Coupled experimental assessment and machine learning prediction of mechanical integrity of MICP and cement paste as underground plugging materials

Compromised integrity of cementitious materials can lead to potential geo-hazards such as detrimental fluid flow to the wellbore(borehole),potential leakage of underground stored fluids,contamination of water aquifers,and other issues that could impact environmental sustainability during underground construction operations.The mechanical integrity of wellbore cementitious materials is critical to prevent wellbore failure and leakages,and thus,it is imperative to understand and predict the integrity of oilwell cement(OWC)and microbial-induced calcite precipitation(MICP)to maintain wellbore integrity and ensure zonal isolation at depth.Here,we investigated the mechanical integrity of two cementitious materials(MICP and OWC),and assessed their potential for plugging leakages around the wellbore.Further,we applied Machine Learning(ML)models to upscale and predict near-wellbore mechanical integrity at macro-scale by adopting two ML algorithms,Artificial Neural Network(ANN)and Random Forest(RF),using 100 datasets(containing 100 observations).Fractured portions of rock specimens were treated with MICP and OWC,respectively,and their resultant mechanical integrity(unconfined compressive strength,UCS;fracture toughness,K_(s))were evaluated using experimental mechanical tests and ML models.The experimental results showed that although OWC(average UCS=97 MPa,K_(s)=4.3 MPa·√m)has higher mechanical integrity over MICP(average UCS=86 MPa,K_(s)=3.6 MPa·√m),the MICP showed an edge over OWC in sealing microfractures and micro-leakage pathways.Also,the OWC can provide a greater near-wellbore seal than MICP for casing-cement or cement-formation delamination with relatively greater mechanical integrity.The results show that the degree of correlation between the mechanical integrity obtained from lab tests and the ML predictions is high.The best ML algorithm to predict the macro-scale mechanical integrity of a MICP-cemented specimen is the RF model(R^(2)for UCS=0.9738 and K_(s)=0.9988;MAE for UCS=1.04 MPa and K_(s)=0.02 MPa·√m).Similarly,for OWC-cemented specimen,the best ML algorithm to predict their macro-scale mechanical integrity is the RF model(R^(2)for UCS=0.9984 and K_(s)=0.9996;MAE for UCS=0.5 MPa and K_(s)=0.01 MPa·√m).This study provides insights into the potential of MICP and OWC as near-wellbore ce-mentitious materials and the applicability of ML model for evaluating and predicting the mechanical integrity of cementitious materials used in near-wellbore to achieve efficient geo-hazard mitigation and environmental protection in engineering and underground operations.

Emergence of nanotechnology in the oil and gas industry:Emphasis on the application of silica nanoparticles

The application of nanotechnology in the oil and gas industry is on the rise as evidenced by the number of researches undertaken in the past few years.The quest to develop more game-changing technologies that can address the challenges currently facing the industry has spurred this growth.Several nanoparticles,of different sizes and at different concentrations,have been used in many investigations.In this work,the scope of the study covered the application of nanotechnology in drilling and hydraulic fracturing fluids,oilwell cementing,enhanced oil recovery(which includes transport study,and foam and emulsion stability),corrosion inhibition,logging operations,formation fines control during production,heavy oil viscosity reduction,hydrocarbon detection,methane release from gas hydrates,and drag reduction in porous media.The observed challenges associated with the use of nanoparticles are their stability in a liquid medium and transportability in reservoir rocks.The addition of viscosifier was implemented by researchers to ensure stability,and also,surface-treated nanoparticles have been used to facilitate stability and transportability.For the purpose of achieving better performance or new application,studies on synergistic effects are suggested for investigation in future nanotechnology research.The resulting technology from the synergistic studies may reinforce the current and future nanotechnology applications in the oil and gas industry,especially for high pressure and high temperature(HPHT)applications.To date,majority of the oil and gas industry nanotechnology publications are reports of laboratory experimental work;therefore,more field trials are recommended for further advancement of nanotechnology in this industry.Usually,nanoparticles are expensive;so,it will be cost beneficial to use the lowest nanoparticles concentration possible while still achieving an acceptable level of a desired performance.Hence,optimization studies are also recommended for examination in future nanotechnology research.