Presented at SPE Western Regional Meeting, Virtual, April 2021.

Abstract

Carbon offset describes the environmental benefit from an initiative that avoids, reduces or removes greenhouse gases (GHGs) from the atmosphere. The Intergovernmental Panel on Climate Change has identified Carbon Dioxide (CO2), Methane (CH4) and Nitrous Oxide (N2O) as major constituent of the GHGs. Wastewater Treatment Facilities (WWTFs) among several other sectors is a neglected source for GHG emission. Considering the risk of rise in GHGs, United States along with other countries signed the Paris Agreement to respond to the global climate change threat in 2016. It is assessing projects to cut GHGs in exchange for emission credits that can be used to comply with goals they set under the United Nations pact. In order to curb the GHG emission by WWTFs, an innovative approach “Bioslurry Injection” (BSI) can be implemented to reduce the emission of the GHGs produced during the course of biological and chemical treatment of wastewater. The technology is inherited from the traditional drill cutting injection and Carbon sequestration technology implemented by the Oil and Gas industry since 1980’s. The BSI operation has the ability to accept the feed from different treatment stages after the initial screening process to prepare the injection slurry and help in controlling the GHG emission at respective treatment stage along with managing the intake volume. The slurry can be prepared by mixing the treated biosolids with wastewater and injected into a pre-selected underground earth formation, where biosolids undergo anaerobic digestion and decompose into CO2 and CH4. An injection formation with sufficient capacity to accept the slurry is selected by conducting a detailed geomechanical and fracture simulation analyses. Along with the injection feasibility, the calculations of the amount of Carbon dioxide equivalent (CO2e) sequestrated underground by implementing BSI technique is presented in this paper. The sequestration of decomposed GHGs is an environmentally friendly activity that has proved to be economically beneficial due to its ability to earn carbon offsets. According to the new carbon law in the state of California the amount of CO2e eliminated from the atmosphere can be traded to earn carbon credits. TIRE facility through its ability to sequester and thus eliminate emission of the GHGs from the atmosphere can gain up to $1.5M worth of carbon credits per year providing both environmental and economic benefit. Also, low capital and operating cost for the BSI facility due to its compact surface requirement is an additional advantage along with reduced risk of spillage hazard when BSI facility is incorporated within the WWTF boundaries.

Evaluation of Annulus Pressure Buildup During Injection Operations

I. M. Mohamed, Y. Panchal, N. Mounir, G. Woolsey, O. A. Abou-Sayed, A. S. Abou-Sayed
J. Energy Resour. Technol. Jul 2021, 143(7): 073002 (9 pages)
JERT-20-1505 https://doi.org/10.1115/1.4048720

Abstract

More than 300 million barrels of saltwater is produced every day from oil and gas production wells. Most of this volume is injected through either saltwater disposal wells or used for water flooding and enhanced recovery purposes. Usually, the regulations require the injection to be conducted through the injector well tubing that is isolated from the well annulus to protect the underground source of drinking water (USDW) by preventing any possible leak through the well casing. Monitoring of the annulus pressure during injection ensures the well integrity. The annulus pressure changes can occur by one of the following mechanisms: thermal expansion of the annulus fluid; ballooning of the injection tubing; communication between the tubing and the annulus; or fluid migration behind the casing. Determining the communication mechanism can be a complex process and a need may arise to run several testing procedures and inspect all the wellbore components. Successful evaluation of the annulus pressure values and trends can directly identify the root cause of the annulus pressure buildup and simultaneously save time and reduce the cost associated with the workover operations. The seven case studies presented in this paper focus on the details pertaining to the annulus pressure buildup under different well conditions and purposes the interpretation technique for each case.

Paper presented at Ain Shams Engineering Journal Volume 12, Issue 1, March 2021, Pages 917-926

Abstract

Predicting the rate of penetration (ROP) plays a key role in the success of the drilling operation. It is not an easy task to predict the ROP with high accuracy as it depends on several factors such as; drilling parameters, drilling fluid properties, and drilled formation characteristics. The objective of this paper is to develop a new empirical equation for predicting the ROP in real-time using different artificial intelligence (AI) techniques such as artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM). For the first time, poly diamond crystalline (PDC) bit design parameters, total flow area, in addition to mud density (MWin), gamma ray (GR), and drilling parameters were used to build the AI models. Actual field data was used to build the AI models (1000 data points from Well A) and another 972 data points from Well B were used for validating the developed AI models. The obtained results confirmed that the three AI techniques could be used to predict the ROP for complex lithologies with high accuracy. The ANN outperformed the SVM and ANFIS for predicting the ROP for the unseen data (972 data points of validation). The developed ROP-ANN model could be used to predict the ROP with high accuracy (the root mean square error (RMSE) was less than 0.659 for the available two wells). The developed empirical correlation was able to predict the ROP with high accuracy, RMSE was 0.66. The new ROP equation can be used without the need for the ANN Matlab code or special software.

Evaluating the effect of using micronised barite on the properties of water-based drilling fluids

Abdelmjeed Mohamed, Salaheldin Elkatatny, Mohamed Mahmoud, Reyad Shawabkeh and Abdulaziz Al-Majed
Presented at International Journal of Oil, Gas and Coal Technology 2020 25:1, 1-18
https://www.inderscienceonline.com/doi/pdf/10.1504/IJOGCT.2020.108851

Abstract

Solids sag and formation damage are serious problems encountered while drilling with barite-weighted drilling fluids. The objective of this study is to investigate the effect of reducing barite particle size on rheological properties, fluid stability, and filter cake removal. Barite samples with different particle sizes were prepared using sieve analysis and ball milling technique. Micronized barite (8 μm) showed a moderate stability with zeta potential measurements for a pH range greater than 8. Solubility tests showed good enhancement in barite removal as the particle size was reduced, with a difference of around 11 g/L between the largest size (75-106 μm) and the micronized size of barite. HPHT filtration test results confirmed the solubility results with 5 wt.% enhancement in filter cake removal efficiency as barite particle size was reduced to micronized size.

Presented at SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Calgary, Alberta, Canada, March 2004.

Abstract

Many of the world’s Mega-fields (> 1 billion barrels of reserves) contain sour gas, a blend of natural gas and hydrogen sulfide (H2S), either alone or in combination with carbon dioxide (CO2). H2S gas is extremely toxic, the combination of H2S and CO2 (Acid Gas – AG), can be highly corrosive, the elemental sulphur reacts with water to form acid rain, and CO2is now recognized as a significant green house gas. Where there is a demand for the natural gas, and capacity to separate the components, the H2S and CO2 can be separated out. However, these components must be managed in a cost-effect way and according to regulatory requirements to maximize recovery of hydrocarbons and minimize AG safety and environmental impacts. To date, the CO2 components have typically been vented to the atmosphere, and sulphur has been produced for industrial uses. Novel step changes are needed to handle the large sour gas volumes to be produced by the mega-fields under development in the Caspian Sea and Middle East regions Underground injection and sequestration of E&P associated streams(such as produced water, drill cuttings slurries, and production/completion return fluids) have been practiced for many years by the industry; sour gas injection is a relatively new technology. This paper reviews comparative economic and risk analyses of various H2S management options available to developing middle East and Caspian fields, considering:the impact on recovery (including rate, ultimate total volume, engineering capacity), the health and safety risks, impact on environment, reputation consequences, implementation (including construction and operation complexity), and regulatory Issues (based on current North American standards, recent judicial actions, and Kyoto Protocols). Emphasis is given to H2S gas injection in improved oil recovery (IOR) projects/schemes because: It is an environmentally sound solution that manages both H2S and other green house gases.; It provides for permanent, reliable storage and eliminates current taxation (in foreign countries) or future liability associated with emissions and/or surface storage of sulphur. For example, one operator in the Caspian Sea was recently fined several million dollars US because of sulphur storage above ground.; Compared with other options, H2S injection can reduce acid production, dust generation and avoid adverse impact on agriculture and aquaculture, thus it improve the operator’s image and reputation with local community.; It is the easiest sulphur-handling method to implement, as it uses established technologies, without requiring sulphur-separation plant and equipment.; H2S mixture has better sweeping efficiency than CO2 or sweet gas alone, therefore H2S injection may increase recoverable hydrocarbon in an EOR or IOR schemes.; It has the opportunity for favorable economics, both in terms of operating cost (lower energy, no storage operations) and by improving production through use in IOR schemes.

An Economic, Technical and Environmental Feasibility Study for Slurry Injection for Biosolids Management in the Dallas Fort Worth Metroplex

Panchal, Y.; Mohamed, I.M.; Pierce, Dale; Mounir, N.; Abou-Sayed, O.; Loloi; Abou-Sayed
The Journal of Solid Waste Technology and Management, Volume 46, Number 1, February 2020, pp. 24-35(12)
https://doi.org/10.5276/JSWTM/2020.24

Abstract

In Dallas Fort Worth (DFW), sewage is treated with a combination of anaerobic digestion, effluent filtration and lime stabilization to create biosolids which are then composted, landfilled, or land applied. The current treatment procedure has certain concerns including emissions or accumulation of odors, pathogens, nutrients, metals, and pharmaceutical products.

An alternative method, the Slurry Injection technique, enables the digestion of biosolids in the deep earth and can replace the current practice of wastewater treatment or disposal in a much more environmentally friendly and cost-efficient manner. By completely sequestering methane and CO2 into deep geologic formations which are produced as biosolids breakdown, reduces the greenhouse gas emissions and enables the operator to create greenhouse gas emission offset credits which can be marketed to offset the operating costs.

The economic, environmental, and technical aspects of building a new biosolids slurry injection facility in DFW, includes both the surface construction requirements as well as the subsurface strata evaluation for containment assurance. For the subsurface aspects, a geomechanical and stress analysis is performed on the Atoka formation (near the city of Fort Worth) and it confirms a confining layer above and below the injection zone to keep the waste contained for permanent storage.

Source: Petrophysics 60 (06): 825–837.

Abstract

Determination of the formation tops is an important and critical parameter while drilling a hydrocarbon well since it is one of the main factors affecting selection of the casing setting depths and drilling fluid design. During the field exploration and delineation phase and based on the geological data, the formation tops are estimated with low accuracy because of data limitations. In this study, a potential alternative technique for predicting formation tops is introduced. This technique involves application of artificial neural networks (ANN) and the use of a combination of the drilling mechanical parameters and the rate of penetration (ROP) to provide an accurate prediction of the formation tops. Incorporating the drilling mechanical parameters in this technique is suggested to help in predicting the true increase or decrease in the ROP regardless of the fluctuation on the other drilling parameters. Field data from two vertical oil wells (Well-A and Well-B) from the Middle East were used in this study. Seventy percent of the data from Well-A (4,436 data points) was used to train the ANN model, which was then tested on the remaining 30% of the data for Well-A (1,900 data points) and validated using the data from Well-B (6,569 data points). The sensitivity analysis confirmed that using a ANN model that consists of 25 neurons, one hidden layer, and with the Levenberg-Marquardt backpropagation function as the training function, is the optimum for predicting the formation tops with correlation coefficients (R) of 0.94 and 0.98 for the testing and validation data of Well-A and Well-B, respectively. The developed ANN model showed high accuracy in estimating the formation tops for both the testing and validation datasets of Well-A and Well-B, respectively.

Paper presented at the SPE Liquids-Rich Basins Conference – North America, Odessa, Texas, USA, November 2019.

Abstract

Hydraulic fracturing is applied ubiquitously in unconventional hydrocarbon reservoir development to increase the well productivity. To design a frac job, an injection falloff diagnostic test, such as minifrac test, is conducted first to determine key formation properties and frac operational parameters, including fracture closure pressure. In low permeability formations, the conventional pressure falloff analysis (i.e. G-function) is not practical to identify the fracture closure since it requires several days of well shut-in to collect enough pressure falloff data to reveal the fracture closure. In SPE-187495-PA, the authors show that it is possible to develop an empirical equation to predict the fracture closure pressure (Pc) from instantaneous shut-in pressure (ISIP), the first falloff data point, by regressive analysis on datasets from minifrac tests for conventional formations, including G-function estimated Pc, ISIP, petrophysical and mechanical properties. Since petrophysical and mechanical properties could be estimated from cores and wireline logs, the application of this equation requires a minifrac test to have a very short falloff period only to estimate ISIP. The objective of this work is to extend that equation for unconventional formations by introducing appropriate deviation terms for tight-sand and shale formations, respectively, in order to reduce the discrepancy between predicted Pc and G-function estimated Pc. To this end, several datasets, each of which contain the same attributes, are collected from publications for shale and tight-sand formations. Part of datasets are selected for developing respective deviation terms, for shale and tight-sand, to be added to the empirical equation, while the remaining datasets are used to test the respective new equation. Then a regression analysis is performed between Pc differences and the individual petrophysical and mechanical properties for shale and tight-sand datasets separately. Eventually, two deviation terms have been derived and incorporated to the empirical equation, one for shale and another one for tight formations. The deviation term for tight-sand formations correlate strongly with the rock mechanical properties, while the other with the rock mechanical properties and the formation porosity. Several field cases have been used to validate the empirical equation with respective deviation terms and the results show that the new formulae predict the fracture closure pressure with a relative absolute error less than 5% compared to those estimated from the G-function analysis for both the shale and the tight formations cases which are not used in developing respective deviation terms.

Paper presented at Petrophysics 60 (05): 660–674

Abstract

Formation density plays a central role to identify the types of downhole formations. It is measured in the field using density logging tool either via logging while drilling (LWD) or more commonly by wireline logging, after the formations have been drilled, because of operational limitations during the drilling process that prevent the immediate acquisition of formation density. The objective of this study is to develop a predictive tool for estimating the formation bulk density (RHOB) while drilling using artificial neural networks (ANN). The ANN model uses the drilling mechanical parameters as inputs and petrophysical well-log data for RHOB as outputs. These drilling mechanical parameters including the rate of penetration (ROP), weight on bit (WOB), torque (T), standpipe pressure (SPP) and rotating speed (RPM), are measured in real time during drilling operation and significantly affected by the formation types. A dataset of 2,400 data points obtained from horizontal wells was used for training the ANN model. The obtained dataset has been divided into a 70:30 ratio for training and testing the model, respectively. The results showed a high match with a correlation coefficient (R) between the predicted and the measured RHOB of 0.95 and an average absolute percentage error (AAPE) of 0.71%. These results demonstrated the ability of the developed ANN model to predict RHOB while drilling based on the drilling mechanical parameters using an accurate and low-cost tool. The black-box mode of the developed ANN model was converted into white-box mode by extracting a new ANN-based correlation to calculate RHOB directly without the need to run the ANN model. The new model can help geologists to identify the formations while drilling. Also, by tracking the RHOB trends obtained from the model it helps drilling engineers avoid many interrupting problems by detecting hazardous formations, such as overpressured zones, and identifying the well path, especially while drilling horizontal sections. In addition, the continuous profile of RHOB obtained from the developed ANN model can be used as a reference to solve the problem of missing and false logging data.

Presented at: International Petroleum Technology Conference, Beijing, China, March 2019.

Abstract

Pressure transient analysis provides useful information to evaluate injection induced fracture geometry, permeability damage near wellbore and pressure elevation in injection zone. Manual analysis of pressure data after each injection cycle could be subjective and time-consuming. In this study a cloud-based approach to automatically analyze pressure data will be presented, which is aimed to improve the reliability and efficiency of pressure transient analysis. There are two fundamental requirements for the automated pressure transient analysis: 1) Pressure data needs to be automatically retrieved from field sites and fed to analyzer; 2) Analyzer can automatically select instantaneous shut-in pressure (ISIP), identify flow regimes, and determine fracture closure point. To meet these requirements and also take the advantages of cloud storage and computing technologies, a web based application has been developed to pull real time injection data from any field sites and push it to a cloud database. Besides analyzing any existing pressure data in the cloud database, a built-in pressure transient analyzer can also detect any real-time pressure data and perform pressure analysis automatically when required data is available. The automated, cloud-based pressure transient analysis has been applied to multiple injection projects. In general, the analysis results including permeability, fracture half length, skin factor, and fracture closure pressure are comparable to these yielded from manual analysis. The discrepancy is mainly caused by poor data quality. The inconsistent selections of ISIP and different slopes defined for G-function and flow regime analyses also contribute to the divergence. Overall, the automated pressure transient analysis provides consistent results as the exact same criteria are applied to the pressure data, and analysis results are independent on analyzer’s experience and knowledge. In addition, machine learning algorithms are applied to continuously refine the criteria and improve the quality of analysis results. As data from oil/gas industry increases exponentially over time, automated data transmission, storage, analysis and access are essential to maximize the value of the data and reduce operation cost. The automated pressure transient analysis presented here demonstrates that cloud storage and computing combined with automated analysis tools is an optimal way to overcome big data challenges facing by oil/gas industry.