at specific criteria and then mixed with the dry polymer to form the mother solution. This solution was diluted with treated water to achieve targeted injected polymer solution viscosity. Water-polymer solutions were injected using a high pressure non-shearing reciprocating pump. Injection rates, polymer concentration and viscosity were continuously monitored along with pressure and temperature. Samples from nearby producers to detect polymer breakthrough and measure polymer concentration were collected. A commercial simulation package CMG STARS™ was utilized for numerical simulation evaluation.
Pre-selected sulfonated polymer was used to evaluate injectivity in medium quality zone at multiple rates and multiple polymer concentrations under sub-fracturing conditions. The rate and concentration for full-field application were determined. Target viscosity in-situ (4.7 cp) was achieved at lower concentration (1600 ppm). BHP pressure response to different injection rate and concentration was established. Polymer injection has the potential to improve conformance and establish more uniform zonal intake of injected fluids. The results show overall good polymer injectivity under matrix condition, however, dynamic modeling results incorporating pilot test data does not provide a cost-effective development strategy when compared to other parts of greater Burgan field.
The result of the Long-Term Polymer injectivity test will be further integrated to design the polymer phase development in Greater Burgan Field, de-risking the commercial polymer flood development and therefore to save cost, time, and accelerate production in Wara reservoir.
Attention-Based Bi-Directional Gated Recurrent Unit (Bi-GRU) for Sequence Shale Gas Production Forecasting
Authors: Fargalla Mandella Ali M. , Yan Wei , Wu Tao 
Published: February 2024

Abstract
This paper presents an innovative application of an Attention-Based Bi-directional Gated Recurrent Unit (Bi-GRU) network for predicting shale gas production. Traditional machine learning models applied to gas production prediction often struggle to capture the complexity of the production process and accurately model temporal dependencies in the data. The proposed model addresses these limitations by integrating an attention mechanism into a Bi-GRU framework. The attention mechanism assigns relative importance to each time step in the input sequence, focusing on the most influential factors that drive shale gas production over time. Consequently, our model effectively learns long-term dependencies and identifies critical features in the historical data, thereby enhancing prediction accuracy. Furthermore, the bidirectional nature of the Bi-GRU enables the proposed model to consider both past and future time step information in the prediction process, leading to a comprehensive understanding of the sequence data. The results demonstrated the performance of the proposed model on a significant shale gas production dataset, showcasing substantial improvements in prediction accuracy over conventional machine learning and deep learning hybrid-based models. The findings of this study underscore the potential of the Attention-Based Bi-GRU model as a powerful tool for predictive modeling in the domain of energy production.
A Data-Infused Methodology for Estimating Swelling Potential in Shales Exposed to Various Completion Fluids
Authors: Khan Mohammad Rasheed , Tariq Zeeshan , Murtaza Mobeen , Yan Bicheng , Kamal Muhammad Shahzad , Mahmoud Mohamed , Abbasi Asiya 
Published: February 2024

Abstract
Formation damage in reservoirs poses a recurring challenge throughout the phases of drilling, completion, and production, significantly impeding efficiency and diminishing resource extraction in oil and gas development. This detrimentally affects production capacity, leading to potential reservoir shutdowns and hindering the timely discovery and development of oil and gas fields. The water-based drilling fluids are mixed with various swelling inhibitors; nevertheless, shale swelling could still take place during the completion phase as these fluids do not usually consider this phenomenon. To quantify the swelling inhibition potential of drilling/completion fluids, several laboratory experiments are usually carried out. These experiments are costly, time-consuming, and tedious. This study used machine learning technique to predict the dynamic linear swelling of shale wafers treated with different types of completion fluids containing varying inorganic salts such as NaBr, CaBr2, and NH4Q.
A comprehensive experimental investigation was conducted to gather datasets suitable for training machine learning model based on various completion fluid constituents. The study involved utilizing a dynamic linear swell meter to quantify swelling inhibition potentials, assessing sodium bentonite clay wafers' responses to all completion fluid solutions through linear swell tests lasting 24 to 48 hours. Additionally, the study measured zeta potential and conductivities across solutions with different concentrations. Leveraging sequential data and memory cell architectures, the research developed an LSTM (Long Short-Term Memory) machine learning model aimed at predicting and comprehending swelling behaviors within specific contexts. This model was trained using input parameters such as zeta potential, salt conductivity, salt concentrations, density, and elapsed time, while the model output represented dynamic linear swelling in percentage.
This intelligent technique can be used to guide and streamline laboratory experiments to determine dynamic linear swelling of shales. It can serve as a quick tool to guide fluid engineers at the rig site to delineate shale swelling reasons pre-, post-, and during completion operations. Consequently, operators will be better prepared to deal with unknown swelling issues that lead to NPT in operations.
Integrated Engineering Approach to Drill a Complex Horizontal Well with Stringent Requirements Dictated: A Case Study from Offshore North West Java, Indonesia
Authors: Yuda Bintang K. , Ridhanta I Nyoman , Sany M. Fajar , Kurniawan Aris , Adi Bayu Arie Punto 
Published: February 2024

Abstract
The objective of this paper is to present a directional drilling approach and the technical solution used to overcome directional challenges during landing horizontal section in Carbonate Baturaja of Offshore North West Java. The main directional challenge is the limited directional space to be able landing with 85-degree inclination +/- 3'TVD inside Top Carbonate Baturaja formation since the target landing coordinate is pretty much close to the platform. The precision in hitting landing point is very critical, firstly because there is a high possibility trajectory become a "hook" considering the lateral section is only 20’ TVD below the top Baturaja. The other well requirements/conditions also make directional work even more challenging such as collision issue at the surface interval, the ability to kick off in very soft formation, the uncertainty of Top Baturaja that affect space for directional work from kick-off to landing, and space tangent section for ESP placement to prevent ESP failure during production.
The integrated directional approach starts from the preparation phase during initiating the feasible trajectory and conducts pre-rig move work such as enhancing data survey quality of the offset well, choosing the best available surface slot, selecting a type of trajectory including the option of negative section trajectory (Pregnant Lady) to add more space for directional and the necessary of the pilot hole, and then conduct the assessment for some worse case scenarios based on challenges. The next step is more to BHA tool selections to enhance the success ratio and give more versatility if the worst-case scenarios happened including developing a landing strategy decision tree. The last step is monitoring strategy during the execution and live updating the directional plan based on actual progress and doing the replan to ensure landing as per plan. Geo-steering was implemented while drilling in the horizontal section for accurate well placement into the very thin Carbonate Baturaja reservoir (+/- 30-50 ft TVD) which is also known as total loss circulation formation.
The implementation of this integrated directional approach requires good coordination among the team involved in achieving technical excellence. In the execution phase, by adhering to the project management process developed including the decision tree and the mitigations, ZUG-X was successfully constructed to hit the landing point as per target without inducing any safety incident and related Non-Productive Time (NPT). Moreover, this became the first negative section well drilled in a horizontal well in North West Java and could be implemented in the future.
Fractal Dimension Measurement Using Wireline-Derived Saturation Height Function
Authors: Altayeb Mohammad , Glover Paul W. J. , Lorinczi Piroska , Cuddy Steve 
Published: February 2024

Abstract
Fractal geometry represents a self-similar object or behavior over different scales. Fractals occur in many aspects of nature including reservoir pore geometry. Fractal dimension is a key parameter that represents how complexity changes with scale. This study attempts to measure the fractal dimension using a power law-based saturation height function that is derived from wireline data. The approach involves estimating the saturation height function (SwH) using Cuddy's method with wire-line data. This method plots water bulk volume (BVW) against height above the free water level (H). Major steps to estimate SwH include identification of the free water level, the presence of shale volume and calculating porosity, water resistivity and water saturation. Cuddy's method often reveals that SwH follows a power law behavior, which is expressed linearly when logarithmic scales are used. Consequently, SwH can be estimated by fitting a line to the data and obtaining two parameters a and b representing the intercept and gradient, respectively. The SwH of 13 wells were derived using Cuddy's method and showed acceptable fit to the power-law assumption. The parameter b, which represents the gradient of the best fit line, has been hypothesized to be related to the fractal dimension. Therefore, the estimated SwH may provide a measurement of fractal dimension of the pore geometry. The fractal dimension is related to the pore geometry heterogeneity, where higher fractal dimension implies higher heterogeneity. Fractal dimension applications include heterogeneity evaluation of pore geometry, reservoir modelling and performance simulation.