bluuebunny/crossref_metadata_2025 huggingface dataset:
Increasing Recovery and Lowering CO2 Intensity Through Use of Autonomous Inflow Control – A Case Study from the Norwegian Continental Shelf
Authors: Halvorsen Martin , Martiniussen Ingvild Berg , Nilsen Knut Arild , Hånes Margrete , Panini Filippo , Amundsen Lene , Elseth Geir
Published: September 2024

The aim of this paper is to demonstrate how the adoption of autonomous inflow control (AICD) can lower water production and CO2 intensity in a field situated on the Norwegian continental shelf. The study outlines the process starting from device qualification and testing in a flow loop, followed by evaluation through modelling and reservoir simulations, then well planning to optimize AICD implementation, and finally the measurement of the effect in several wells.

Breidablikk is an oilfield located on the Norwegian continental shelf that commenced oil production in October 2023. The oil reserves are at a depth of about 1750 meters, forming a thin layer that sits atop a strong water aquifer. Prior to production, a range of AICD solutions aimed at water reduction were evaluated, including qualification, and testing of the devices in a flow loop to obtain their flow performance data. This data was subsequently used as input in multiple reservoir simulations to determine the optimal distribution of AICDs in the wells. The wells are closely monitored to confirm that the expected AICD behavior has been achieved.

The Breidablikk wells consist of long horizontal sections, both single and multilaterals, with lengths of up to 1600 meters. They are equipped with screens, various inflow control devices, blank sections, and swell packers. Following implementation and production start-up, measured flow rates and pressures are compared against the expected AICD performance. The lower completion design is optimized using a steady-state well simulator and the use of a dynamic reservoir simulator to provide expected production profiles. During the stepwise clean-up of the wells and branches, differential pressure and oil rates are used to measure well clean-up and AICD efficiency. The pressure drop across the AICDs is determined based on down hole pressure, plotted versus initial well test rates, and is consistent with laboratory tests under similar conditions. Chemical tracers have also been used to verify clean-up during flow performance evaluation. Based on the successful implementation, there is a good basis for planning and optimizing use of AICDs in future wells at Breidablikk.

The reservoir drainage strategy for Breidablikk depends upon implementing artificial lift and utilizing the well designs mentioned earlier. Production of the wells at minimal gas lift rates, in combination with on/off well production, allows for higher NPV and recovery to be attained throughout the anticipated field lifetime. To ensure a successful implementation, step rate tests are performed to affirm the AICD flow characteristics determined through lab testing and simulations.
Towards Robust and Automated Contamination Estimation During Downhole Fluid Sampling
Authors: Berkane M. , Kristensen M. , Ali A. M. , Al-Hamad M. , Albuainain F.
Published: September 2024

Accurate, robust, and real-time quantification of oil-based mud (OBM) filtrate contamination using data from downhole fluid analysis (DFA) sensors remains a challenging problem, especially under difficult sampling conditions and for advanced sampling tools with complex inflow geometries and active guarding of filtrate flows. Such tools and conditions lead to contamination cleanup behaviors that do not necessarily follow the simple power-law models that are commonly used in contamination monitoring algorithms. Moreover, the need for improved efficiency calls for more automation in sampling operations, especially in high-activity regions with many wells and sampling stations. Achieving such automation within existing contamination monitoring workflows has been proven to be challenging.

In this paper, we deploy a new method for contamination monitoring, based on the inversion of DFA data using a full 3D numerical flow model of the contamination cleanup process. This flow model predicts the evolution of filtrate contamination as a function of time and pumped volume and can thus be used to forward-model the DFA sensor responses. Sensor data, such as optical density, mass density, and gas-oil ratio, are then inverted in real time to provide contamination predictions. Real-time computation is enabled through fast, high-fidelity proxy models for the cleanup process. The proxy models are trained on and validated against a library of 300,000 precomputed full-scale numerical simulations covering a wide range of hardware configurations and sampling environments.

We applied the new method on 14 field sampling stations of oil sampling in OBM conditions, with four different types of sampling inlets: single probe, radial probe, dual packer, and focused radial probe. The data sets also included challenging stations with cleanup conducted over multiple 4-hour sessions with possible reinvasion of mud between sessions. We found that contamination predictions with the new method are in good agreement with results from laboratory analysis.

Compared to the previous algorithms, the new contamination monitoring algorithm is applicable for all types of sampling hardware, a wider set of operating conditions, and over the full history of a sampling station (i.e., from formation fluid breakthrough to a clean sample). This latter attribute enables robust early-time predictions of the pumping time remaining to reach a clean sample. Finally, the new method is independent from manual user intervention and thus lends itself well to workflow automation.
Comparing Open-Source Drill String Models: Establishing Test Cases and Methods
Authors: Yoon D. , Hamidov E. , Endres L. , Gildin E. , Pastusek P. , Shor R. , Li X.
Published: September 2024

This paper presents a foundational guide for comparing drilling dynamics models, emphasizing the establishment of methodologies rather than just results. We assess two open-source soft-string dynamic models: the distributed, torsional model by Aarsnes and Shor, and the lumped-mass, coupled axial-torsional model by Dixit et al. Both models are evaluated through six progressively complex test cases designed to enhance methodology development for future model comparisons. Despite their different assumptions and mathematical foundations, both models demonstrated similar responses in drill string vibrations and stick-slip behaviors, with minor differences attributed to the use of distinct friction models (Coulomb versus Stribeck). The study also illustrates the benefits of open-source software in drilling dynamics, such as ease of modification and problem-solving speed, highlighting its potential for improving productivity and insights when combined with collaborative efforts from various industries and academic entities. This work lays the groundwork for further research and robust comparisons of drilling dynamics models, enhancing confidence in both open and closed-source models.
Physics Augmented Machine Learning Models for Determining Gas Solubility in Formation Brines for CCS and Gas Processing Applications
Authors: Ratnakar R. R. , Chaubey V. , Gupta S. S. , Hackbarth J. , Rui Z. , Dindoruk B.
Published: September 2024

Gas solubility in brine is crucial input for engineering design of various chemical/petroleum processes such as oil recovery, CO2 sequestration in saline aquifers and water bearing formations, separation and utilization, corrosion in wellbores/facility/pipelines with acidic gases, and gas processing. However, with limited experimental data, intrinsic trends of gas solubility under varying conditions (pressure/ temperature/brine composition) are difficult to obtain. This study uses machine-learning-based approach to explore those intrinsic trends while identifying the contributing elements correlated with fluid properties.

Gas solubility in brine depends on various thermodynamic properties of the components, pressure, temperature, and salinity. To explore effect of these factors, we employ various Machine learning (ML) tools: decision tree (DT), random forest (RF) and artificial neural network (ANN) techniques due to their stability and convergence characteristics coherent with the data utilized and develop a framework to determine solubility of a gas in brine solutions. While most existing literature are limited to very few salts (NaCl/KCl/CaCl2), this work captures combinations of various common salts (chlorides, carbonates/bicarbonates, and sulphates) as they are seen in real formation brines and water utilities.

The prediction from ML models were validated against the available experimental data that were not used in training. The main results are as follows:

Validation processes indicated that ML models predict the experimental trends accurately, within the relative error of 1% for gas-water systems and 3% for complex gas-brine systems. Various input features based on the thermodynamic and physical properties of gases and ions (cations and anions) were considered and main contributing features were identified. Most importantly, the framework is general, fast, convenient and can easily be extended for different gas species including greenhouse or hydrocarbon gases, as well as for variety of salts. Additionally, it can fill the gaps in experimental data for the gas-brine systems, and extrapolate to elevated pressure and temperature conditions.

While ML-based approach to estimate gas solubility in brines have been developed in the literature, they are very restrictive in terms of their broadness/applicability and valid for specific gases such as CO2 as well as few salts (NaCl/KCl/CaCl2). Here, the ML-based framework covers a wide range of salts and gases, and additionally, the current framework can easily be extended to other systems.
Phase Behavior of Hydrocarbons in Shale Nanomatrix-Fracture System: Experiment and Simulation
Authors: Dong Xiaohu , Xiao Zhan , Guo Ziyang , Liu Huiqing , Lyu Xiaocong , Chen Zhangxin
Published: September 2024

Currently, the methods of nanofluidic chip and molecular dynamics simulation have been widely applied to characterize the effect of nanoscale confinement on the fluid phase behavior in shale rocks. However, most of the published literatures just concentrate on the pure nanoscale pores. Actually, in shale rocks, the phase transition phenomenon of fluids is happened in a nanomatrix–fracture system, which highly differs from the pure nanoscale pores.

In this work, we combine the methods of experiment and simulation to address the phase behavior of hydrocarbons in a nanomatrix–fracture system, which can effectively represent the actual pore space of fluids in shale. A new experimental device for the fluid phase behavior is firstly developed in this study, which is based on the conventional PVT test equipment. But for this newly-proposed device, the test cell is separated into two connected spaces. During experiment, one of them is filled with nanoporous material to represent the shale nano-matrix, and the other one is used to simulate the fracture system. Then, by using this device, through a step-wise reduction on the test cell volume, the bubble point pressure of a hydrocarbon mixture (C1/C8) is tested. The applied nanoporous materials in this study include MCM-41 (pore size: 4 nm) and SBA-15 (pore size: 2.5 nm). Through a comparison, the effect of nanopore size is analyze. Thereafter, the obtained experimental data are compared against the simulation results of our previous proposed mathematical model to discuss the effect of fracture system. Simultaneously, a set of Grand Canonical Monte Carlo (GCMC) simulation runs are also performed for the microscopic mechanisms for the nanoconfinement effect on fluid phase behavior.

The obtained bubble point pressures of C1/C8 mixture in the SBA-15 and MCM-41 porous systems are 4.65 MPa and 4.80 MPa respectively. They are lower than the that of the pure bulk fluids (5.07 MPa). It can be found that with the nanopore size reduces, the deviation is obviously increased. Then, the experimental data is compared with the calculation results of our mathematical model (4.22 MPa and 4.37 MPa), it is found that without the consideration of fracture system, the bubble point pressure of hydrocarbons can be underestimated. Furthermore, based on the GCMC simulation results, it is found that the wettability characteristics of shale rock can significantly impact the phase behavior of hydrocarbons, while the pore size distribution in shale typically controls fluid phase transitions during production. This study provides a novel experimental method to characterize the fluid phase behavior in nanoporous shale rocks. Some important new insights are obtained to understand the complicated phase transition phenomenon in shale reservoirs.
Wellbore Stability Uncertainty Quantification Using a Bayesian Machine Learning Framework
Authors: Ray Peng L. , Tipton John R. , Cheng S. , Francom Devin
Published: September 2024

One of the major challenges in wellbore stability analysis is to quantify the uncertainty of the mud weight window and provide probabilities of breakouts and drilling induced tensile fractures for a given mud weight. Modeling the safe drilling window requires many geomechanics inputs, such as: rock strength, elastic properties, in-situ stresses, pore pressure, etc. Most of these inputs are calculated using empirical correlations based on limited test data; or calculated using simple elastic equations and calibrated with limited data points. Uncertainties in test and calibration data combined with uncertainties in correlations make it exceedingly difficult to correctly assess the uncertainty of the resulting safe drilling window. An accurate quantification of measurement and other input uncertainties and correct propagation of uncertainty throughout the modeling process can improve decision quality and wellbore instability related non-productive time and drilling costs.

Geomechanics modeling of a complex physical system requires a solid understanding of fundamental physics and domain expertise. However, current uncertainty methods often lack a mathematically rigorous framework that can integrate both physics and domain expertise into the modeling process.

To address the challenges mentioned above, a Bayesian machine learning framework has been adopted as the foundation of our physics-based uncertainty workflow, which allows to incorporate prior information from domain knowledge and fundamental physics, to integrate collected lab and field measurement data into the modeling process, and to improve the model using an iterative workflow.

The resulting workflow can account for the complex interrelationships and interactions between different components in the geomechanics system; specifically, the capability to model and calibrate the uncertainties for several essential components underlying the one-dimensional wellbore stability model. These components include geomechanical core tests and stress measurements (such as Leak-off Test (LOT), Extended Leak-off Test (XLOT), Diagnostic Fracture Injection Tests (DFIT), etc.), as well as drilling performance (such as mud weight used, appearance of breakout and drilling induced tensile fractures, etc.).

This workflow provides a systematic and consistent framework for evaluating uncertainty in building a 1D Mechanical Earth Model (1DMEM) which is a crucial step in ensuring a safe and efficient drilling process. We will demonstrate this uncertainty quantification workflow and its application for estimating the safe drilling windows with a case study.
Best Practices of Inline Tests and Deliverability Assessment in the Canadian Low Permeability Duvernay for Improved Design of Completion and Characterization of the Reservoir
Authors: Virues C. , Robertson A.
Published: September 2024

In March 2021, the Alberta Energy Regulator (AER) introduced a new directive on pressure and deliverability testing, providing updated guidelines for inline testing to match industry practices. The AER now receives from oil and gas operators flowback data from inline tests and absolute open flow (AOF) interpretations. After analyzing this data, the AER has developed best practices. Best practices resulting from this study are contrasted to published studies of the Duvernay Formation.

Currently, the AER cannot obtain fracture geometry and other fracture parameters. With the addition of pressure data, this study will aid the AER in obtaining fracture geometry, such as half-length, and fracture parameters, such as stimulated reservoir volume (SRV), permeability and productivity parameters. The new interpretation will aid and enhance reserves assessment and improve our understanding of inter-wellbore communication. New descriptions using mathematical (analytical/numerical) methods allow improved design of completions and enhanced characterization of the reservoir. This technical paper provides fundamental equations along with inline test and AOF interpretation techniques, which can be easily replicated by subject matter experts.

Eighty unconventional flowback submissions were analyzed and displayed for non-vertical and non-deviated wells in the Duvernay. Productivity indices were calculated using flowback information and the AOF-based C value-related index of productivity (PI). Fracture geometry, SRV permeability, and PI estimates all significantly affect characterization of the reservoir. Currently, the AER only uses production rates in its flagship report AEO. This study will enhance the Duvernay reserves evaluation by using rate transient analysis (RTA) along with pressure parameters, such as tubing/casing pressure. Results from the study will affect what industry could do not only in the Duvernay but also in similar low-permeability resource plays in the United States of America and elsewhere. Production and pressure data aids in the interpretation of flow regimes to determine the optimal separation between wells and designs of completions.

Flowback information has been used before. However, our work is novel in how it applies to the Duvernay Formation, which has been target of many companies in Alberta, and the implications on reserves evaluation, completion design, and characterization of the reservoir.
Embed-to-Control-Based Deep-Learning Surrogate for Robust Nonlinearly Constrained Life-Cycle Production Optimization: A Realistic Deepwater Application
Authors: Nguyen Quang Minh , Onur Mustafa , Alpak Faruk Omer
Published: September 2024

This paper presents a realistic deepwater application of a deep-learning-based reservoir surrogate model in forecasting reservoir performance (via the prediction of state variables and well outputs) and nonlinearly constrained production optimization under geological uncertainty by utilizing an efficient gradient-based method. The reservoir surrogate model is based on the multi-model Embed-to-control Observe (E2CO) architecture, consisting of four blocks of neural networks: encoder, transition, transition output, and decoder. In this work, the surrogate model is coupled with the powerful stochastic-gradient-based line-search sequential quadratic programming (LS-SQP) workflow to handle robust production optimization in the presence of nonlinear state constraints. Five realizations of the synthetic realistic Reservoir X model with different channelized heterogeneous permeability distributions permeability under waterflooding are used for showcasing the prediction and optimization performances of the deep-learning-based framework. The results from this framework are directly and quantitatively compared with the ones simulated using a commercial high-fidelity reservoir simulator.
Unconventional Field Testing with Clamp-on Multiphase Flowmeter
Authors: Black Michael , Heinks Carsten , Cramer Ron
Published: September 2024

Following on from a preliminary set of field testing performed at a selection of USA based unconventional wells longer term measurements with a clamp-on Electromagnetic Acoustic Transducer (EMAT) multiphase metering system shall be presented. Longer term trends and correlations between the well behavior and the meter data shall be discussed including opportunities for asset optimization. The clamp-on meter can be rapidly installed on the production flow lines of new wells or retrofitted to existing wells with zero intervention. The EMAT measurement approach is clamp-on and non-radioactive.

The workflow used to configure the meter prior to installation to the line shall be described. This includes empirical and physics-based flow analysis of reported well conditions based on pipe geometry, production data and PVT information. Following this, EMAT flowmeters were brought to multiple well sites and installed on producing flowlines downstream of the well heads where measurements were taken. Results obtained were compared against expected flow conditions over longer time periods than previously presented.

EMAT flowmeters were installed on several unconventional wells with an installation time less than one hour and with zero intervention. Multiphase flow measurement results from these unconventional wells shall be presented and compared expected well behaviors. The flexibility of the clamp-on deployment allows measurement across multiple wells within a field or reservoir. How this can be applied to real time production optimization and surveillance shall also be explained. In preparation for each well, simulations were performed using the Taitel and Dukler model to predict the flow regime. More detailed physics-based simulations were performed to understand the flow behavior of the individual phases. It is possible with the clamp-on meter to monitor the flow regime and quickly detect flow regime changes within the pipe through monitoring of diagnostic parameters: these shall be compared against the predicted behavior of each of the unconventional wells. Furthermore, the longer-term nature of the measurement allows insight into longer time constant behavior changes of wells and fields leading to further insight into optimization for production and assets.

The flexible deployment of the EMAT multiphase flow measurement device presents potential for significant well production and HS&E improvements in oilfield surveillance and production optimization. The longer term assessment of the wells compared to previous measurements with this technology shall allow further insight into longer time constant well- and field-related phenomena which must be understood to effectively optimize production of the well.
Graph-Level Feature Embedding with Spatial– Temporal GCN Method for Interconnected Well Production Forecasting
Authors: Xu Ziming , Leung Juliana Y.
Published: September 2024

Deep-learning models have been employed for production forecasting in oil and gas reservoirs, but they often assume that each well operates independently, neglecting the connectivity and dynamic interactions between wells. This simplification can significantly compromise prediction accuracy. Therefore, graph convolutional networks (GCNs) have been applied to incorporate data from neighbouring wells. However, existing spatial-temporal GCN (ST-GCN) methods are mainly used for autoregressive tasks and face limitations in predicting newly developed wells and fully utilizing temporal neighbour interactions. This study introduces an ST-graph- level feature embedding (ST-GFE) method that provides accurate production forecasting for newly developed wells. It enhances forecasting by aggregating the historical data from neighbouring wells into a single feature vector. This aggregated vector, merging local and contextual information, contains richer information about the studied region.

We evaluate ST-GFE using a dataset of 6,605 Montney shale gas wells, incorporating formation properties, fracture parameters, and production history. The ST-GFE is integrated with a non-autoregressive encoder-decoder structure to do production forecasting. The findings demonstrate that ST-GFE significantly improves prediction accuracy for newly developed wells compared to the purely temporal models, such as recurrent neural network (RNN)-based and Transformer models. ST-GFE adapts to production changes in adjacent wells, providing accurate predictions across various application scenarios, including shut-in and in-fill drilling activities. Additionally, while traditional GCNs require a full-batch training approach that leads to scalability issues, the ST-GFE model treats each well and its surrounding wells as a graph, enabling batch training and significantly reducing memory usage. Furthermore, the model dynamically updates its forecasts with real-time production data, enhancing precision and relevance. Experimental results confirm that ST-GFE effectively leverages spatio-temporal dynamics and interactions between adjacent wells, further improving production forecasting accuracy. This method enhances predictions and generalization capabilities for new developing locations, broadening its applicability to various drilling and production scenarios.
Quantitative Laboratory Assessment of Caprock Integrity for Geological Carbon and Hydrogen Storage
Authors: Nath F. , Cabezudo E. , Romero N. G.
Published: September 2024

Caprock integrity is a critical factor in geological carbon storage (GCS) and hydrogen storage (GHS), as it ensures containment assurance, environmental and safety considerations, storage efficiency, risk mitigation, long-term viability, public and regulatory confidence, economic considerations, and informed decision-making. Caprock can break mechanically by radial cracking, plastic deformation, inner and outrebounding, and channeling. Understanding these failure mechanisms is crucial, but traditional deformation tracking methods have limitations. To address this gap, this study utilizes three-dimensional digital image correlation (3D-DIC) to investigate the deformation and failure of caprock specimens from GCS and GHS, thereby ensuring their integrity.

Caprock samples were compressed diametrically using a precision 100 kN electromechanical load frame moving at 0.05 mm/min. There were a variety of minerals in the samples that were examined. During the diametrical compression testing, a 3D-DIC system was set up to monitor the samples without touching them at a rate of 10 frames per second. A black-and-white speckle pattern is applied to the specimen to monitor its deformation under stress. The 3D-DIC system is used to process, visualize, and analyze caprock sample damage under varied load conditions.

A load-strain relationship was established by integrating several factors: 1) DIC-generated horizontal, vertical, and shear strain maps, providing insight into the sequence of strain development and fracture occurrences attributed to pre-existing or natural fractures, anisotropy, heterogeneity, laminations, faults, cracks, or channeling. 2) The results demonstrated a 4-stage deformation evolution process: initial, linear elastic, elastic-plastic, and plastic damage. 3) Additionally, the study illustrated the failure process of caprock samples, yielding tension and compression within ±0.05%, shear within ±0.001%, and a tension-compression ratio ranging from 1% to 5%. 4) three failure patterns were observed in the tested caprock specimens, including central, deviated from the center, and mixed modes due to tension, compression, and shear.

Injecting H2 or CO2 into the formation can lead to increased pore pressure and caprock failure in geological carbon and hydrogen storage projects. Geomechanical risks such as caprock instability, fault slip, and formation deformation can occur throughout the project's lifespan. There is no perfect methodology that covers all the problems in caprock integrity research. This study focuses on predicting caprock deformation and failure using non-contact optical methods, with 3D-DIC being more effective in terms of test range, affordability, accuracy, and full-field monitoring. The image-based algorithm is better at understanding anisotropic and heterogeneous conditions, enhancing the inspection of caprock integrity in underground carbon and hydrogen storage projects.
Human Factors Ecosystem – How Human Performance and Process Safety Builds the Energy Transition and a Low-Carbon Future for O&G Industry
Authors: França J. E. M.
Published: September 2024

The transition to a low-carbon future in the O&G industry seeks a comprehensive understanding of the Human Factors Ecosystem, particularly in relation to human performance and process safety. This paper explores the critical role that human factors play in fostering an energy transition, highlighting how optimized human performance and enhanced process safety can significantly contribute to the industry's sustainability goals. The study delves into the interplay between human behavior, organizational culture, and technological advancements, emphasizing the need for a systemic approach to manage the inherent complexities and risks of high-stakes environments. By examining case studies and applying methodologies such as the Functional Resonance Analysis Method (FRAM), the paper illustrates how a systemic and integrative Human Factors approach can lead to more resilient and adaptable operations. The findings underscore the importance of integrating different disciplines into the strategic planning and operational frameworks of O&G companies to drive efficiency, safety, and environmental stewardship. This research provides valuable insights for industry leaders, policymakers, and safety professionals aiming to align human performance with the overarching goals of the energy transition, adopting practical cases studies of accident analysis, human reliability and risk analysis with FRAM.
Rethinking Surfactant Huff-n-Puff for Enhanced Oil Recovery in the Bakken: Recent Pilot Field Study Utilizing Multifunctional Biosurfactants
Authors: Pearl M. , Nelson E. , Rothe J. , Black K. , Muus P. , Dittus A. , Shumway M. , Kakadjian E. , Au Yong H.
Published: September 2024

Since November 2019, the Bakken shale play has experienced a decline of 6% annually to the current production of 1,100 Mbbl/d. With decline rates as high as 85% over the first three years of production and a primary recovery rate of 3-10% of original oil in place, finding ways to recover more oil using EOR techniques in this maturing unconventional play is key. In recent years, huff-n-puff EOR techniques were typically regarded as the most cost- effective methods for unconventional plays. However, novel biotechnology, proven in multiple US Basins and now expanded to Bakken for the first time in 2021, has shown very promising results. This paper will provide details of a pilot study conducted on multiple wells, showcasing the potential of this innovative approach.

The treatment fluid, a biosurfactant-based solution, was successfully applied in the field by pumping an aqueous solution through the tubing-casing annulus of each rod-pumped well. Treatment volumes were pumped between 13- 15 bbl/minute with a top pressure of 1,400 psi observed. Post-application produced fluid samples were regularly collected and analyzed to monitor the residual impact of biosurfactants, reassuring us of the effectiveness of this method.

The baseline production for each initial test well was approximately 20 bbl of oil, 20 Mcf of gas, and less than 10 bbl of water per day. Following the treatments, the wells were returned to production after a shut-in period of less than 8 days. Upon return to production, each test well achieved peak oil production of more than 70 barrels per day after just 5 days of run-time, with peak water production of 110 barrels per day achieved after 2 days of run-time. After 125 days of run-time, the average oil production per well was maintained at greater than 32 barrels per day, a 70%+ increase over forecast oil volumes. To date, the total oil recovered above pre-treatment forecasts has been greater than 1,700 bbl for each well, and the total water recovered above forecast was less than 20% of the treatment volume for each well. The pilot study has demonstrated that this new, sustainable technology based on multifunctional biosurfactants provided a significant and impressive production boost, instilling optimism for the future of enhanced oil recovery in the Bakken shale play.
Nonlinearly Constrained Life-Cycle Production Optimization Under Geological Uncertainty: A Realistic Application
Authors: Toktas Omer Lutfu , Nguyen Quang Minh , Onur Mustafa , Alpak Faruk Omer
Published: September 2024

The objective of this study is to present a realistic application of our previously developed nonlinearly constrained stochastic gradient-based optimization framework that can efficiently solve nonlinearly constrained robust production optimization problems. We apply the optimization framework to a synthetic, yet realistic, field optimization problem using multiple history-matched realizations of the geologic model. For the synthetic realistic field example, the reservoir is assumed to undergo production for several years. Well controls (BHPs for producers and water injection rates for injectors) on a sequence of control steps (time intervals) are optimized during the forecast period by maximizing the expected value of the net present value (or ensemble average NPV) over multiple realizations of subsurface description for the subsequent 10 years of production under a waterflooding scenario subject to nonlinear constraints. The optimization framework is based on our in-house nonlinear optimizer employing sequential quadratic programming (SQP) coupled with stochastic simplex approximated gradients (StoSAG). It incorporates the line-search procedure within SQP and is referred to as line-search sequential quadratic programming (LS-SQP). We couple LS-SQP with two different constraint handling schemes; the expected value constraint scheme and minimum-maximum (min-max) constraint scheme, to avoid the explicit application of nonlinear constraints for each reservoir model. Both bound constraints on well controls and nonlinear constraints on field liquid production rate, field water injection rate, and/or individual producer water production rates are considered. We consider several different history-matched geological realizations of the realistic field example with several injectors and producers. A black-oil commercial reservoir simulator is utilized in the optimization workflow to assess the objective function with imposed constraints. Results show that the framework LS-SQP algorithm with StoSAG can effectively handle the nonlinear constraints in a robust life-cycle production optimization problem. Moreover, our results show that the LS-SQP framework with any of the two different constraint handling schemes considered effectively handles the nonlinear constraints in a life-cycle robust production optimization problem. However, the expected value constraint scheme results in higher optimal NPV than the min-max constraint scheme, but at the cost of possible constraint violation for some individual geological realizations. Application results demonstrate that our nonlinearly constrained stochastic gradient-based robust optimization framework has great potential to be a rapid approximate capability for performing life cycle production optimization under subsurface uncertainty with bound and nonlinear constraints.
Realtime Caving Prediction for Drillstring Packoff Simulation Using Data-Driven Model Coupled with Geomechanics
Authors: Liu Zhengchun , Zhang Yuan , Samuel Robello
Published: September 2024

During well construction operations, when the drill cuttings are not transported efficiently out of the well, or if parts of the wellbore collapse on the drillstring because of an unstable formation, the well can begin to pack off. There are a lot of models to estimate the volume of drilling cuttings. But few literatures can be found for volume estimation of cavings from stress-induced wellbore damage.

Caving volume is predicted using a geomechanical caving model. Assuming the caving debris has the shape of a prism, caving volume can be estimated as a function of wellbore radius, caving depth, caving length, and the breakout angle. Breakout angle is estimated as a function of rock strength, geostress and wellbore pressure. The effects of wellbore inclination and azimuth are also taken into account for deviated wells. Caving depth is typically obtained from the caliper log, which is usually not available during drilling operations. Therefore, hybrid engineering and data-driven approach are employed to predict the caving depth.

Different data-driven models (e.g., Random Forest) were developed and tested for predicting caving depth h, assuming it is a function of rock geomechanical properties, geo-stresses, pore pressure, wellbore pressure, and gamma ray log. Caliper data log data of multiple wells are used to train and cross-validate those data-driven models. Cross-validation results indicate that three data-driven models including random forest, neural network, and Adaboost are performing very well.

The simulation method can be applied for real-time monitoring of pack-off risk during drilling operations. In the workflow, real-time survey data (measured depth, inclination, azimuth) and rig-state data, including measured depth (MD), gamma ray (GR), flow rate, rotational speed (RPM), rate of penetration (ROP), weight on bit (WOB), etc., are continuously received by the computer and buffered in caches. These buffered data are then consumed by the geomechanical caving width model and the data-driven caving depth model. As a result, real-time caving volume is calculated at every depth of interest.

Combing both real-time cuttings data and real-time caving data, a real-time packoff volume percent can be obtained and displayed in a continuously updating plot. The approach is based on synthesis of an engineering model and a data-driven model.
2023 SPE Diversity and Inclusion Survey: Driving Equality in the Energy Industry
Authors: Carreras P. E. , Guiñazu M. L.
Published: September 2024

An SPEi survey was launched in May 2023 to determine the status of Diversity and Inclusion (D&I) in the energy industry from the workforce perspective. Besides, survey results were compared with the results of a similar survey launched in 2020. The survey consisted of seven demographics, fifteen multiple-choice and five open-ended questions overall similar to the questions from the 2020 survey. A few additions aimed to capture changes in the workplace due to the pandemic and the ongoing energy transition. The proposal was reviewed and approved by the SPE D&I committee. SPE Marketing Research Group submitted the survey to SPE members and non-members located worldwide. The survey was also promoted on social media to increase its reach. The responses were analyzed with the help of charts and tables that summarized key findings. More than 900 responses were received from all over the world. 74% were men, 21% women, 1% non-binary and 4% preferred not to answer the gender question. Most of the participants were from North America (58%) and Western Europe (12%), followed by Asia (8%), South America (8%) and Africa (7%). 96% of the respondents held college degrees. Their ages were 26-35 years old (12%), 36-45 years old (20%), 46-55 years old (17%), 56-65 years old (21%) and older than 66 years old (23%). 58% worked for upstream, 14% for oil field services and equipment and 13% were consultants. Most professionals have positive perceptions about the industry's benefits, work environment, and contributions to the energy transition, top conditions to recruit and retain younger generations. Most respondents prefer to work virtually or part time from home. Women consider that they face gender biases at work and that their careers are limited by negativity or discrimination by management. While some participants express skepticism or opposition to D&I initiatives, the majority emphasize the importance of creating an inclusive work environment. Hiring, assignments and promotions were supported based on merit regardless of differences or merit without using quotas to benefit minorities. The understanding of women and men about important D&I topics differs. For example, majority of women perceive that, even today, they are not treated equally at work, while majority of men perceive that everyone is treated equally. D&I has matured to include not only aspects of identity (gender, race) but also aspects of the value and benefits obtained by promoting diversity of thoughts, making merit-based decisions, challenging biases, and fostering equal opportunities for all employees. The different opinions that women and men have expressed in the survey illustrate how organizations can be nurtured by the presence of diverse individuals that bring their unique perspectives and contributions for long-term success.
Gas Lift Anomaly Detection in Unconventional Fields Using Expert System Techniques
Authors: Zejli A. , Shrestha A. , Cormier B.
Published: September 2024

Production engineers managing unconventional gas lifted wells face ever-changing operational conditions with newly drilled wells coming online, on-going well workover, facility upsets and artificial lift configuration changes. Optimizing gaslift operations involves delivering optimal gaslift injection to wells while respecting operational constraints but also detecting misbehaving wells early to mitigate underperformance. Detecting anomalous behavior in gaslift wells is onerous due to the dynamic nature of the operations and the deluge of field measurements that need to be analyzed.

This paper will cover a novel approach to detecting anomalous behavior, suboptimal production, and integrity issues in gaslift wells in unconventional fields. The approach relies on using expert system techniques in conjunction with physics-based models to detect these conditions and notify engineers to take remedial action. The paper will also cover how this novel approach was built into a scalable and extensible solution called the Gas Lift Anomaly Detection (GLAD) tool.
Preliminary Research on Applications of Large Language Models in E&P Industry
Authors: Zhou X. G. , Jing X. W. , Wu W. K. , Gao G. Z.
Published: September 2024

The objective of this paper is to develop EPChat, an industry-specific large language model tailored for the exploration and production (E&P) field through retrieval-augmented generation (RAG) and fine-tuning techniques. This novel EPChat model effectively mitigates the common issue of hallucinations inherent in generic LLMs, thereby enabling it to function as a reliable AI assistant in the oil and gas industry, providing accurate and contextually relevant responses.
Real Time Application of Deep Learning Based Flare Smoke Detection
Authors: Gupta Kratika , Chatterjee Purba , Chen Xianzhong John , Kadam Jitendra
Published: September 2024

This work focuses on developing a smoke detection model that can be employed in an automated, end-to-end flare smoke detection, alerting and distributed control solution that leverages existing flare Closed Circuit Television (CCTV) cameras used at manufacturing facilities. At the core of this solution is a deep-learning computer vision model that is developed leveraging an extensive and diverse data set and the model has demonstrated fit-for-use performance in wide-ranging conditions. The smoke detection model uses a novel approach for real time detection of hydrocarbon emission leveraging semantic segmentation, through compact vision transformers. This innovative approach enables real-time identification of smoke in flare images, which is a key control signal for an efficient closed-loop system.

The training dataset includes a rich diversity of flare images (collected from global locations and environment conditions), including steam images with blank smoke masks to mitigate false positives. Additionally, images with minimal smoke were included to enhance the model's sensitivity to light smoke detection. External testing was conducted on previously unseen datasets, ensuring the model's robustness and generalizability. The developed model performs robust smoke detection with high precision alerting and minimizing false positives from steam images. This application has been deployed in the plant within an end-to-end solution, creating a positive impact on operations.
Enhancing Cement Integrity in CO2 Sequestration Wells - Advanced Strategies and Implications for Environmental Regulatory Standards
Authors: Meng M. , Zhou L. , Baldino S. , Liu Y. , Mehana M. , Chen B. , Frash L. , Carey J. William
Published: September 2024

Geological carbon sequestration requires injecting CO2 into the deep formation through wellbores with an injection rate as high as ~1 Mt/year. This process can lead to substantial temperature drops near the wellbore, potentially causing the cement debonding from the casing or formation, resulting in severe leakage. In this paper, we first analyzed the transient wellbore temperature and pressure profile across the well's depth using both an analytical model and one dynamic multiphase flow simulator, with cross validation. We then adapted these results for the subsequent well integrity analysis using a fully coupled thermoporoelastic model with transient solutions. Our analysis shows that the cooling effect depends on injection rate, surface CO2 temperature, and reservoir pressure. For a simulated Class VI well, a combination of high injection rate, low surface CO2 temperature, and depleted reservoir could result in the wellbore temperature that close to the bottom dropping by 60 ℃. Similar significant cooling scenarios have been observed at the Aquistore field located in Canada, the Alwyn field, and the Goldeneye field in the U.K. The maximum allowable cooling temperature for a typical cement formulation with 10 GPa elastic modulus, and 0.15 Poisson's ratio is 40 ℃. Therefore, this cooling effect could compromise wellbore integrity and cause well leakage through cement-casing debonding. Multiple practical strategies have been found to enhance cement integrity during CO2 injection, including modifying cement formulation to be more ductile and resilient, enhancing the initial state of stress in the cement using a pre-stressing method or expansive additive, and adding an insulated coating layer to the tubing. Pre-heating CO2 at the surface is effective but can be expensive and impractical. Using a protective annulus fluid with a lower thermal conductivity and a new tubing material with low thermal conductivity have been found to be ineffective. Overall, our comprehensive analysis enables us to assess the long-term impacts of CO2 injection on well integrity and promote sustainable and effective geological carbon sequestrations with proper environmental protection protocols.