bluuebunny/crossref_metadata_2025 huggingface dataset:
Reductions in Emissions and Fuel Cost with Start/Stop System Technology for Diesel Frac Fleets
Authors: Fulks Robert , Shelton Robert , Bishop Mark
Published: January 2023

Idling during hydraulic fracturing generates considerable emissions of NOX, CO, and particulate matter (PM). Field studies conducted during 2021-22 documented an average of 5 - 10 hours per day of diesel-powered idling during fracturing operations across multiple U.S. unconventional basins.

Prior to 2021 boardroom level corporate environment, social, and governance (ESG) initiatives by oil & gas producers focused on limiting gas leaks, Scope 1 (direct corporate) emissions, and overall carbon footprints. Controlling emissions during the idling of hydraulic fracturing equipment, until recently, had not been a high priority on executive ESG lists. "The name of the game in unconventional shale development has quickly shifted from production at all costs to maximizing cash flow and reducing emissions to improve ESG performance. Operators have placed a priority on their ESG efforts as financial institutions have prioritized responsible investing." (Walzel, B. et.al., 2021).

By late 2021 concerns associated with idling during fracturing operations drew more attention from operators. The issues of idle waste control (unnecessary emissions, fuel, and excessive maintenance cost), identified as easy to resolve, garnered limited attention. Yet by early 2022, the challenges associated with frac diesel idling remained prevalent and became more acute with the dramatic rise in diesel costs.

In early 2022 ESG surveys began to include mention of frac idle waste. Contributing to this is the fact U.S. frac fleets continued utilizing diesel and/or dual fuel (diesel combined with natural gas) engines for 91% of fracturing operations. One of the lesser-known characteristics of Tier 2 and Tier 4 diesel frac pump engines is the fact they burn 100% diesel during idling – including dual fuel engines.

Stop/Start technology, first adopted in the auto industry and later by long haul diesel-powered trucking companies, began gaining traction within the oil & gas industry in 2017. The reduction in idle times attributed to Start/Stop systems proved lower fuel consumption and emissions generation rates were possible. This study focuses on the contributions made by diesel Start/Stop technologies. Positive results from 2019 forward substantiate the efficacy of idle reduction methods used in conjunction with hydraulic fracturing. Limiting frac idle times proved effective in reducing frac emissions, lowering fuel consumption, as well as cutting maintenance costs for hydraulic fracturing fleets.

This report highlights a new Start/Stop technology with field results from 2021 – 2022. This recent technology delivered a simpler, innovative hydraulic start centralized plug & play method of powering an entire frac fleet rather than using multiple Start/Stop systems installed to electrically start each individual frac pump. The results of a 2022 Permian basin technology field application are presented here.
Validation of Continuous Wavelet Transform Closure Detection Technique Using Strain Measurements
Authors: Adel Gabry Mohamed , Eltaleb Ibrahim , Soliman M. Y. , Farouq-Ali S. M. , Cook Paul. J. , Soom Florian. A. , Guglielmi Yves
Published: January 2023

Although closure detection has a crucial role in hydraulic fracturing operations, significant debate surrounds the various methodologies to determine its value. Several competing methodologies have been presented in the literature that sometimesyield significantly different estimates of closure pressure and time. The conventional techniques rely on assumptions that may be competing or even contradictory.

The continuous wavelets transform technique is a data transform technique that convolves the pressure and/or temperature data using a short wavy signal called "wavelet". The wavelet transform provides a representation of the pressure signal by letting the translation and scale parameters of the wavelets vary continuously. That enables the analyst to find the details of the pressure data by observing the wavelet energy spectrum for the monitored signal (pressure and/or temperature) signal. In this case the event of contact between two fracture faces and complete fracture closure is clearly identified.

As a part of The EGS Collab project, a series of fracture injection tests have been conducted to estimate the minimum principal stress with direct observation of well bore deformation using the SIMFIP tool (Step-Rate Injection Method for Fracture In-Situ Properties). The tool monitors the deformation using strain gauges as a fracture opens and closes during multiple tests. The publicly available data provide a great opportunity to experimentally calibrate the new technique for detecting the closure event using continuous wavelet transform. The effect of fracture closure events and fracture faces contact events detected using continuous wavelet transform were compared to the experimental measured deformation.

The continuous wavelet transform technique for closure detection showed an agreement with the deformation measurement. The effect of the presence of natural fractures and complex fracture closure events were recognized using the continuous wavelet transform technique. The Contineous Wavelet Transform (CWT) is a global technique that can be applied to the pressure decline data without requiring further information about the reservoir geomechanical parameters or pumping data. The technique can be easily embedded in machine learning algorithms for hydraulic fracturing diagnostics.
Pressure Transient Analyses and Poroelastic Modeling of Hydraulic Fracture Dilation for Multiple Injections at the Devine Fracture Pilot Site
Authors: Haddad Mahdi , Ahmadian Mohsen
Published: January 2023

Our team has conducted electromagnetic (EM) surveys for the past six years to monitor hydraulic-fracture behavior at the Devine Fracture Pilot Site (DFPS). The sub-horizontal orientation of a shallow hydraulic fracture at the DFPS provides uniform access to the fracture area for interrogation and data collection. Ahmadian et al. (2023) suggested a possible correlation between spatiotemporal changes in the flow rate, bottomhole pressure (BHP), and the observed surface recorded electric field at the DFPS. In this paper, we present the development of poroelastic forward models and pressure transient analyses (PTAs) to support the development of a multiphysics inverse model for these EM surveys.

First, we conducted PTAs of the shut-in periods after six injections out of 10 to determine the fracture closure pressure (FCP) or the overburden pressure used in a poroelastic fracture reopening model. Second, we developed a finite-element poroelastic model throughout five injection cycles to include the effect of the cumulative injected volumes due to the previous injections on current fracture dilation in the presence of highly permeable unpropped and propped zones adjacent to the cohesive layer that models fracture reopening. Fracture reopening in this poroelastic model is based on a calibrated traction-separation response using the bottomhole pressure collected in two injection campaigns in 2020 and 2022. We used the outcomes of a previous simulation study of the primary hydraulic-fracturing stimulation to define the dimension of an unpropped fracture zone ahead of the propped fracture area.

The PTAs led to FCPs consistent with those obtained using the injection data collected at the DFPS in 2020. Further, these analyses showed that at later injections, the fracture closure occurred at a later time with respect to the shut-in time, inferring the effect of cumulative injected volumes in previous injections. The simulation results show that considering the propped and unpropped fracture zones improves our poroelastic model in predicting the injection-well BHP. The numerical simulation results demonstrate a significant excess pore pressure near the fracture because of the preceding formation loadings by the previous injections.

The obtained fracture dilation area and fluid pressure distribution provide a basis to improve the development of a multiphysics inverse model. Furthermore, in an iteratively coupled scheme, this pressure distribution can be introduced into EM models to render a holistic view of the causative mechanisms for the surface signal anomalies.
Sealed Wellbore Pressure Monitoring (SWPM) and Calibrated Fracture Modeling: The Next Step in Unconventional Completions Optimization
Authors: Olson Karen , Merritt Joshua , Barraez Rair , Fowler Garrett , Haffener Jackson , Haustveit Kyle
Published: January 2023

Sealed Wellbore Pressure Monitoring (SWPM) has been utilized across North and South America Basins with over 16,000 stages monitored as of June 2022. Since May 2020, the analysis procedure has been automated using a cloud-based software platform designed to ingest, process, and analyze high-frequency hydraulic fracturing data (Iriarte et al., 2021). A real time option of SWPM was also developed to aid in real time fracturing decisions (Ramirez et al., 2022).

The latest development is the added capability of a fracture model that can automatically history match the volume to first responses (VFRs) determined from SWPM. This next level allows for the matching of the VFRs and the visualization of the resulting fracture geometries from a fully-coupled fracture propagation, reservoir, and geomechanics simulator. The simulator is capable of accounting for complex processes such as poroelastic stress changes from depletion, allowing for evaluation of complex interactions of fracture propagation and depletion. Insights gained from this process allows the operator to optimize their completion design faster and with fewer field trials.

This paper’s focus is a case study of the DOE Eagle Ford refracturing project where a range of completion designs were trialed while monitoring offset SWPM and fiber optic strain. The resulting VFRs of the SWPM project were compared to the fiber data and then used to calibrate the fracture model. Fracture model calibration was first performed assuming that restimulation fractures propagated independently of the previously created fractures. The VFR of each stage design was calculated and summarized. The model is constructed with three stage designs primarily identified by cluster count: 7-clusters, 12-clusters, and 22-clusters. The VFR for the 7-cluster stage design was then used as an objective in an automated history matching algorithm employing the fracture model. The resulting best fit model was then evaluated on VFRs for the 12 and 22-cluster stage designs.

The results demonstrate the model calibrated to the VFR of the 7-cluster stage design was able to predict VFRs in the far field for 12 and 22-cluster stage designs. Further, it is shown that including the original fractures in the model and allowing crossflow between the original and newly created fractures can match the rapid VFRs observed on a minority of stages. These same results were confirmed by the fiber data (not shared with modelers prior to calibration).

Conclusions of the DOE project will show the optimum cluster spacing, cluster count and stage spacing as confirmed by the SWPM analysis and the fracture modeling.
Hydraulic Fracturing Value Boosting Through Operational Innovation and Data Analytics, MDC and Inchi Fields Case Study
Authors: Salazar Franck , Vasconez Nestor Danilo , Artola Pedro , Jaramillo Dorian , Cueva Diego , Cuenca Dario , Coronel Bernardo , Unapanta Mauricio
Published: January 2023

A fracturing campaign in mature fields in Ecuador demonstrated the advantages of hydraulic fracturing to optimize production and maximize the extraction of the remaining reserves. Good design practices were key to the success of the fracturing campaign in MDC and Inchi fields.

Initially, a comprehensive process of characterization was carried out to select the candidates for hydraulic fracturing in the Napo U and T formations to perform an initial fracturing campaign, studying among other characteristics, reservoir permeability, skin, pore pressure, remaining oil saturation, porosity, geomechanical properties, and completion integrity. A small group of wells was selected for hydraulic fracturing using the channel fracturing technique. The second phase consisted of optimizing the fracture design by improving the fracture geometry and conductivity, as well as the application proppant flowback control. Improved fracture geometry and proppant flowback prevention were identified as key elements for the success of the fracturing campaign in these mature fields.

Fracturing channel technique was implemented to generate higher fracture conductivity in a low reservoir pressure environment by creating a highly conductive fracture that reduce the drawdown pressure during production. Because of successful implementation, the channel fracturing technique became the preferred completion method in the field for wells requiring stimulation. Twenty five hydraulic fracturing treatments were performed from 2018 to 2022, all demonstrating outstanding production results. The implementation of hydraulic fracturing increased the volume of recoverable reserves by 20%. Operationally, the application of channel fracturing allowed performing more aggressive pump schedules without the risk of screenout, achieving fracture conductivities in the order of 90,000 md-ft and skin values of –2 and –3.5.

The learning curve and the results obtained in these fields are important sources of information for implementing hydraulic fracturing in mature fields to increase production and reduce risk.
A Novel Semi-Supervised Clustering Method for Carbonate Pore Size Distribution in Middle East Using Earth Mover Distance
Authors: Ma Ruicheng , Hu Dandan , Zhao Zeqi , Li Yong , Zhao Yixuan , Gu Fei , Wang Yuning
Published: January 2023

Pore space texture and its pore size distribution are critical parameters for displacement efficiency for waterflood, which have been applied in Middle East. For sandstone, pore size is a log-normal distribution usually. However, when it comes to carbonate reservoir, complex sedimentary and diagenesis yield complex pore size distributions including unimodal, bimodal and multimodal distribution. Therefore, it is not reasonable to classify different pore size distribution through statistic parameters such as mean, standard deviation, skewness and kurtosis. It is essential to propose a novel method to classify numerous pore size distribution results accurately and automatically by curve shape. This paper proposes the application of earth mover's distance (EMD) to pore size distribution, which is not mentioned before. The automatic clustering method is efficient and accurate to process massive data. Therefore, this method can prompt to systematic analysis and research of a giant scenario of reservoir injection characteristics in Middle East.
PVT Data Making the Difference: Delineating the Size of Gas Caps in Stacked Reservoirs
Authors: in ‘t Panhuis Peter , Van Jaarsveld Jort , Cornelisse Peter , Ramaswami Shyamalan , Janson David , Rien Descamps
Published: January 2023

A proper characterization of fluid contacts and their uncertainties are an essential input to the development of gas and oil reservoirs. However, during exploration and early appraisal there is usually little information available that can reliably anchor the uncertainty ranges. Depending on the expected fluid type there may be little infrastructure in place to evacuate the reservoir fluids. Particularly the presence of a large gas cap can have a big impact on the economics of a potential development, depending on the evacuation options for the gas. This paper aims to show how PVT data may be used to delineate the size of the gas caps in stacked reservoirs.
Evaluation of Different Numerical Techniques for Accurate Modelling of Tracer Flow in Porous Media
Authors: Yaralidarani Muhammad , Aghabozorgi Shokoufeh , Farzaneh Seyed Amir , Sohrabi Mehran
Published: January 2023

Inter-well tracer tests have numerous applications in determining the volumetric sweep, levels of heterogeneity, and delineation of flow barriers such as the faults in subsurface in reservoirs. Tracer tests are also performed in the laboratory to determine core samples’ heterogeneity. Simulation of physical dispersion requires refined models, which inevitably increase the numerical dispersion in the obtained results. In this paper, we quantify the numerical dispersion associated with various techniques available for the simulation of tracer flow. Numerical dispersion in the simulations can be quantified by comparing the simulation results with the experimental data. For this purpose, we first reviewed the fundamentals of tracer flow and introduced the Convection-dispersion equation as a basic model to describe tracer flow in porous media. Then we constructed a refined model to simulate a series of tracer flood scenarios. A detailed sensitivity analysis was carried out in a systematic manner to specify the individual impact of physical and numerical dispersion. Finally, we modelled tracer experiments performed on Indiana Limestone (IL) carbonate rocks to examine the accuracy of Fick's and Darcy's equations, and the results are presented. The impact of both numerical (non-physical) and physical dispersion was examined during the core scale simulations. It was concluded that the numerical TVD algorithm (embedded within the commercial ECLIPSE software) can appropriately model the tracer flow in porous media with minimal numerical errors during simulations. It was also shown that physical dispersion significantly affects tracer test results and that it must be considered when simulating tracer flows by defining an appropriate Peclet number. Finally, the results showed that solving the conventional convection-dispersion equation along with the numerical TVD algorithm can perfectly match the experimental data of several tracer flood tests performed on outcrop Indiana limestone core samples.
Integrated History Matching Method for Horizontal Wells Using Advanced Pressure Transient Analysis
Authors: Ma Ruicheng , Wang Shu , Zhao Yixuan , Li Yong , Hu Dandan , Zhao Zeqi , Hao Siying , Chen Yihang
Published: January 2023

Horizontal wells have been applied in Middle East universally. However, with complex geological conditions including thief zone, aquifer and heavy oil, there are diverse production/injection performances for horizontal wells. Therefore, history matching is a tough job for reservoirs with horizontal wells. Pressure transient analysis (PTA) data is abundant and relatively economically obtained. With PTA technique, reservoir properties, streamline distribution and effective horizontal interval can be obtained easily. Therefore, history matching results could be identical with actual reservoir performance.

This paper proposes an optimized history matching method for horizontal wells assisted by advanced pressure transient analysis. Considering heterogeneity of layer-cake reservoir, a newly corrected method of log-log plot of PTA analysis is built to make a better interpretation under the effect of high permeability zone (HPZ). Then pressure transient analysis results will be validated with production/injection performance and other surveillance data such as resistivity logging. Finally, based on interpreted results, history matching is conducted. The advanced PTA model yields favorable interpreted results in accordance with production/injection performance and other surveillance data. Integrated history matching yields better results compared with typical workflow and modification of dynamic model. For several tough problems such as layer-cake reservoir with bottom aquifer, profile tuning is better than modification of grid property around wellbore.
Application of Historical Pressures for Mapping Reservoir Heterogeneity in Brown Fields
Authors: Kayode Babatope Omotayo , Stephen Karl D.
Published: January 2023

After a reservoir's structural framework has been established using seismic and well tops data, a conceptual heterogeneity pattern is used to control the spatial distribution of core and log data. Traditionally, seismic acoustic impedance or depositional facies map are the conceptual heterogeneity patterns used. Sometimes, facies map may be derived from limited wells, and seismic data is expensive to acquire. In such cases, a purely statistical approach like sequential gaussian simulation is used and the resulting heterogeneity pattern may have large uncertainty, being driven by probability rather than actual data. The goal of this study is to derive a new conceptual heterogeneity pattern using inexpensive and readily available historical pressure data.

It is known that pressure cluster differentiation can be caused by faults or fractures, we investigated in this study whether a non-faulted and non-fractured reservoir can exhibit pressure cluster differentiation. Numerical experiments are performed using hypothetical models to study the impact that stepwise and continuous permeability variations, the permeability ratio, duration of shut-in, and flowrate all have on the historical pressure trends of wells located within regions of similar and different permeability magnitudes. The historical pressures for all wells are plotted on the same axis to assess cluster differentiation. Closed polygons are drawn around the wells within each differentiated cluster.

The simulation results indicate that pressure cluster differentiation can occur in non-faulted and non-fractured reservoirs and typically implies heterogeneity. Therefore, in a reservoir where pressure cluster differentiation is observed, mapping the spatial locations of wells within each pressure cluster would result in a reservoir heterogeneity map which is shown to be comparable to seismic acoustic impedance map in an example dataset. A key contribution of this study is the development of an alternative conceptual heterogeneity pattern, using inexpensive and readily available data, that can be used as a trend map for guiding spatial property distribution during geo-modeling. An application example is used to further strengthen the conclusions derived from numerical experiments.
An Innovative AI Physics Hybrid Technology to Overcome the Time-Intensive History Matching Challenges, A Case Study: Minagish Oil Field
Authors: Qubian Ali , Zekraoui Mohammed Ahmad , Mohajeri Sina , Mortezazadeh Emad , Eslahi Reza , Bakhtiari Maryam , Al Dabbous Abrar , Al Sagheer Asma , Alizadeh Ali , Zeinali Mostafa
Published: January 2023

Reservoir simulation is the main factor in decisions made by oil companies in reservoir management. However, the simulation of huge and complex oil reservoirs through a time-saving and high-accuracy method is the primary concern in reservoir simulation. In this study, a novel AI-Physics hybrid model was proposed for combining with the traditional reservoir simulation to overcome the time-intensive history matching challenges. A combination of classical numerical simulation and deep learning neural network was applied to train the hybrid model with historical data. As a result, a model was obtained with predictive capabilities to forecast the field's behavior. Then, we combined AI-Physics history training with blind test prediction calculation of remaining oil maps. Finally, forecast scenario definitions based on the remaining oil map were created by the AI-Physic model. The proposed novel simulation method can reduce the history matching and scenario assessment time by 90 to 95%. According to its capabilities, three improved forecast scenarios were created based on a predefined scenario. These improved scenarios can produce a significant million standard barrels more oil than the original development scenario within three years. This technology eliminates limitations for multiple scenario assessments. In our AI hybrid model, the power of dynamic reservoir simulation is combined with a modern machine learning approach to "Evergreen" forecasts in reservoir assets. Consequently, the simulation resulted in a sub-optimal shortcut between model updates and inconsistencies in production forecasting. Moreover, applying deep learning methods to focus on the critical reservoir properties intelligently leads to tremendous time-saving in the static model update life cycle. In fact, with this novel simulation that we implemented, the new production data could be incorporated within minutes to regenerate more reliable and up-to-date forecasts. This simulation generates ‘up-to-date’ remaining hydrocarbon maps interactively, so the operator can continuously optimize the infill drilling locations between Field Development Plan (FDP) cycles.
Trial and Assessment of a New Downhole Sand Production Measurement in Oil and Gas wells
Authors: Eltaher Yahia , Nasser Mamdouh , Tarihoran Anton , Zaouali Zouhir
Published: January 2023

Sand production is one of the major issues in unconsolidated sandstone gas and oil reservoirs. The amount of the produced sand in gas wells might vary from a few grams to a few kilograms per hour, yet it can lead to the erosion of downhole and surface equipment and to partial or complete filling of the perforations, the screens or gravel pack completions downhole, hence localizing and quantifying the amount of sand downhole is a necessity for sand production management and sand shut-off, and to update accurately the existing geomechanical models to save huge costs involved from the CT clean-out operations and the repairs caused by erosion.

A newly developed downhole sand production measurement was successfully tested. The tool incorporates an innovative design that enables enhanced sensitivity to minute sand entries within the wellbore with minimal interference from the logging environment. This novel measurement was trial tested in different well completions. The carefully designed logging programs involved combining production logging tools – either conventional or array logging tools – along with the downhole sand detection tool for comprehensive log data interpretation. In this paper we illustrate with examples the results of the trial test and pinpoint the advantages as well as limitations of this new technology.

Log results in the vertical gas well were generally superior to those from other wells. Where the tool illustrated high sensitivity to locating small grains of sand production across the perforated intervals, as well as providing representative qualitative assessment of downhole sand production volume. Yet, most importantly it helped obtain the optimum drawdown for the free sand rate in this well. The tool was deployed also in one deviated oil well and two horizontal gas wells. These wells are completed with smart completions, sand screens and frac ports. The results helped reveal the efficiency of the screens and the integrity issues of frac ports and determine future plan to patch the frac ports to shut-off sand production. It is generally observed that log data quality and interpretation were challenged by the downhole logging environment and the well condition during log data acquisition.

The new downhole sand detection technology shows added value to allocate and qualitatively quantify sand production, check the integrity of sand screens and gravel packs, and several other applications. Candidate selection, job design and real-time log monitoring are crucial for the optimum benefit of the logging survey.
Carbon/Oxygen Spectral Data Processing, its Affiliation to Scintillation Detector Selectivity & their Impact on Reservoir Saturation Monitoring, Lessons Learnt and Recommended Workflow
Authors: Eltaher Yahia , Ma Shouxiang
Published: January 2023

For decades, it has been affirmed that pulsed neutron (PN) spectral Carbon/Oxygen (C/O) logging is the industry's most robust salinity-independent means for reservoir saturation monitoring (RSM); yet C/O logging still comes with considerable uncertainty that has to be identified and handled with ultimate care. In this paper we investigate two main aspects of such uncertainties and showcase some recommendations to enhance the accuracy of the measurement for improved reservoir saturation monitoring.

Two fundamental factors affecting C/O measurement are the type of gamma ray (GR) scintillation detector crystals used and the method for C/O spectral data processing. Currently, there are mostly six types of crystals used as GR detectors in commercial PN logging tools for routine operations. Each detector type has its advantages and limitations. With respect to data processing, the most commonly adopted method is the Windows method, due to its simplicity and statistical robustness. Whereas the Yields method is much more complicated to develop and prone to statistical variation, though it tends to provide more accurate results. Similarly, each of these two methods has its own set of advantages and disadvantages.

A comprehensive study involved different logging instruments and datasets acquired under various logging environments showed that both the physical properties of the detector, as well as the characteristics of the data processing method, have to be fully considered for optimum results. The Windows method, for instance, can be adequate for detectors of statistical nature. Unlike the Yields method, which requires an optimized set of detector and tool specifications. Where for certain GR detectors, significant differences in C/O data and consequently the calculated fluid saturation were observed when processed by using the Windows and the Yields methods. C/O data processing method selection is commonly fit for purpose; yet with the continuous advancement in GR detection technology, standardization is recommended for accurate and precise log measurement.

Accuracy and precision are keys to C/O logging and consequently successful reservoir surveillance and oil field management. Accordingly, a new standard RSM workflow is recommended where all available elements are properly tailored, to enhance the quality of the answer product.
Reservoir Modeling and Optimization Based on Deep Learning with Application to Enhanced Geothermal Systems
Authors: Yan Bicheng , Xu Zhen , Gudala Manojkumar , Tariq Zeeshan , Finkbeiner Thomas
Published: January 2023

With the energy demand arising globally, geothermal recovery by Enhanced Geothermal Systems (EGS) becomes a promising option to bring a sustainable energy supply and mitigate CO2 emission. However, reservoir management of EGS primarily relies on reservoir simulation, which is quite expensive due to the reservoir heterogeneity, the interaction of matrix and fractures, and the intrinsic multi-physics coupled nature. Therefore, an efficient optimization framework is critical for the management of EGS.

We develop a general reservoir management framework with multiple optimization options. A robust forward surrogate model fl is developed based on a convolutional neural network, and it successfully learns the nonlinear relationship between input reservoir model parameters (e.g., fracture permeability field) and interested state variables (e.g., temperature field and produced fluid temperature). fl is trained using simulation data from EGS coupled thermal-hydro simulation model by sampling reservoir model parameters. As fl is accurate, efficient and fully differentiable, EGS thermal efficiency can be optimized following two schemes: (1) training a control network fc to map reservoir geological parameters to reservoir decision parameters by coupling it withfl ; (2) directly optimizing the reservoir decision parameters based on coupling the existing optimizers such as Adam withfl.

The forward model fl performs accurate and stable predictions of evolving temperature fields (relative error1.27±0.89%) in EGS and the time series of produced fluid temperature (relative error0.26±0.46%), and its speedup to the counterpart high-fidelity simulator is 4564 times. When optimizing withfc, we achieve thermal recovery with a reasonable accuracy but significantly low CPU time during inference, 0.11 seconds/optimization. When optimizing with Adam optimizer, we achieve the objective perfectly with relatively high CPU time, 4.58 seconds/optimization. This is because the former optimization scheme requires a training stage of fc but its inference is non-iterative, while the latter scheme requires an iterative inference but no training stage. We also investigate the option to use fc inference as an initial guess for Adam optimization, which decreases Adam's CPU time, but with excellent achievement in the objective function. This is the highest recommended option among the three evaluated. Efficiency, scalability and accuracy observed in our reservoir management framework makes it highly applicable to near real-time reservoir management in EGS as well as other similar system management processes.
Identifying New Behind Casing Opportunities Using Machine Learning
Authors: Fadhil Imran M , Shah Jamari M , Sansudin Salmi , Abdollahzadeh Asaad , Husiyandi Husni , Azizul Nur Aimi Azimah , Hasnan Fairuz Hidayah , Thai Yuan Jiun
Published: January 2023

This paper discusses the adoption of Machine Learning (ML) approach to identify new Behind Casing Opportunities (BCO) in two brown fields (B and S) offshore East Malaysia. A multi-stage field-based ML models were developed based on selected wells and consequently used to predict reservoir characteristics in completed wells. The predicted results indicated new upside BCO for add perforation candidate.

Raw and interpreted data from B and S fields were analyzed and processed for model training and evaluation. For the case of identifying new opportunity, a specific model development strategy and train dataset selection was employed. The trained ML models evaluated to select the optimal models to predict lithologies, porosity, permeability and water saturations which are then been compared against the actual interpretation. Eventually, the identified upside potentials are validated by Subject Matter Experts (SME) before being proposed as add perforation candidate.

It was observed that the models’ performances vary between the two fields due to unique geological complexity as well as the varying quality of raw and interpreted data from each field. Field B which is more geologically complex performs less compared to Field S.

In conclusion, this study provides and insight on the advantages and limitations of machine learning to identify new upside BCO in completed wells. The novelty in this work is in the specific model development strategy to identify new upside BCO potentials.

This work may be beneficial and essential especially in enhancing resource monetization in brown fields which face challenges in terms of high idle well percentage, low recovery, and declining production.
Data Integration as a Key of Reservoir Characterization and Optimize the Well Placement. Abu Dhabi Offshore Case Study
Authors: Elabsy Eslam Mohamed , Soliman Ahmed Mohamed , Deng Lichuan , Aouacheri Amel , Alabed Sundos Ibrahim , Honda Kazumasa , Alsabeai Jawaher Adel
Published: January 2023

Carbonates heterogeneity is a continuous challenge for subsurface experts. The case in hand illustrates the advantage of data integration between Logging While Drilling (LWD) including Nuclear Magnetic Resonance (NMR), High-resolution image and Wireline (WL) formation testing and fluid sampling. The formation evaluation integration analysis results are used for characterizing the rock quality across multiple reservoir zones and to determine the optimum zones to be drilled horizontally and completed using the inflow control device (ICD) completion.

Appraisal and improvement of the lateral drainage area of the reservoir are the main objective of the field development strategy, by drilling pilot holes to evaluate Cretaceous carbonate layers then targeting the optimum zone for lateral side-track based on the provided data. Therefore, the plan has initiated by deploying the advanced LWD including the Gamma Ray, propagation Resistivity, Neutron, Density and Micro-Resistivity imaging along with NMR to acquire all the needed data for formation evaluation and reservoir characterization. The formation pressure testing and sampling device has been deployed using Wireline (WL) for the comprehensive evaluation of subsurface resources.

In this study, the reservoir petrophysical properties including total, effective porosity, permeability, movable fluid and bound water volume provided from NMR played an important role for the reservoir characterization and enhancement of the final formation evaluation results, based on the NMR results the formation testing / sampling points were decided. The Micro-Resistivity image revealed various types of geological features such as the conductive, enhanced, resistive fractures and different bedding plane in addition to different geological textures were interpreted along the well trajectory and was used to fine tune the formation testing and fluid sampling zones. The fluid mobility analysis from formation testing is used to calibrate the NMR permeability index along with the comparison with core data, also, the fluid characterization and sampling result are integrated with the rest of the data to formulate better reservoir understanding. The final integrated results from all the data and analysis used to identify the highest potential zones, therefore the side-track lateral drain was planned and geosteered based on the data integration results from the pilot hole. By extending the pilot hole understanding to the lateral section, the lateral section completed successfully across the optimum zones.

This paper provides insight into the integration of the Open Hole logging data from Logging While Drilling and Wireline to eliminate the uncertainty of the reservoir characterization and Formation Evaluation analysis to sustain and increase the oil production.
Use of Deconvolution for Pressure Transient Analysis in Layered Reservoirs – A New Method
Authors: Khalaf Mina S. , El-Banbi Ahmed H. , Sayyouh M. H.
Published: January 2023

Many reservoirs exhibit multilayer behavior. Multilayer oil/gas reservoirs are usually classified as (1) systems with formation crossflow and (2) systems without formation crossflow (commingled systems). The focus in this work is the analysis of pressure transient data of commingled layers.

Pressure transient analysis for multilayer reservoirs to estimate individual layer properties is usually difficult and suffers from many limiting assumptions. The available interpretation methods are usually based on a subjectively presumed model (usually homogenous and isotropic layers, and radial infinite reservoirs) that has many unknown parameters (e.g., permeabilities and skin factors) to be estimated through a history matching process. The individual layer properties obtained from such analysis methods may not be reliable. The reliable way to evaluate individual layers’ characteristics is to isolate and test each layer separately, which is challenging due to high costs and occasional operational constraints.

In this work, we suggest a testing procedure and an analysis approach to analyze well test data of commingled reservoirs that allows reliable characterization of individual layers. The approach benefits from modern deconvolution techniques to eliminate the rate variation (rate transients) effects from the bottom-hole pressure signal acquired during the test. The methodology presented does not make assumptions about the individual layer reservoir models and recovers distinct pressure signals of the individual layers. The recovered pressure signals of each layer are also free of wellbore storage (WBS) effects. In addition, the individual layers’ pressure signals are stretched over the whole test duration (both drawdown and buildup periods).

Successful application of deconvolution requires complete sandface rate data of the individual layers in the commingled system. However, the continuous measurement of individual layers sandface rates is usually not available. A simple model is introduced in this work to make good estimations of the layer rate profile using few measurements of the production logging tool (PLT). The developed rate profiles are then used in deconvolution and individual layer's pressure signal can be recovered for further analysis.

The approach was verified against simulated cases with variety of layer models. The results obtained from the developed approach are in good agreement with the true solution. The findings of this study can be used to characterize commingled reservoir systems and determine the individual layers properties. It has applications in optimizing injection/production well performance of commingled systems.
Identifying and Characterizing Mass-Transport Complexes in the Longmaxi Shale in Sichuan Basin, China
Authors: Liang Xing , Shu Honglin , Zhang Jiehui , Luo Yufeng , Liu Bo , Zhang Xu , Zhao Chunduan , Li Shenzhuan , Sultan Gamal , Shasmal Sudipan
Published: January 2023

The Lower Silurian Longmaxi Shale Formation of Sichuan Basin is the largest shale gas reservoir in China. It was formed mainly in a deepwater shelf depositional environment with a relatively stable tectonic background. More and more cores and borehole images

data from the Longmaxi Shale revealed that the sedimentary features show considerable changes in mineralogy or texture in different regions. Mass-transport complexes (MTC) were identified for the first time in the Longmaxi Shale based on the integration of logs, borehole images, and seismic data. The MTC have led to a clearly abnormal formation thickening in both well and seismic data. Identifying and characterizing the MTC in the Longmaxi Shale is the objective of this case study.

The integration of the logs, borehole images, and seismic data brings more insights into different aspects of MTC. The repeat section that results from the MTC can be identified based on the well logs directly. Detailed analysis of the borehole images yields additional evidence such as lithofacies and textures in the MTC. The faults and fractures in the MTC can also reveal the controlling factors on the shale gas enrichment. The analogs between the concept model and seismic reflections have improved our understanding of when and how the MTC are likely to form in the Longmaxi Shale.

The MTC in the Longmaxi Shale are developed above the detachment surface between the calcite-cemented sandstone body and the high-clay-volume shale. It brings more than 20 m of repeat section inside the Longmaxi Formation. The boundary between the original and repeat sections is a low-angle thrust fault. The repeat sections can be divided into four zones based on their log responses. The gamma ray (GR) response and the borehole image features have validated the presence of the repeat section. The minerology differences between the original and repeat sections have highlighted the facies variations in the Longmaxi Shale. This is also the reason why the MTC could occur in this area. Based on the calibrations of well data, the geometry and architecture of the MTC are readily observed in 3D seismic data. The MTC in the Longmaxi Shale are formed in a slope background. It helps to get more understanding of the sedimentary variations in the Longmaxi Formation. The process of MTC forming may have a significant impact the reservoir quality of the Longmaxi Shale.
Study on Fracture Propagation Simulation With the Integration of Fully-Coupled Geomechanical and DFN Modeling
Authors: Fan Qinghu , Qu Gang , Xu Chuanyou , Zhang Chengbin , Wang Lixin , Bian Tengfei , Wu Xin , Zeng Congliang , Jia Fei , He Jiajun , Xu Hanbing , Huang Xingning
Published: January 2023

Junggar Basin is a typical superimposed oil and gas bearing basin in western China, where Mahu Depression is the largest oil-gas accumulation zone and exploration area. On April 23rd, 2013, the peak daily oil production of Mahu No.1 well reached 52 tons, achieving a significant breakthrough. On August 23rd of the same year, the high yield of Mahu No.18 well opened a new chapter of oil and gas exploration in the slope belt of the Mahu Depression. Since then, CNPC (China National Petroleum Corporation) has increased the exploration and development process of the Mahu Depression, establishing the exploration theory and technology system of the conglomerate reservoir in the depression area. Finally, the exploration and discovery of a supergiant conglomerate oil field (1 billion tons) were achieved. However, a series of development problems were exposed, which was due to the lack of in-depth research on reservoir geomechanics and distribution law for natural fractures: 1) The Hydraulic fracture propagation law is not clear; 2) In the process of fracturing, there exists an obvious phenomenon of non-uniform fluid inflow and expansion in various clusters of fractures; 3) Serious interference occurs between wells during the fracturing operation. Therefore, it is necessary to strengthen the study of geomechanics and distribution law for natural fractures in unconventional oil reservoirs, so as to provide technical support for the optimization of the horizontal well fracturing scheme, improvement of well pattern deployment parameters, and adjustment of oil reservoir development scheme, which further leads to cost reduction and development efficiency improvement while increasing single well production in unconventional oil reservoirs.
Application of Machine Learning in a Giant Mature Reservoir to Speed-Up Infill Prospects Screening, Optimize Field Development and Improve the Ultimate Recovery Factor
Authors: Fabbri Clement , Reddicharla Nagaraju , Shi Wen , Al Shalabi Alaa , Al Hashmi Sara , Al Jaberi Sulaiman
Published: January 2023

In giant reservoirs, production sustainability strongly depends on the identification of opportunities for infill drilling. This paper presents the use of Machine Learning to speed-up and improve the efficiency of the evaluation of future infill wells, in an effort to optimize field development of a Giant Mature reservoir Onshore Abu Dhabi.

In the mature giant carbonate reservoir studied, more than 420 wells are already drilled with consistent spacing but with varying orientations. This paper illustrates some examples of settings that are difficult to assess without geometric calculations, leading to time-consuming opportunity identification and classification.

The minimum set of input for the program includes existing wells trajectories, faults polygons, contact, and production data. Users can define the minimum drainage area for each well, maturity criteria and drain length. For each subsurface target identified, a polygon and simulation input are generated. The Python program is developed and run on an in-house platform and solve the future wells positioning in three main steps: (1) Geometric screening and identification of locations with required spacing, (2) Analysis of nearby well performance, (3) automatic generation of simulation input for evaluation of the subsurface target.