Alkinani, Husam H. (Missouri University of Science and Technology) | Al-Hameedi, Abo Taleb T. (Missouri University of Science and Technology) | Dunn-Norman, Shari (Missouri University of Science and Technology) | Al-Alwani, Mustafa A. (Missouri University of Science and Technology) | Lian, David (Missouri University of Science and Technology) | Al-Bazzaz, Waleed H. (Kuwait Institute For Scientific Research)
It is not easy to obtain an optimal hole cleaning for the drilling operation because of the complicated relationship between the drilling parameters influencing hole cleaning. The two viscosity components (e.g. plastic viscosity (PV) and yield point (YP)) and the flow rate (Q) are essential parameters for effective hole cleaning. Thus, understanding the relationship between those parameters will contribute to efficient hole cleaning. The aim of this paper is to explore those relationships to provide optimal hole cleaning.
Descriptive data analytics was conducted for data of more than 2000 wells drilled in Southern Iraq. The data were first cleansed and outliers were removed using visual inspection and box plots. The Pearson correlation (PC), a widely used method to measure the linear relationship between two parameters, was utilized to access the relationships between PV and Q, YP and Q, and YP/PV and Q. Moreover, a 10% sensitivity analysis was escorted to quantify and comprehend those relationships.
The PCs were calculated to be 0.5, 0.076, and 0.22 for the relationships between YP, PV, and YP/PV with Q, respectively. YP had the highest direct relationship with Q, while PV had the lowest. When the YP increases, a sufficient Q has to be provided to initiate the flow and maintain the mud cycle. In addition, to prevent large solid particles from settling due to the slip velocity, sufficient annular and particle velocities have to be achieved. After initiating the flow, an increase in flow rate to overcome resistance due to PV will not be significant. Therefore, YP has more effect on Q than PV. To maximize hole cleaning, thickening ratio (YP/PV) should be increased. This requires an increase in flow rate, which can be quantified by using the sensitivity analysis provided to achieve the required Q for any increase in YP/PV.
Al-Hameedi, Abo Taleb T. (Missouri University of Science and Technology) | Alkinani, Husam H. (Missouri University of Science and Technology) | Dunn-Norman, Shari (Missouri University of Science and Technology) | Al-Alwani, Mustafa A. (Missouri University of Science and Technology) | Lian, David (Missouri University of Science and Technology)
Flow rate (Q) affects many drilling operations and parameters such as equivalent circulation density (ECD), hoisting and lowering the drillstring, and breaking gel strength during circulation. The aim of this work is to understand the relationship between ECD and Q based on flow regimes (e.g. laminar, transitional, and turbulent) to avoid or at least minimize the unwanted consequence during drilling practice.
Field data from over 2000 wells drilled in Iraq were collected and analyzed to identify the physical relationship between flow regimes and ECD to enhance the drilling rates. After visualizing the whole dataset, a decision was made to break down the data into three parts based on flow regimes (e.g. laminar, transitional, and turbulent). Descriptive data mining techniques were utilized to establish the relationship between flow regimes and ECD. By achieving better control of ECD in the well, not only faster and cheaper operations are possible, but also safety will be improved.
Previous studies and literature showed that flow regimes can tremendously affect ECD. Many studies have been conducted to understand the relationship between Q and ECD. Nevertheless, the consideration of flow regimes was not implemented in these studies. Inconsistency in the literature results was identified, some concluded the relationship between Q and ECD to be direct, and others showed it to be inverse. Thus, this paper will eliminate this discrepancy in the literature, and it will show that the flow regimes have a pivotal role in the relationship between Q and ECD.
The results of this paper showed that if the flow regime is laminar, the relationship between ECD and Q is inverse. However, in transitional and turbulent flow regimes, the relationship between ECD and Q is direct. That is because, in the laminar flow regime, the cutting will fall out of suspension due to low Q, which will cause a cutting bed to be built and decreases ECD. As Q increases (entering the transitional and turbulent flows) the cutting bed will be eroded, and most of the cuttings will be suspended in the fluid which will increase ECD.
This study examines and expands the understanding between how the characteristics of flow regimes affect ECD. Additionally, this paper will eliminate the discrepancy in the literature about this relationship between ECD and Q.
The Middle cretaceous Wara sandstone reservoir in Minagish Field is considered as highly heterogenetic sandstone which implying lateral facies extensive variations, stacked sand bodies with varying petrophysical properties. Several horizontal wells has been successfully drilled in lower part of Wara 6 sand channel, best thicker clean sand channel with very good oil production rate. Recently some wells have shown depleting of oil and increase water production. To develop such a challenging reservoir to maximize the oil production, a new plan has been developed to explore for new opportunities in Wara reservoir. The objective is to target different good stacked sand bodies in different Wara layers by drilling deviated wells. Some of old depleted Minagsih Oolite reservoir wells have shown good opportunities to sidetrack the wells into good Wara sand layers. This paper presents the integration between geostatistical models, well logs, well test results and different seismic elastic properties maps to identify best subsurface locations for drilling new deviated wells which combine the best quality sand bodies in different Wara layers. A few years ago geostatistical reservoir model along with core data and well log data were utilized to drill successful horizontal wells in W6 sand channels. However due to low resolution seismic data, Wara highly heterogonous lithology and uncertainty in geo-statistical model, it was challenging to continue identify good quality stacked sand bodies in different Wara layers without drilling unwanted silty sand or shale layers. Seismic inversion related elastic impedance data could discriminate between the good quality oil-bearing sand, shaly, and silty sandstones. Several old vertical wells that include good stacked sand bodies in different Wara layers; have been selected to validate the accuracy of elastic impedance maps along Wara layers.
The goal of this work is to evaluate the applicability of a novel set of surfactants to enhance recovery from a viscous oil, high temperature, high permeability, clastic reservoir. A large number of novel short-hydrophobe based surfactants/cosolvents were designed and synthesized. As these surfactants do not require expensive aliphatic alcohols for their synthesis, they are likely to be less costly than conventional anionic surfactants. Here only phenol hydrophobe based non-ionic surfactants with varying number of propylene oxide (PO) and ethylene oxide (EO) groups are discussed. These surfactant molecules were investigated for their aqueous stability limits, interfacial tensions (IFT) with a viscous crude oil and oil recovery from sandpack or sandstone cores. Surfactant phase behavior experiments with viscous crude oil showed low IFT (not ultralow) for single surfactant systems. Only one surfactant (Phenol-7PO-15EO) formulation was chosen for coreflood in sandpack and sandstone cores. Water flood recovered about 50% original oil in place (OOIP) and reduced the oil saturation to about 48% in the high permeability sandpacks. The tertiary surfactant polymer flood with Phenol-7PO-15EO increased the cumulative recovery to 99% for sandpacks. The oil recovery was insensitive to injection brine salinity in the range studied. As the permeability decreased, the tertiary oil recovery decreased if the permeability is lower than 7 Darcy. Surfactant-polymer (SP) formulations with this surfactant can be recommended for high permeability sandstone reservoirs with viscous oils, but not for sub-Darcy sandstones.
Tariq, Zeeshan (King Fahd University of Petroleum & Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Carbonate rocks have a very complex pore system due to the presence of interparticle and intra-particle porosities. This makes the acquisition and analysis of the petrophysical data, and the characterization of carbonate rocks a big challenge. In this study, functional network tool is used to develop a model to predict water saturation using petrophysical well logs as input data and the dean-stark measured water saturation as an output parameter. The data comprised of more than 200 well log points corresponding to available core data. The developed FN model was optimized by using several optimization algorithms such as differential evolution (DE), particle swarm optimization (PSO), and covariance matrix adaptation evolution strategy (CMAES). FN model optimized with PSO found to be the most robust artificial intelligence (AI) model to predict water saturation in carbonate rocks. The results showed that the proposed model predicted the water saturation with an accuracy of 97% when related to the experimental core values. In this study in addition to the development of optimized FN model, an explicit empirical correlation is also extracted from the optimized FN model. To validate the proposed correlation, three most commonly applied water saturation models (Simandoux, Bardon and Pied model, Fertl and Hammack Model, Waxman-Smits, and Indonesian) from literature were selected and subjected to same well log data as the AI model to estimate water saturation. The estimated water saturation values for AI and other saturation models were then compared with experimental values of testing data and the results showed that AI model was able to predict water saturation with an error of less than 5% while the saturation models did the same with lesser accuracy of error up to 50%. This work clearly shows that computer-based machine learning techniques can determine water saturation with a high precision and the developed correlation works extremely well in prediction mode.
While many factors in the reservoir cannot be controlled, there are three controllable factors in field development that make a significant impact. More reservoir contact leads to more oil produced. Controlling sand and water means lower treatment costs, and in-situ reservoir management leads to higher cumulative production. While the underlying technologies have been around for up to 20 years, it is only recently that their synergies and true value are understood. This paper will demonstrate the effect each of these technologies has on increasing overall production rates, improving recovery, and reducing the cost per Barrel of Oil Equivalent (BOE).
The successful implementation of multilaterals in the North Sea will be analyzed. Since 1996, over 300 multilateral junctions have been installed on the Norwegian continental shelf fields with currently approximately 30 junctions completed each year.
Additionally, simulations will be used to demonstrate the incremental improvements in oil recovery that can be obtained by using properly designed advanced completions that include multilaterals, sensors, and passive/active flow control equipment.
The paper will evaluate production performance of a vertical well field development base case against scenarios using horizontal and multilateral wells. It will show how fields can be optimized, leading to increased oil and decreased water production.
Production rates can be significantly improved by combining multilaterals with other advanced completion techniques, such as intelligent completions and inflow control devices. The subject field simulation can be further optimized to manage gas and water production.
With a tailored multilateral field design, combined with properly designed advanced completions systems, the simulation succeeds in terms of achieving maximum contact with the oil reservoir and meeting improved ultimate recovery objectives.
It can be concluded that as reservoir contact is increased, a reduced decline in production rate is observed leading to both a higher Estimated Ultimate Recovery (EUR) and optimized drawdown profile distributions. Additionally, results will be presented that have considered oil production and a method to lower production of unwanted fluids or gas.
This paper also demonstrates the value of field development design from the perspective of reservoir simulation. It is through reservoir insight that a level of understanding is created that can help define the optimum well and completion design to meet field expectations.
Advanced multilaterals continue to grow in popularity with many operators, and it therefore becomes important to evaluate the value of different field development methods. This knowledge can aid operators in unlocking new reservoir targets and optimizing field development, and ultimately will improve recovery factors and overall field economics.
Intelligent multilateral well completions provide downhole flow rate, pressure, and temperature measurements at multiple well segments which allows for a continuous spatiotemporal data stream. Such an extensive data input poses a challenging task to decide on the optimal strategy of manipulating the inflow control valve (ICV) settings over time for best performance. This study investigated the use of machine learning to analyze and predict well performance given different ICV settings to ultimately maximize the well output.
A commercial reservoir simulator was used to generate two synthetic reservoir models: homogeneous (Case A) and heterogenous (Case B). These synthetic data were used to train, validate, and test machine learning models. The reservoir cases were generated based on a segmented, trilateral producer completed with three ICV devices installed at tie-in segments. The data used were measurements of wellhead and downhole flow rates across ICV segments over a period of 4,000 days. A total of 1,330 experiments were conducted with an eight-day timestep, generating a total of 667,660 sample data points for each of Case A and Case B. Fully connected neural networks were used to fit the data while model generalizability was enhanced using regularization techniques, namely L2 regularization and early stopping.
Both random sampling and Latin Hypercube Sampling (LHS) methods were evaluated in constructing the training, validation, and testing splits. Trained with different sample sizes drawn from the 1,330 simulated data histories for the two reservoir models, the proposed neural network showed excellent results. Given only ten simulated choices of ICV settings for training, the network proved capable of predicting oil and water production profiles at surface for both homogeneous and heterogeneous reservoir models with over 0.95 coefficient of determination (R2) when evaluated at unseen, test ICV settings. Extending the problem to downhole flow performance prediction, about 40 training simulated settings were necessary to achieve 0.95 R2. We observed that LHS was superior to random sampling in both R2 average and confidence interval. We also found that increasing the training and validation sample sizes increased the test R2 when testing against unseen cases. Study results suggest the applicability of machine reinforcement learning to estimate the well output at different ICV settings, where the neural network model depends fully on the real-time well feedback and production measurements.
By using a machine learning approach during the operation of a well with multiple ICV settings, it would be feasible to estimate the lateral-by-lateral output at unseen scenarios. Hence, it becomes possible to maximize the well output by using an optimization algorithm to determine the optimal ICV settings.
This review is based on latest application of nanoparticles in hydraulic fracturing, and their feasibility as compared to other conventional methods. Focusing on technical, economic, mechanisms and direction of future research. Current status and advancement give a promising future application by using unique properties of nanomaterials such as small sizes, stability, magnetic properties and surface area which are yet to be exploited to full potential. Nano materials can be inculcated in drilling in all forms. From acting as additives in drilling mud there by enhancing density, gel breaking strength, viscosity, acting as a proppant, cross linking agent etc.
There are certain problems which are difficult to overcome using macro and micro type additives due to limitations in physical, chemical and environmental characteristics. Hence, the scientists are looking for such smart fluids which can overcome these limitations. Compared to their parent materials, nanoparticles can be modified physically, chemically, electrically, thermally, thermodynamic properties and interaction potential of nanomaterial. However more investment, work and pilot projects are required to understand properties of nanomaterials at reservoir temperature and pressure.
Nanomaterials such as aluminium oxide, zinc oxide, copper oxide, silicon dioxide, low cost carbon nanotubes, fly ash nanoparticles in unconventional reservoirs need to be further researched. Moreover, focus should be put on economic analysis, performance at reservoir conditions, cross linking and agglomeration properties, wettability alterations, interfacial tensions properties. The enhanced hydrocarbon recovery from unconventional reservoirs through wettability alterations and interfacial tension decrement by nanomaterials and combined use of fracturing fluid system comprising of VES, foams, proppants gives a promising future application.
Being the largest conference for SPE members, the SPE Annual Technical Conference and Exhibition (ATCE) offers a great opportunity for members to give back to the community that graciously hosts the conference for the week. SPE launched a new initiative called SPE Cares at the ATCE recently held in Dubai. As part of the launch of the program, the SPE Cares Work Group organized a "Give a Ghaf" tree planting event, with more than 70 volunteers representing 16 countries participating. The event aimed to preserve the Ghaf tree, a tree species indigenous to the UAE, Oman, and Saudi Arabia. It is a drought-tolerant, evergreen tree that can survive harsh desert environments.
This challenging reservoir characterization case study is defined by the interaction between two reservoirs with different production mechanisms: a fractured basement reservoir and an overlying sandstone reservoir. The existing static geologic concept has been significantly enhanced by integrating pressure data from a unique three-year shut-in period to aid modeling of fractured reservoir connectivity. Previously, the seismic dataset was predominantly used to model the fault and fracture network and guide well planning. In the current approach, the full field data set, including all drilling parameters and new reservoir surveillance data were integrated to address uncertainty in the connected hydrocarbon volume and the relative importance of each production mechanism. The result is a reservoir management tool with which to test re-development concepts and effectively manage pressure decline and increasing gas/oil ratio (GOR) and water production.
To achieve a fully integrated history matched model, the first step was to make a thorough review of the existing detailed seismic interpretation, vintage production logging tool runs (PLT's), wireline logs (including borehole image logs (BHI)) and drilling data to find a causal link between hydraulically conductive fractures and well production behavior. In parallel, a material balance exercise was run to incorporate the new pressure data acquired during the field's shut-in period. The results of the material balance analysis were combined with seismic and well data to define the distribution of connected fractures across the field. Additionally, the material balance analysis was used to determine the connected hydrocarbon volume, the distribution of initial oil in-place and the relative hydrocarbon contribution from each production mechanism.
The field is covered by multi-azimuth 3D seismic and 43 vertical to highly deviated development wells, providing significant static and dynamic data for characterizing the distribution of connected fractures. Despite this high quality, diverse and field-wide dataset, prior modeling iterations struggled to sufficiently describe the production behavior seen at the well level. This has resulted in a major challenge to predicting the production behavior of new development wells and planning for reservoir management challenges. Capturing the complex interaction between production variables (including lithology, matrix versus fracture network, geomechanical stresses, reservoir damage and pressure depletion) at a field level instead of at an individual well level resulted in a unified static and dynamic model that reconciles all scales of observation.
This oilfield represents a unique reservoir characterization opportunity. The result is a key example of how iterative, integrated geological and engineering driven reservoir modeling can be used to inform the development in a complex, mature field. This case study provides an excellent analogue for the reservoir characterization of other fractured Basement fields and/or Basement-cover reservoir couplet fields in the early to late phases of their development.