Oil production decline and excessive water production are prevalent in mature fields and unconventional plays, which significantly impact the profitability of the wells and result in costly water treatment and disposal. To seek for a sustainable development of those wells, reducing the operation cost and extending their economic lives, this paper presents a method of synergistic production of hydrocarbon and electricity, which could harvest the unexploited geothermal energy from the produced water and transfer heat to electricity in the wellbore. Such method is cost-effective, since it does not require any surface power plant facility, and it is replicable in numerous wells including both vertical wells and horizontal wells. By simultaneous coproduction of oil and electricity, the value of existing assets could be fully developed, operation cost could be offset, and the economic life of the well could be extended.
This recently proposed method incorporated thermoelectric power generation technology and oil production. In this method, electricity could be produced by thermoelectric generator (TEG) mounted outside of the tubing wall under temperature gradient created by produced fluid and injected fluids. The aim of this paper is to illustrate the economic practicability of oil-electricity coproduction by using thermoelectric technology in oil wells based on previously proposed design. We examined the technical data of high water-cut oil wells in North Dakota and collected required information with respect to performance thermoelectric power generations. Special emphasis was placed on the key parameters related to project economics, such as thermoelectric material, length of TEG and injection rate. Sensitive studies were carried out to characterize the impact of the key parameters on project profits. We showed that by simultaneously production of oil and electricity, $234,480 of additional value could be generated without interfering with oil production.
The proposed method capitalizes on the unexploited value of produced water and generates additional benefits. This study could provide a workflow for oil and gas operators to evaluate an oil-electricity coproduction project and could act as a guidance to perform and commercialize such project to balance parts of the operation cost and extend the life of the existing assets.
Temizel, Cenk (Aera Energy) | Balaji, Karthik (University of North Dakota) | Canbaz, Celal Hakan (Ege University) | Palabiyik, Yildiray (Istanbul Technical University) | Moreno, Raul (Smart Recovery) | Rabiei, Minou (University of North Dakota) | Zhou, Zifu (University of North Dakota) | Ranjith, Rahul (Far Technologies)
Due to complex characteristics of shale reservoirs, data-driven techniques offer fast and practical solutions in optimization and better management of shale assets. Developments in data-driven techniques enable robust analysis of not only the primary depletion mechanisms, but also the enhanced oil recovery in unconventionals such as natural gas injection. This study provides a comprehensive background on application of data-driven methods in oil and gas industry, the process, methodology and learnings along with examples of data-driven analysis of natural gas injection in shale oil reservoirs through the use of publicly-available data.
Data is obtained and organized. Patterns in production data are analyzed using data-driven methods to understand key parameters in the recovery process as well as the optimum operational strategies to improve recovery. The complete process is illustrated step-by-step for clarity and to serve as a practical guide for readers. This study also provides information on what other alternative physics-based evaluation methods will be able to offer in the current conditions of data availability and the understanding of physics of recovery in shale oil assets together with the comparison of outcomes of those methods with respect to the data-driven methods. Thereby, a thorough comparison of physics-based and data-driven methods, their advantages, drawbacks and challenges are provided.
It has been observed that data organization and filtering takes significant time before application of the actual data-driven method, yet data-driven methods serve as a practical solution in fields that are mature enough to bear data for analysis as long as the methodology is carefully applied. The advantages, challenges and associated risks of using data-driven methods are also included. The results of comparison between physics-based methods and data-driven methods illustrate the advantages and disadvantages of each method while providing the differences in evaluation and outcome along with a guideline for when to use what kind of strategy and evaluation in an asset.
A comprehensive understanding of the interactions between key components of the formation and the way various elements of an EOR process impact these interactions, is of paramount importance. Among the few existing studies on natural gas injection in shale oil with the use of data-driven methods in oil and gas industry include a comparative approach including the physics-based methods but lack the interrelationship between physics-based and data-driven methods as a complementary and a competitor within the era of rise of unconventionals. This study closes the gap and serves as an up-to-date reference for industry professionals.
Fiallos Torres, Mauricio Xavier (The University of Texas at Austin) | Yu, Wei (The University of Texas at Austin) | Ganjdanesh, Reza (The University of Texas at Austin) | Kerr, Erich (EP Energy) | Sepehrnoori, Kamy (The University of Texas at Austin) | Miao, Jijun (SimTech LLC) | Ambrose, Raymond (EP Energy)
Optimizing spacing of infill wells and fractures can lead to large rewards for shale field operators, and these considerations have influences on primary and tertiary development of the field. Although several studies have been employed to show the existence of well interference, few models have also implemented Huff-n-Puff and injection containment methods to optimize further hydraulic fracture designs and pressure containment to improve the efficiency of Enhanced Oil Recovery (EOR). This study has performed a rigorous workflow for estimating the impacts of spatial variations in fracture conductivity and complexity on fracture geometries of interwell interference. Furthermore, we applied a non-intrusive embedded discrete fracture model (EDFM) method in conjunction with a commercial compositional reservoir simulator to investigate the impact of well interference through connecting fractures by multi-well history matching to propose profitable opportunities for Huff-n-Puff application. First, based on a robust understanding of fracture properties, updated production data and multi-pad wellbore image logging data from Eagle Ford, the model was constructed to perform nine wells sector model history matching. Later, inter-well connecting fractures were employed for enhanced history matching where results varied significantly from unmeasured fracture sensitivities. The result is the implementation of Huff-n-Puff models that capture inter-well interference seen in the field and their affordable impact sensitivities focused on variable injection rates/locations and multi-point water injection to mimic pressure barriers. The simulation results strengthened the understanding of modeling complex fracture geometries with robust history matching and support the need to incorporate containment strategies. Moreover, the simulation outcomes show that well interference is present and reduces effectiveness of the fracture hits when connecting natural fractures. As a result of the inter-well long fractures, the bottom hole pressure behavior of the parent wells tends to equalize, and the pressure does not recover fast enough. Furthermore, the EDFM application is strongly supported by complex fracture propagation interpretation and ductility to be represented in the reservoir. Through this study, multiple containment scenarios were proposed to contain the pressure in the area of interest.
The model has become a valuable template to inform the impacts on well location and spacing, completion design, initial huff-n-puff decisions, subsequent containment strategies (e.g. to improve cycle timing and efficiency), and to expand to other areas of the field. The simulation results and understandings afforded have been applied to the field satisfactorily to support pressure containment benefits that lead to increased pressure build, reduced gas communication, reduced offset shut-in volumes, and ultimately, improvements in net utilization and capital efficiency.
Plunger lifted, and free-flowing gas wells experience a wide range of issues and operational inefficiencies such as liquid-loading, downhole and surface restrictions, stuck or leaking motor control valves, and metering issues. These issues can lead to extended downtime, equipment failures, and other production inefficiencies. Using data science and machine-learning algorithms, a self-adjusting anomaly detection model considers all sensor data, including the associated statistical behavior and correlations, to parse any underlying issues and anomalies and classifies the potential cause(s). This paper presents the result of a Proof of Concept (PoC) study conducted for a South Texas operator encompassing 50 wells over a three-month period. The results indicate an improvement compared to the operators' visual inspection and surveillance anomaly detection system. The model allows operators to focus their time on solving problems instead of discovering them. This novel approach to anomaly detection improves workflow efficiencies, decreases lease operating expenses (LOE), and increases production by reducing downtime.
Traditionally, petroleum exploration and development teams have utilized workflows and software which require single instance installation and cater to domain-specific needs. Design results from one domain would require incorporation into applications of other associated domains to deliver team-wide engineering. This is often time consuming, requiring multiple review meetings and extra administrative effort for the drilling engineer.
To add to the complexity, whenever iterations or sensitivity evaluations are needed across the entire plan, there is often no simple platform within which all the required processes can be managed, requiring engineering evaluations to be executed across multiple software. An example is hydraulics which is required for mud design, bottom hole assembly (BHA) and bit design, hole cleaning and borehole stability aspects of drilling. Although all these engineering considerations evaluate the same fluid properties, they typically sit on separate engines and are only integrated by criteria and thresholds in the final plan and not through concurrent engineering design.
This paper presents a new cloud deployed well construction planning solution, that aims to resolve these historical challenges by enabling multiple processes to be connected and executed from a common contextual dataset in a single system. For example, the hydraulics design is coherent across all design tasks which increases planning efficiency and plan quality. The entire solution also integrates across domains, from geology and geomechanics to drilling engineering and service company planning. This coupled with project orchestration, team collaboration and data management provide further productivity gains and cost savings for the entire team.
This paper summarizes the digital well construction planning solution and provides case study examples of how cross domain experts plan concurrently in a single common system. This approach allows a teamwide focus on planning better wells faster in a single engineering solution. Case studies show how the well planning team was able to improve cross-discipline collaboration between engineering and geoscience as well as interactions with service companies. Overall, the well planning time was reduced significantly, and the reliability of the well design was ensured through the engineering validation of each task. The integrated digital well planning solution proved to be a more cost-effective solution for well planning and ensured the high-quality delivery of drilling programs.
Rosenhagen, Nicolas M. (Colorado School of Mines) | Nash, Steven D. (Anadarko Petroleum Corporation) | Dobbs, Walter C. (Anadarko Petroleum Corporation) | Tanner, Kevin V. (Anadarko Petroleum Corporation)
The volume of stimulation fluid injected during hydraulic fracturing is a key performance driver in the horizontal development of the Niobrara formation in the Denver-Julesburg (DJ) Basin, Colorado. Oil production per well generally increases with stimulation fluid volume. Often, operators normalize both production and fluid volume based on stimulated lateral length and investigate relationships using "per-ft" variables. However, data from well-based approaches commonly display such wide distributions that no useful relationships can be inferred. To improve data correlations, multivariate analysis normalizes for parameters such as thermal maturity, depth, depletion, proppant intensity, drawdown, geology and completion design. Although advancements in computing power have decreased cycle times for multivariate analysis, preparing a clean dataset for thousands of wells remains challenging. A proposed analytical method using publicly available data allows interpreters to see through the noise and find informative correlations.
Using a data set of over 5000 wells, we aggregate cumulative oil production and stimulation fluid volumes to a per-section basis then normalize by hydrocarbon pore volume (HCPV) per section. Dimensionless section-level Cumulative Oil versus Stimulation Fluid Plots ("Normalization" or "N-Plot") present data distributions sufficiently well-defined to provide an interpretation and design basis of well spacing and stimulation fluid volumes for multi-well development. When coupled with geologic characterization, the trends guide further refinement of development optimization and well performance predictions.
Two example applications using the N-Plot are introduced. The first involves construction of predictive production models and associated evaluation of alternative development scenarios with different combinations of well spacing and completion fluid intensity. The second involves "just-in-time" modification of fluid intensity for drilled but uncompleted wells (DUC's) to optimize cost-forward project economics in an evolving commodity price environment.
Seunghwan Baek and I. Yucel Akkutlu, Texas A&M University Summary Source rocks, such as organic-rich shale, consist of a multiscale pore structure that includes pores with sizes down to the nanoscale, contributing to the storage of hydrocarbons. In this study, we observed hydrocarbons in the source rock partition into fluids with significantly varying physical properties across the nanopore-size distribution of the organic matter. This partitioning is a consequence of the multicomponent hydrocarbon mixture stored in the nanopores, exhibiting a significant compositional variation by pore size-- the smaller the pore size, the heavier and more viscous the hydrocarbon mixture becomes. The concept of composition redistribution of the produced fluids uses an equilibrium molecular simulation that considers organic matter to be a graphite membrane in contact with a microcrack that holds bulk-phase produced fluid. A new equation of state (EOS) was proposed to predict the density of the redistributed fluid mixtures in nanopores under the initial reservoir conditions. A new volumetric method was presented to ensure the density variability across the measured pore-size distribution to improve the accuracy of predicting hydrocarbons in place. The approach allowed us to account for the bulk hydrocarbon fluids and the fluids under confinement. Multicomponent fluids with redistributed compositions are capillary condensed in nanopores at the lower end of the pore-size distribution of the matrix ( 10 nm). The nanoconfinement effects are responsible for the condensation. During production and pressure depletion, the remaining hydrocarbons become progressively heavier. Hence, hydrocarbon vaporization and desorption develop at extremely low pressures. Consequently, hydrocarbon recovery from these small pores is characteristically low. Introduction Resource shale and other source-rock formations with significant amounts of organic matter, such as mudstone, siltstone, and carbonate, have a multiscale pore structure that includes fractures, microcracks, and pores down to a few nanometers (Ambrose et al. 2012; Loucks et al. 2012). The total amount of hydrocarbons stored is directly proportional to the amount of organic matter.
Significant research has been conducted on hydrocarbon fluids in the organic materials of source rocks, such as kerogen and bitumen. However, these studies were limited in scope to simple fluids confined in nanopores, while ignoring the multicomponent effects. Recent studies using hydrocarbon mixtures revealed that compositional variation caused by selective adsorption and nanoconfinement significantly alters the phase equilibrium properties of fluids. One important consequence of this behavior is capillary condensation and the trapping of hydrocarbons in organic nanopores. Pressure depletion produces lighter components, which make up a small fraction of the in-situ fluid. Equilibrium molecular simulation of hydrocarbon mixtures was carried out to show the impact of CO2 injection on the hydrocarbon recovery from organic nanopores. CO2 molecules introduced into the nanopore led to an exchange of molecules and a shift in the phase equilibrium properties of the confined fluid. This exchange had a stripping effect and, in turn, enhanced the hydrocarbon recovery. The CO2 injection, however, was not as effective for heavy hydrocarbons as it was for light components in the mixture. The large molecules left behind after the CO2 injection made up the majority of the residual (trapped) hydrocarbon amount. High injection pressure led to a significant increase in recovery from the organic nanopores, but was not critical for the recovery of the bulk fluid in large pores. Diffusing CO2 into the nanopores and the consequential exchange of molecules were the primary drivers that promoted the recovery, whereas pressure depletion was not effective on the recovery. The results for N2 injection were also recorded for comparison.
Image processing of high-resolution 3D images to create digital representation of pore microstructures for image-based rock physics simulations remains a highly subjective enterprise, despite the seemly precision associated with improving imaging resolutions and intensive parallel computations. The decisions on how to identify pore space, both macro- and micropores, and various mineral components remain very much dependent upon user choices and biases. This study demonstrates how uncertainty can be quantified for a highly subjective segmentation process. A set of shaly sand samples with significant amounts of authigenic chlorite/smectite that lines larger pores was tested to identify uncertainty quantification (UQ) requirements associated with image-processing steps, segmentation in particular. Much of the porosity in these coarse-grain samples is associated with subresolution micropores that complicates their assignment in any pore-grain segmentation strategy. Two segmentation strategies, a binary segmentation with a linear-threshold and a machine-learning (ML) approach to two-phase segmentation, are employed with different UQ parameter space. The contribution of resolvable macropores in these samples, and their spatial distributions with regard to pore-lining clay mineral with unresolvable microporosity, are iteratively studied over the defined UQ parameter space, and cross-validated by independent NMR and MICP measurements. The pore structure extracted from these different iterations was the basis of simulations for basic petrophysical properties. Upon cross-validation of simulated results with measured core properties, a UQ framework is proposed to assess the differences between the different measurements from three angles: sampling, numerical and physical.
Paul, Ferm (Nouryon) | Jeff, Germer (Nouryon) | Kurt, Heidemann (Nouryon) | Stuart, Holt (Nouryon) | Andrew, Robertson (Nouryon) | Jannifer, Sanders (Nouryon) | Klin, Rodrigues (Nouryon) | John, Thomaides (Nouryon) | Nick, Wolf (Nouryon) | Lei, Zhang (Nouryon)
The controlled release of scale inhibitors (SI) and other treatment chemicals in the near-wellbore region is a key strategy to improving water management and extended well production. In addition, during some completion and stimulation operations, it is desired that robust particles providing controlled release be placed in gravel and sand packs. A novel controlled release scale inhibitor particle is presented which provides beneficial properties due to its unique chemistry and polymer processing methods. This technology provides extended feedback of scale inhibitor with tunable release rates.