bluuebunny/crossref_metadata_2025 huggingface dataset:
West Nile Delta Campaign Delivering Energy to Egypt
Authors: Khedr Sherine , El-dabi Fady , Nashaat Mohamed , Mohiuldin Ghulam , Galal Alaa , Slim Teddy , Hughes Andrea , Morris Lyndsay , Ramsay David , El-wakeel Wael , Mubarak Hussein , Smith Jeffrey , Munger Robert
Published: September 2019

Giza Fayoum Completions was the second campaign of the West Nile Delta project. The campaign consisted of eight cased-hole gravel pack subsea wells. The Giza Fayoum campaign was sanctioned in August 2017 with an execution start date five months later. In this time, the well designs were finalized, downhole completion equipment manufactured, and the execution plan approved. A high rate water pack sand control technique was designed to deliver an estimated production rate of 120 MMscf/d / well. It was planned to deliver eight wells over a period of 5 months from Q1 2018 giving an average of two and a half weeks per well. Seven of the eight wells were cleaned up through a large bore completion landing string system. Each well was flowed to high rate temporary well test equipment installed on the DP semi-submersible rig to a gas rate of 65 MMscf/d, with PLT logs conducted.

This successful, fast-paced campaign is the result of applying lessons learned from the former campaign; Taurus Libra and identifying additional efficiencies that would improve performance. The design similarities between the two campaigns permitted the team to extend the learning curve and deliver superb performance on Giza Fayoum.

As for safety performance, the campaign was delivered without any lost time incident. A rigorous approach to continuous improvement resulted in reducing the completion time to 12 days per well (not including rig move, de-suspension and suspension activities). The optimized bean up procedures supported by PLT data made it possible to reduce greenhouse emissions by 20%. The sand control technique resulted in a significant reduction of total skins. Moreover, the team succeeded in delivering the wells safely, ahead of plan and under budget while adhering to BP's overarching strategy of delivering safe, compliant and reliable wells.

The efficiencies, safety culture and technology used during this campaign are now being set as the standard for future campaigns in Egypt and beyond.
The Extended Whittle-Agarwal Equation for Investigated Volume Under Mass Recharge - Theoretical Foundations and Application to Channel-Sand and Unconventional Reservoirs
Authors: Kubota Leonardo , Keidel Conrado
Published: September 2019

Whittle (2008) proposes an analytical method based on pressure-rate deconvolution to determine the minimum tested volume during a transient well test. Agarwal (2010) also presents a similar equation. Let us refer to it as Whittle-Agarwal Equation (WAE).

The purpose of this paper is threefold: (1) provide a theoretical foundation for WAE based on the method of Succession of Pseudo-Steady States, Shahamat (2014), (2) propose the Extended WAE, which corrects the investigated volume from WAE to account for the presence of mass recharge, due to aquifer influx, gas or water injection in a neighboring well, and (3) provide evidence that the Extended WAE yields sound results by applying it to two field cases: (i) oil producer under the influence of a nearby gas injector, both wells located in a channel-sand reservoir, and (ii) oil producer in unconventional reservoir. Attention is drawn to the fact the original WAE will overestimate the investigated volume if mass recharge is present.
Matching of Pilot Huff-and-Puff Gas Injection Project in the Eagle Ford Shale Using a 3D 3-Phase Multiporosity Numerical Simulation Model
Authors: Fragoso Alfonso , Lopez Jimenez Bruno A. , Aguilera Roberto , Noble Graham
Published: September 2019

Production of oil from pilot shale wells has generally increased by implementing huff-and-puff (H&P) gas injection. The objective of this paper is using a new 3D, 3-Phase, physics-based, multiporosity model for matching and understanding primary oil production as well as recovery by H&P gas injection from a pilot well in the Eagle Ford shale.

History matching and performance forecast are carried out with a newly-developed fully-implicit 3D multi-phase modified black-oil finite difference numerical model, which uses a multiple porosity approach. "The model is capable of handling five storage mechanisms, including (1) organic porosity, (2) inorganic porosity, (3) natural fracture porosity, (4) adsorbed porosity, and (5) hydraulic fracture porosity" (Lopez Jimenez and Aguilera, 2019). Furthermore, the model has capabilities to handle dissolved gas in the solid part of the organic matter, adsorption/desorption from the organic walls, and stress-dependent properties of natural and hydraulic fractures.

These storage and fluid flow mechanisms, as well as the stress-dependency of hydraulic fractures, are widely recognized in the case of some shale petroleum reservoirs. Their inclusion in our simulation model permits evaluating the effect of these mechanisms during H&P gas injection. Results of the simulation, presented as cross-plots of production rates and cumulative production vs. time, indicate that oil recovery from shale petroleum reservoirs can be increased significantly by H&P gas injection. The possibility of desorption and gas diffusion is investigated.

The approach implemented in this H&P history match of an Eagle Ford pilot well should prove of value for simulating complex shale reservoirs.
Processing Asset Data at the Intelligent Edge: Implementation of an Industrial IoT Architecture to Drive Business Value
Authors: Joy David , Smith Doug
Published: September 2019

An Industrial IoT architecture is presented which enables edge computing of data collected from rotating machinery in a model conducive to multiple use cases. The architecture is compared to traditional "IoT data to the cloud" models and on-premise point solutions, illustrating the effectiveness of computing at the edge with a generic IoT solution. Customer benefits from the solution are presented and discussed. The initial solution framework of asset condition monitoring and predictive maintenance is provided by on-premise data analytics software incorporating historical models of asset behavior. Computer hardware traditionally available only in data centers is available to provide powerful computing capabilities. A wireless communications mesh is implemented to provide secure connectivity between plant assets, computers and workers. A digital twin is constructed and augmented reality methods are implemented to extract information from the raw data. Inclusion of plant personnel from planning, to installation, to implementation has been critical to the acceptance and use of the technology in operations. Implementation of a generic IoT architecture rather than a point solution increases the value obtained from the initial investment as subsequent solution frameworks are added. An IoT platform operating at the "intelligent edge" can be more efficient and less costly than cloud-based solutions, reducing time from acquired data to corrective action. An architecture which accounts for role-based and location-aware data presentation further increases the value of the data to the individual worker. Just bringing sensor data to an IoT cloud platform does not create business value from the data. Ensuring interoperability, connectivity, adherence to industry standards and integration of security and manageability are all prerequisites to architecting a scalable Industrial IoT solution that delivers value to the customer. Additional important benefits to the customer are safety, management of change and personal growth of the employees using the technology.
Effect of Compaction on Oil Recovery Under Low Salinity Flooding in Homogeneous and Heterogeneous Chalk
Authors: Hao Jiasheng , Shapiro Alexander
Published: September 2019

Compaction of the reservoir chalk (e.g. surface subsidence) may facilitate oil production. However, only few works have linked smart water flooding with chalk compaction and additional oil recovery. In this work, core flooding experiments with sequential injection of low salinity brines were performed to examine the effect of chalk compaction on oil recovery under smart water flooding. X-ray computer tomography scanning was applied to select outcrop and reservoir cores with different level of heterogeneity, which was demonstrated to be an important factor that determines the recovery even on core scale. A linear variable differential transformer (LVDT) device made it possible to detect changes of the core length during the experiments, which served as an indication of the compaction. Overburden pressure was increased stepwise at the final stages of the flooding to achieve higher compaction of the cores.

During secondary flooding, slight gradual compaction of the cores was observed. Subsequent low salinity flooding did not lead to further compaction for all the samples, nor additional oil recovery. Under final compaction, significantly more oil was produced from the heterogeneous cores, especially, from the reservoir core. Some fines production was observed during the core cleaning after the experiments. Fluid diversion due to closing micro-fractures under compaction and/or relocation of the fines is speculated to be a driving mechanism behind additional recovery from heterogeneous cores. Rock compaction appears to be a potential mechanism for enhanced oil recovery, however with a limited efficiency.
Formation Fluid Sampling Simulation: The Key to Successful Job Design and Post-Job Performance Evaluation
Authors: Kristensen Morten , Dumont Hadrien , Akindipe Tunde , Chugunov Nikita , Garcia German
Published: September 2019

Acquisition of fluid samples using wireline formation testers (WFTs) is an integral part of reservoir evaluation and fluid characterization. The increasing complexity of fluid sampling operations, especially in remote or offshore fields, requires a careful planning process involving systematic de-risking of the sampling objectives through quantitative evaluation of sampling hardware performance under uncertain downhole conditions and reservoir properties. During job execution, the cleanup of mud filtrate is monitored using downhole fluid analysis (DFA) sensor measurements. In addition to quantifying produced contamination and providing guidance for real-time decisions, these measurements hold valuable information about formation and fluid properties that can be extracted through advanced interpretation workflows.

In this paper, we demonstrate how a quantitative, model-based workflow was applied to both planning and interpretation for a series of sampling jobs in a remote and harsh environment. At its core, the workflow consists of high-resolution numerical flow models for the filtrate cleanup process that cover both conventional and focused sampling tools. To enable real-time, interactive, and probabilistic workflows, we use machine learning techniques to construct fast, high-fidelity proxy models, which, after thorough validation, replace numerical simulation in the workflow. Finally, the workflow employs methods for uncertainty quantification, global sensitivity analysis, and model inversion.

During the pre-job planning phase, the model-based workflow was used to select and mobilize the optimal sampling hardware, estimate sampling time uncertainty, and pinpoint the dominant sources of this uncertainty through global sensitivity analysis. After successful sample acquisition, the DFA measurements were reconciled with the cleanup model and the petrophysical evaluation to extract additional value from the measurements. Using measurements of water-cut and pressure, and conditioned to the petrophysical evaluation, the cleanup model was inverted for two-phase relative permeabilities. This recently developed methodology complements laboratory measurements of relative permeability on core samples.

Building on previous work in this area, this paper demonstrates the practical application of advanced planning and interpretation workflows for downhole fluid sampling. The methodology presented couples traditional, full-physics flow modeling with modern machine learning techniques to achieve highly agile workflows, enabling operators to more efficiently plan sampling jobs and extract value from the measurements.
Methods for Probabilistic Uncertainty Quantification with Reliable Subsurface Assessment and Robust Decision-Making
Authors: Tanaka Shusei , Dehghani Kaveh , Wang Zhenzhen
Published: September 2019

Reliability of subsurface assessment for different field development scenarios depends on how effective the uncertainty in production forecast is quantified. Currently there is a body of work in the literature on different methods to quantify the uncertainty in production forecast. The objective of this paper is to revisit and compare these probabilistic uncertainty quantification techniques through their applications to assisted history matching of a deep-water offshore waterflood field. The paper will address the benefits, limitations, and the best criteria for applicability of each technique.

Three probabilistic history matching techniques commonly practiced in the industry are discussed. These are Design-of-Experiment (DoE) with rejection sampling from proxy, Ensemble Smoother (ES) and Genetic Algorithm (GA). The model used for this study is an offshore waterflood field in Gulf-of-Mexico. Posterior distributions of global subsurface uncertainties (e.g. regional pore volume and oil-water contact) were estimated using each technique conditioned to the injection and production data.

The three probabilistic history matching techniques were applied to a deep-water field with 13 years of production history. The first 8 years of production data was used for the history matching and estimate of the posterior distribution of uncertainty in geologic parameters. While the convergence behavior and shape of the posterior distributions were different, consistent posterior means were obtained from Bayesian workflows such as DoE or ES. In contrast, the application of GA showed differences in posterior distribution of geological uncertainty parameters, especially those that had small sensitivity to the production data. We then conducted production forecast by including infill wells and evaluated the production performance using sample means of posterior geologic uncertainty parameters. The robustness of the solution was examined by performing history matching multiple times using different initial sample points (e.g. random seed). This confirmed that heuristic optimization techniques such as GA were unstable since parameter setup for the optimizer had a large impact on uncertainty characterization and production performance.

This study shows the guideline to obtain the stable solution from the history matching techniques used for different conditions such as number of simulation model realizations and uncertainty parameters, and number of datapoints (e.g. maturity of the reservoir development). These guidelines will greatly help the decision-making process in selection of best development options.
Simple, Systematic Reserves Estimation for Unconventional Wells
Authors: Cartier Cheryl , Miller Patrick , Wagner Peter
Published: September 2019

Booking reserves for unconventional, multi-frac wells is a critical business process, but to be done effectively, often requires significant time investment and multiple interpretation techniques. Although reserves can be estimated quickly with decline curve analysis (DCA) alone, the subjectivity in DCA makes it challenging for evaluators to estimate reserves with appropriate levels of uncertainty and maintain consistency between evaluators. The objective of this paper is to present a fast, systematic, yet rigorous methodology for estimating 1P, 2P and 3P estimated ultimate recoveries (EURs) for new wells. This methodology utilizes regression to correlate easy to obtain, early life indicators of well performance to 2P EURs, which have been estimated from more detailed interpretations. Multiple methodologies are presented for estimating 1P and 3P EURs.
Data Driven Modeling and Prediction for Reservoir Characterization Using Seismic Attribute Analyses and Big Data Analytics
Authors: Zhou Xu , Tyagi Mayank , Zhang Guoyin , Yu Hao , Chen Yangkang
Published: September 2019

With recent developments in data acquisition and storage techniques, there exists huge amount of available data for data-driven decision making in the Oil & Gas industry. This study explores an application of using Big Data Analytics to establish the statistical relationships between seismic attribute values from a 3D seismic survey and petrophysical properties from well logs. Such relationships and models can be further used for the optimization of exploration and production operations.

3D seismic data can be used to extract various seismic attribute values at all locations within the seismic survey. Well logs provide accurate constraints on the petrophysical values along the wellbore. Big Data Analytics methods are utilized to establish the statistical relationships between seismic attributes and petrophysical data. Since seismic data are at the reservoir scale and are available at every sample cell of the seismic survey, these relationships can be used to estimate the petrophysical properties at all locations inside the seismic survey.

In this study, the Teapot dome 3D seismic survey is selected to extract seismic attribute values. A set of instantaneous seismic attributes, including curvature, instantaneous phase, and trace envelope, are extracted from the 3D seismic volume. Deep Learning Neural Network models are created to establish the relationships between the input seismic attribute values from the seismic survey and petrophysical properties from well logs. Results show that a Deep Learning Neural Network model with multi-hidden layers is capable of predicting porosity values using extracted seismic attribute values from 3D seismic volumes. Ultilization of a subset of seismic attributes improves the model performance in predicting porosity values from seismic data.
The Effect of Temperature on Two-Phase Oil/Water Relative Permeability in Different Rock/Fluid Systems
Authors: Esmaeili Sajjad , Sarma Hemanta , Harding Thomas , Maini Brij
Published: September 2019

Two-phase oil/water relative permeability measurements were conducted at ambient and high temperatures in two different rock-fluid systems; one using a clean Poly-Alpha-Olefin (PAO) oil and the other with Athabasca bitumen. The tests were performed in a clean sand-pack with the confining pressure of 800 psi, using deionized water as the aqueous phase. Both the JBN method and the history match approach were utilized to obtain the relative permeability from the results of isothermal oil displacement tests. The contact angle and IFT measurements were carried out to assess any possible wettability alteration and change in fluid/fluid interaction at higher temperatures.

Results, Observations, Conclusions: The results of the clean system using the viscous PAO oil confirmed that the two-phase oil/water relative permeability in this ultra-clean system is practically insensitive to the temperature. The slight variation in oil endpoint relative permeability, especially at ambient condition, was attributed to variations in the packing of sand. It was found that the history matching derived two-phase relative permeability from the highest temperature test provides reasonably good history matches of the other displacements that were conducted at lower temperatures. In addition, it is shown that the JBN approach based relative permeability curves show larger variations, primarily due to insufficient volume of water injection at lower temperatures, which makes the practical residual oil saturation much higher than the true residual. In contrast with the ultra-clean system, the results obtained with bitumen showed much larger variations in relative permeability with temperature.

Most of the reported studies involving history matching approach treat the low-temperature measurements as the base case and show that changes in relative permeability are needed to history-match the tests at higher temperatures. We have shown that the displacement done at the highest temperature provides a more reliable estimate of the relative permeability and, in some cases, this relative permeability can successfully history match tests done at lower temperatures. In view of the impracticality of injecting sufficient water to reach close to real residual oil saturation at low temperatures, it would be better to obtain relative permeability data at high temperatures for characterizing the two-phase flow behavior of viscous oil systems.
Drilling Mechanics Analysis of Record Hybrid Drill Bit Runs in Gulf of Mexico Salt Formation and its Correlation with Rock-Mechanical Properties of Salt
Authors: Prasad Umesh , Roy Chowdhury Ashabikash , Anderson Mark
Published: September 2019

Operators face the continuing challenge to improve drilling efficiency for cost containment, especially in deepwater drilling environments where drilling costs are significantly higher. Innovative drilling technologies have been developed and implemented continuously to support the initiative. In many areas of the world, including the Gulf of Mexico (GOM), hydrocarbon reservoirs exist below thick non-porous and impermeable sequences of salt that are considered a perfect cap rock. However, salt poses varied levels of drilling challenges due to its unique mechanical properties.

At ambient conditions, the unconfined compressive strength (UCS) of salt varies between 3,000 to 5,000 psi; however, the strain at failure for salt can be an order of magnitude higher when compared to other rocks. Consequently, during drilling salt's viscoelastic behavior requires that its must be broken with an inter-crystalline or trans-crystalline grain boundary breakage. When compared to other rock types, the unique isotropic nature of salt results in a level of strain that is much higher for the given elastic moduli. This strain level makes salt failure mechanics different from other rock types that are prevalent in the GOM.

Hybrid bits combine roller-cone and polycrystalline diamond compact (PDC) cutting elements to perform a simultaneous on-bottom crushing / gouging and shearing action. Two divergent cutting mechanics pre-stresses the rock and apply high strain for deformation and displacement, resulting in highly efficient cutting mechanics. To meet the drilling objectives, different hybrid designs have been implemented to combine stability and aggressiveness for improved drilling efficiency. An operator, while drilling salt sections at record penetration rates, has successfully used this innovative process of rock failure utilizing the dual-cutting mechanics of hybrid bits. This has resulted in significant value additions for the operator.

This paper analyzes field-drilling data from successful GOM wells and attempts to correlate salt failure mechanics and provide insight into dual-cutting mechanics and its correlation with salt failure. The paper also reviews the drilling mechanics of hybrid bits in salt and highlights importance of dual-cutting mechanics for achieving higher penetration rates in salt through improved drilling efficiency.
Lessons Learned from Casing-Casing Annulus Repair Utilizing Rigless Operation Approach in Offshore Oil Field
Authors: Al-Dhafeeri Abdullah , Al-Aklubi Sultan , Abo Zkery Shebl
Published: September 2019

Several aged oil wells in offshore oil field are drilled in a conventional method. These wells are subjected to Casing-Casing Annulus (CCA) problems that might appear during the production operation and/or the shutdown phases. A continuous monitoring is performed to avoid issues related to well integrity and safety. The expected source of Casing-Casing Annulus (CCA) problem is mainly due to poor primarily cementing placement into the outer-casing strings especially across shallow aquifers formations. Due to long shutdown period on subject wells, these wells are encountered with high rate of CCA phenomena among other wells. An immediate mitigation action is required to resolve the issues by applying rig workover operation which is considered highly cost approach with low success rate. The rig workover operation results might lead to suspension or abandonment of these wells. The impact will affect the production target and the oil recovery around the area.

A new methodology approach was selected using chemical sealant recipes as a rigless operation to repair CCA problem with cost-effective and safe manner for first time in offshore filed. Based on the wellhead and annuli survey, the bleed down and build up tests were conducted and followed by close monitoring on suspected wells, which revealed sustained casing pressures and fluid return at the surface. Several fluid samples were collected and analyzed in the lab. Based on the findings, the procedures and the proper design were conducted to inject the chemical sealant into connected cement channels behind casing strings. Curing time and injection rate with required volumes of chemicals were considered based on the pressure responses and chemical performance.

The results from the rigless operation job utilizing the new approach showed wide-ranging success rates based on well by well cases and conditions such as 1) Age of the well, 2) Sustained pressure observed at the surface, 3) Injectivity rates, 4) Chemical additives volume and 5) Downhole conditions (pressure / temperature).

The new technique added a great value on restoring the well integrity and saving the rig operation cost. In addition, the approach contributed to achieve maximum sustainable production target through ensuring the well operability and reducing the production down time. Challenges, methodology, work schedule, risk assessment, lessons learned and findings have been covered in this paper.
A New Fluid-Based Isolation Spacer Increases Success When Spotting Off-Bottom Cement Plugs
Authors: Webber Claire , Turner Jay
Published: September 2019

Successfully spotting an Off-Bottom Cement Plug (OBCP) has been problematic in the oilfield for decades. The use of high viscosity support spacers below the cement plug has been an attractive method to support the cement. These isolation spacers have traditionally been formulated with either biopolymers or bentonitic clays, but they too often fail to support the cement plug due to inadequate viscosity or poor placement. The result is that the cement slumps below the desired location in the wellbore, requiring some remedial action such as spotting an additional spacer and OBCP. One response to this challenge has been the use of a packer to support the plug, but this entails the additional cost of the packer plus the rig time required to place it.

A better method was needed to increase OBCP success while reducing costs. A new isolation spacer technology was adapted to meet this challenge, and the resulting field applications are described in this paper. Developmental lab testing will be detailed along with an initial trial well where the spacer was used to isolate a retrievable packer from workover debris. After success there, the same spacer technology was used to successfully support an OBCP on a rigless well abandonment.
Optimizing Horizontal Well Placement Through Stratigraphic Performance Prediction Using Artificial Intelligence
Authors: Popa Andrei , Connel Sean
Published: September 2019

Accurate predictions of connectivity and heterogeneity pose important technical challenges for successful maturation of conventional and unconventional reservoirs. We present the success of a new reservoir management workflow that uses both artificial intelligence and classic models to define the impact of stratigraphic connectivity and heterogeneity on horizontal-well production performance in a mature heavy oil field. The data-driven model based on fuzzy logic was used to compute a new attribute named dynamic Reservoir Quality Index (dRQI). The classical models used the stratigraphic Lorenz Plots, Reservoir Quality Index (RQI) and Flow-Zone indicator (FZI). Workflows were validated through a lookback process on more than 400 wells used to predict the fine-scale stratigraphic and directional heterogeneities within intervals targeted by horizontal wells, and production performance. The workflow was successfully used to optimize the horizontal well placement for 2019-2020 drilling programs.
Data Driven Methods to Monitor Sticking Friction While Running Pipe
Authors: Spivey Benjamin , Bailey Jeffrey , Pokluda Joshua , Page Coby
Published: September 2019

Drillpipe and casing can become stuck while running into a wellbore or pulling out of a wellbore which can lead to significant non-productive time (NPT) events and cause costly remediation and lost production. These sticking events can be caused by wellbore quality issues, also called mechanical sticking, or differential sticking due to the pressure differential between the wellbore and formation pressure which creates normal force on the tubular. Pipe running friction is traditionally monitored by viewing trends for average hookload and torque values while picking up, slacking off, or rotating off-bottom. These average hookload and torque values only apply to dynamic friction factors and do not differentiate the cause of friction.

Static friction correlates with differential sticking whereas dynamic friction can be caused by mechanical sticking. This paper discusses a sticking advisory system for detecting both static and dynamic sticking as friction increases while running into or pulling out of the wellbore. The system uses one-second surface drilling data to identify friction indicators in the axial and rotational directions based on data-driven analysis of time series data trends. The system can create these trends in near-real-time to enable a team to monitor ongoing operations and make proactive decisions. The system trends may also be used in hindcasting mode to identify thresholds for friction indicators based on historical events. The friction indicators are validated by comparing data trends from wells with and without known sticking events and correlating trends with operational activities. Two indicators, Static Sticking Index and Dynamic Sticking Index, are introduced to provide useful surveillance parameters during pipe running operations.
Analytical Model for Rate Transient Analysis in Low-Permeability Volatile Oil Reservoirs
Authors: Luo Le , Cheng Shiqing , Lee John
Published: September 2019

This paper presents a simple yet rigorous analytical solution for two-phase (gas-oil) flow in closed volatile oil reservoirs. The solution includes all flow regimes over the life of a multi-fractured horizontal well, including the usually long-duration early transient flow followed by the transition and the boundary-dominated flow regimes. The solution will be particularly useful in rate transient analysis of production data and production forecasting for horizontal wells with multiple fractures in ultra-low permeability reservoirs, such as shales. We formulated the governing, non-linear partial differential equations (PDEs) for simultaneous gas-oil flow with an inner boundary condition of constant bottom-hole pressure (BHP). We then defined pseudo-variables to transform the non-linear PDEs to linear forms. By developing deterministic models for calculation of fluid properties using multi-regression analysis of PVT data and relative permeability curves, we were able to find analytical solutions by the separation of variables method for specified initial and outer boundary conditions. We obtained a production rate-time relation which can be used to generate type curves or to provide a basis for history matching production data and forecasting future production. Under constant bottom-hole pressure producing condition, the resulting solutions that describe the relationship between dimensionless rate and dimensionless two-phase pseudotime indicate a complicated decline with an exponential relation inside an infinite series. We validated the solutions through comparisons with compositional simulation using commercial software; the satisfactory agreements demonstrated the accuracy and utility of the analytical solutions. Our results indicate that the production performance in multi-phase flow is far different than performance in single-phase flow, and that formation properties interpreted using techniques appropriate for single-phase flow can be seriously in error when applied to two-phase flow situations. Finally, we found that our analytical solution yielded reasonable interpretations of actual field data from the Midland Basin.
Industry-University Collaboration to Develop Sustainable Petroleum Engineering Program and Meet the Industry Needs in Kazakhstan
Authors: Syzdykov Murat , Ozkan Erdal
Published: September 2019

This paper presents the background, implementation, and initial results of a pilot project to address the shortage of qualified petroleum engineers in developing countries. Oil and gas talent gap in emerging markets was identified as an eminent problem by the Steering Committee of the World Economic Forum's (WEF) Oil & Gas Community in 2017. Chevron, Eni, and Shell acted on the initiative of WEF and, with the addition of Colorado School of Mines (Mines) as the academic partner, kicked off a pilot project to improve the Petroleum Engineering (PE) program at Satbayev University (SU) in Almaty, Kazakhstan, in 2018. The WEF working group, consisting of the representatives of the three companies and the department heads of Mines and SU, identified three priority areas: (1) Establishment of an Industry-Advisory Board (IAB) to promote mutual trust and collaboration between academia and industry, (2) Curriculum revision and improvement of the course material and delivery with the support of Mines, and (3) Student and faculty internship programs to provide industry training and support for faculty development. Many challenges of the Kazakh PE education are common to the other emerging oil and gas producing countries also. Therefore, the lessons learned from this project will be useful to develop similar projects not only in Kazakhstan but also around the world. This paper presents the details of implementation, challenges encountered, and initial results of the project.
Application of Constrained Multiwell Deconvolution to Interfering Wells in Gas Reservoirs With Significant Pressure Depletion
Authors: Jaffrezic V. , Razminia K. , Cumming J. , Whittle T. M. , Gringarten A. C.
Published: September 2019

Objectives/Scope

This paper uses pseudo-time to extend the application of constrained multiwell deconvolution algorithm to gas reservoirs with significant pressure depletion. Multiwell deconvolution is the extension of single well deconvolution to multiple interfering wells. Constraints are added to account for a-priori knowledge on the expected deconvolved derivative behaviors and to eliminate non-physical solutions.

Methods, Procedures, Process

Multiwell deconvolution converts pressure and rate histories from interfering wells into constant-rate pressure responses for each well as if it were producing alone in the reservoir. It also extracts the interference responses observed at each of the other wells due to this single well production. The deconvolved responses have the same duration as the pressure history. This allows to identify reservoir features not visible during individual build ups.

Deconvolution techniques can only be applied to pressure and rate data when flow can be represented by linear equations. In strongly depleted gas reservoirs, fluid properties, and gas compressibility in particular, are pressure dependent, which makes the flow problem non-linear. The paper uses pseudo-pressure and pseudo-time transforms to linearize the problem in such conditions.

Results, Observations, Conclusions

The pseudo-time method developed by Levitan and Wilson (2010) is extended to constrained multiwell deconvolution in highly depleted gas reservoirs. It is an iterative process, which yields the correct initial gas in place, whereas the use of real time does not. The algorithm is validated with a two gas well synthetic example, and successfully applied to a field case with four gas producers.

Novel/Additive Information

The paper extends the application of constrained multiwell deconvolution to strongly depleted gas reservoirs. Constrained multiwell deconvolution is an efficient way to exploit data recorded by permanent downhole pressure gauges and provides information not otherwise available. It can help to identify field heterogeneities and compartmentalization early in field life, making it possible to modify the field development plan and to improve locations of future wells. It can accelerate history-matching with the reservoir model by doing it on the constant rate pressure responses rather than on the actual, usually complex, production history. An added advantage is that comparison between the pressure derivatives of the model and the actual deconvolved derivatives allows identification of mismatch causes.
Converting Time Series Data into Images: An Innovative Approach to Detect Abnormal Behavior of Progressive Cavity Pumps Deployed in Coal Seam Gas Wells
Authors: Saghir Fahd , Gonzalez Perdomo M. E. , Behrenbruch Peter
Published: September 2019

Progressive Cavity Pumps (PCPs) are the predominant form of artificial lift method deployed by Australian operators in Coal Seam Gas (CSG) wells. With over five thousand CSG wells [1] operating in Queensland's Bowen and Surat Basins, managing and maintaining PCP supported production becomes a significant challenge for operators. Especially when these pumps face regular failures due to the production of coal fines.

It is possible to gauge the holistic production performance of PCPs with the aid of real-time data, as this allows for pro-active and informed management of artificially lifted CSG wells. Based on data obtained from two (2) CSG operators, this paper will discuss in detail how features extracted from time series data can be converted to images, which can then aid in autonomously detecting abnormal PCP behavior.
Amino Acid as a Novel Wettability Modifier for Enhanced Waterflooding in Carbonate Reservoirs
Authors: Lara Orozco Ricardo A. , Abeykoon Gayan A. , Wang Mingyuan , Argüelles Vivas Francisco J. , Okuno Ryosuke , Lake Larry W. , Ayirala Subhash C. , AlSofi Abdulkareem M.
Published: September 2019

Reservoir wettability plays an important role in waterflooding especially in fractured carbonate reservoirs since oil recovery from the rock matrix is inefficient because of their mixed wettability. This paper presents the first investigation of amino acids as wettability modifiers that increase waterflooding oil recovery in carbonate reservoirs.

All experiments used a heavy-oil sample taken from a carbonate reservoir. Two amino acids were tested, glycine and β-alanine. Contact angle experiments with oil-aged calcite were performed at room temperature with deionized water, and then at 368 K with three saline solutions: 243,571-mg/L salinity formation brine (FB), 68,975-mg/L salinity injection brine 1 (IB1), and 6,898-mg/L salinity injection brine 2 (IB2). IB2 was made by dilution of IB1.

The contact angle experiment with 5-wt% glycine solution in FB (FB-Gly5) resulted in an average contact angle of 50°, in comparison to 130° with FB, at 368 K. Some of the oil droplets were completely detached from the calcite surface within a few days. In contrast, the β-alanine solutions were not effective in wettability alteration of oil-aged calcite with the brines tested at 368 K.

Glycine was further studied in spontaneous and forced imbibition experiments with oil-aged Indiana limestone cores at 368 K using IB2 and three solutions of 5 wt% glycine in FB, IB1, and IB2 (FB-Gly5, IB1-Gly5, and IB2-Gly5). The oil recovery factors from the imbibition experiments gave the Amott index to water as follows: 0.65 for FB-Gly5, 0.59 for IB1-Gly5, 0.61 for IB2-Gly5, and 0.33 for IB2. This indicates a clear, positive impact of glycine on wettability alteration of the Indiana limestone cores tested.

Two possible mechanisms were explained for glycine to enhance the spontaneous imbibition in oil-wet carbonate rocks. One mechanism is that the glycine solution weakens the interaction between polar oil components and positively-charged rock surfaces when the solution pH is between glycine's isoelectric point (pI) and the surface's point of zero charge (pzc). The other mechanism is that the addition of glycine tends to decrease the solution pH slightly, which in turn changes the carbonate wettability in brines to a less oil-wet state.

The amino acids tested in this research are non-toxic and commercially available at relatively low cost. The results suggest a new method of enhancing waterflooding, for which the novel mechanism of wettability alteration involves the interplay between amino acid pI, solution's pH, and rock's pzc.
Co-Development of Multiple Targets in Permian Unconventional Reservoirs
Authors: Cao Richard , Chen Chaohui , Girardi Alejandro , Li Ruijian , Chowdhury Nitin
Published: September 2019

Optimum co-development layout of multiple targets for unconventional reservoirs is extremely challenging due to complex 3-dimentional well interactions, stochastic well performance, complex fracture geometry, dynamic SRV/DRV evolution, heterogeneous rock properties, various operating conditions, and different economic drivers. In this study, an integrated workflow is developed and applied for co-development of multiple targets in Permian unconventional reservoirs.

In this workflow, the field pilot and trial measurements, Microseismic, geochemistry measurement, data analytics, detailed geomechanical and reservoir modeling, stochastic multiple history matching and forecast, all combined to quantify the horizontal and vertical interference factors and obtain production profiles for different co-development designs. The stochastic behavior of the well performance is explored from three different aspects: static rock properties, dynamic fracturing, and production. The SRV/DRV evolution are presented as the probability distribution function of half fracture length from Microseismic data and effective drainage half-length from stochastic modeling.
Production Forecasting: Optimistic and Overconfident – Over and Over Again
Authors: Bratvold Reidar B , Mohus Erlend , Petutschnig David , Bickel Eric
Published: September 2019

The oil & gas industry uses production forecasts to make a number of decisions as mundane as whether to change the choke setting on a well, or as significant as whether to develop a field. As these forecasts are being used to develop cashflow predictions and value and decision metrics such as Net Present Value and Internal Rate of Return, their quality is essential for making good decision. Thus, forecasting skills are important for value creation and we should keep track of whether production forecasts are accurate and free from bias.

In this paper we compare probabilistic production forecasts at the time of the development FID with the actual annual production to assess whether the forecasts are biased; i.e., either optimistic, overconfident, or both.

While biases in time and cost estimates in the exploration & production industry are well documented, probabilistic production forecasts have yet to be the focus of a major study. The main reason for this is that production forecasts for exploration & production development projects are not publicly available. Without access to such estimates, the quality of the forecasts cannot be evaluated.

Drawing on the Norwegian Petroleum Directorates (NPD) extensive database, annual production forecasts, given at time of project sanction (FID), for 56 fields in the 1995 – 2017 period, have been compared with actual annual production from the same fields. The NPD guidelines specify that the operators should report the annual mean and P10/90-percentiles for the projected life of the field at the time of the FID; that is, the forecasts should be probabilistic. The actual annual production from the fields was statistically compared with the forecast to investigate if the forecasts were biased and to assess the financial impact of such biases.

This paper presents the results from the first public study of the quality of probabilistic production forecasts. The main conclusions are that production forecasts that are being used at the FID for E&P development projects are both optimistic and overconfident. As production forecasts form the basis for the main investment decision in the life of a field, biased forecasts will lead to poor decisions and to loss of value.
A Novel Workflow for Oil Production Forecasting using Ensemble-Based Decline Curve Analysis
Authors: Hakim Elahi Siavash
Published: September 2019

In the absence of well-developed calibrated geologic and simulation models, empirical approaches such as decline curve analysis (DCA) are normally used for production forecasting and reserve estimation. DCA is computationally more efficient compared to simulation models when the active well base exceeds hundreds of wells. However, the underlying assumption for conventional DCA is no change in well operation settings. Moreover, the common approach for production forecasting consists of manual outlier detection and removal, interpretation of missing measurements and data fitting using different models for each well. Therefore, the process of conventional DCA is subjective due to the lack of a standard workflow for preprocessing and data cleansing. The common practice for doing DCA has three main steps: 1. Finding the most representative period in the history of well, 2. Detecting the initial rate (start point) of forecast, 3. Selecting the type of decline and fitting the appropriate model to data points. The solutions to these problems could vary from engineer to engineer and it can be time consuming to analyze all wells manually. To address these issues, we developed a novel workflow based on stochastic methods for detecting various well interventions including change in artificial lift, pump changes and acid treatment, and for forecasting oil production rate more accurately in the presence of uncertainty. The novelty of the proposed ensemble-based approach is forecasting conditioned on various well interventions. Furthermore, the proposed unsupervised stochastic anomaly detection method will detect various well works (or events) in the case of missing records of time and type of events. In this paper, we designed two experiments to test the proposed workflow for oil production rate forecasting and evaluation of acid treatments.
A Model Predictive Control Method for Autonomous Directional Drilling
Authors: Demirer Nazli , Zalluhoglu Umut , Marck Julien , Gharib Hossam , Darbe Robert
Published: September 2019

Directional drilling for hydrocarbon exploration has been challenged to become more cost-effective and consistent with fast-growing drilling operations for both offshore and onshore production areas. Autonomous directional drilling provides a solution to these challenges by providing repeatable drilling decisions for accurate well placement, improved borehole quality, and flexibility to adapt smoothly to new technologies for drilling tools and sensors. This work proposes a model predictive control (MPC)-based approach for trajectory tracking in autonomous drilling. Given a well plan, bottomhole assembly (BHA) configuration, and operational drilling parameters, the optimal control problem is formulated to determine steering commands (i.e., tool face and steering ratio) necessary to achieve drilling objectives while satisfying operational constraints.

The proposed control method was recently tested and validated during multiple field trials in various drilling basins on two- and three-dimensional (2D and 3D) well plans for both rotary steerable systems (RSS) and mud motors. Multiple curve sections were drilled successfully with automated steering decisions, generating smooth wellbores and maintaining proximity with the given well plan.