Automated Sample Preparation and Data Collection Workflow for High-Throughput In Vitro Metabolomics

Regulatory bodies have started to recognise the value of in vitro screening and metabolomics as two types of new approach methodologies (NAMs) for chemical risk assessments, yet few high-throughput in vitro toxicometabolomics studies have been reported. A significant challenge is to implement automated sample preparation of the low biomass samples typically used for in vitro screening. Building on previous work, we have developed, characterised and demonstrated an automated sample preparation and analysis workflow for in vitro metabolomics of HepaRG cells in 96-well microplates using a Biomek i7 Hybrid Workstation (Beckman Coulter) and Orbitrap Elite (Thermo Scientific) high-resolution nanoelectrospray direct infusion mass spectrometry (nESI-DIMS), across polar metabolites and lipids. The experimental conditions evaluated included the day of metabolite extraction, order of extraction of samples in 96-well microplates, position of the 96-well microplate on the instrument’s deck and well location within a microplate. By using the median relative standard deviation (mRSD (%)) of spectral features, we have demonstrated good repeatability of the workflow (final mRSD < 30%) with a low percentage of features outside the threshold applied for statistical analysis. To improve the quality of the automated workflow further, small method modifications were made and then applied to a large cohort study (4860 sample infusions across three nESI-DIMS assays), which confirmed very high repeatability of the whole workflow from cell culturing to metabolite measurements, whilst providing a significant improvement in sample throughput. It is envisioned that the automated in vitro metabolomics workflow will help to advance the application of metabolomics (as a part of NAMs) in chemical safety, primarily as an approach for high throughput screening and prioritisation.

Influence of Injection Well Location on CO2 Geological Storage Efficiency

An analysis of the influence of injection well location on CO2 storage efficiency was carried out for three well-known geological structures (traps) in deep aquifers of the Lower Jurassic Polish Lowlands. Geological models of the structures were used to simulate CO2 injection at fifty different injection well locations. A computer simulation showed that the dynamic CO2 storage capacity varies depending on the injection well location. It was found that the CO2 storage efficiency for structures with good reservoir properties increases with increasing distance of the injection well from the top of the structure and with increasing depth difference to the top of the structure. The opposite is true for a structure with poor reservoir properties. As the quality of the petrophysical reservoir parameters (porosity and permeability) improves, the location of the injection well becomes more important when assessing the CO2 storage efficiency. Maps of dynamic CO2 storage capacity and CO2 storage efficiency are interesting tools to determine the best location of a carbon dioxide injection well in terms of gas storage capacity.

Proactive Decision-Making Through Real-Time Geomechanical Support Leveraging Drilling a Long Horizontal Section Through a Tight Unconventional Reservoir of an Oil and Gas Field: Middle East

This paper discusses the Integrated Role of Geomechanics and Drilling Fluids Design for drilling a well oriented towards the minimum horizontal stress direction in a depleted, yet highly stressed and complex clastic reservoir. There are multiple challenges related to such a well that need to be addressed during the planning phase. In this case, the well needs to be drilled towards the minimum horizontal stress direction (Shmin) to benefit multi-stage hydraulic fracturing. At the same time, the most prominent challenge is that this well orientation is more prone to wellbore failure and requires a maximum mud weight, due to the present strike slip stress environment. Well planning challenges in such an environment include (a) the determination of formation characteristics and rock properties, (b) the anticipation of higher formation collapse pressure during the course of drilling the lateral section within the reservoir, (c) the determination of the upper bound mud weight to prevent lost circulation due to a low fracture gradient against depleted sections, or due to the presence of pre-existing natural fractures, d) mitigating the higher risk of differential sticking against depleted porous layers, and determining appropriate bridging in the drilling fluids, (e) recognizing the prolonged exposure time of the formation due to the length of the lateral and the lower rate of penetration against the tight highly dense formations. For successful drilling, and to mitigate the above risks, the first step is to prepare a predrill GeoMechanical model along with adequate fluid design and drillers action plans to be considered during drilling. Offset well petrophysical logs and core data are considered for the preparation of the predrill GeoMechanical model, along with the drilling experiences in the offset locations. Based on the above, a predrill GeoMechanical model is prepared, a risk matrix is being established, and a representative mud weight window is recommended (Wellbore Stability Analysis). In most cases, the offset well locations considered are vertical- or inclined-, or lateral wells of different trajectory azimuth than the target well location and the predrill GeoMechanical model can incorporate such variations easily; however, any Geology uncertainty, leading to a different rock property- and stress set-up (or even different pore pressure than expected), at the actual well location will be part of the uncertainty of the predrill GeoMechanical model and Wellbore Stability Analysis. This is where the real time monitoring is playing out its full potential: giving an updated model and wellbore stability analysis during drilling. While drilling the lateral section, the wellbore condition is being monitored using LWD (logging while drilling) tools, e.g. Gamma Ray, Density, Neutron, Acoustic Caliper, Azimuthal density image and ECD (equivalent circulating density). While gamma ray helps in determining the lithology, density logs help to understand the formation hardness, and they can be used to generate a calibrated pseudo acoustic log. Based on this pseudo acoustic log, the rock strength and other rock mechanical properties of the pre- GeoMechanical model can be updated as soon as they become available. This gives insight into the model differences and helps to understand model variations and adjust Wellbore Stability recommendations accordingly. While the neutron log helps to determine the zones of high porosity, and thus potential risk zones for differential sticking, the azimuthal density image clearly indicates the breakout zones caused by the shear failure of the wellbore. The presence of wellbore failure (breakout) is further confirmed by acoustic caliper data, and accordingly wellbore stability related recommendations are communicated to the operator, for an increase in the specific gravity of the mud, and thus, to balance the wellbore. From a mud rheology perspective, high performance OBM (oil-based mud) parameters are maintained consistent with the formation properties, to minimize fluid loss, optimize wellbore strengthening characteristics and minimize at the same time solids concentrations in order to avoid excessive ECD (equivalent circulating density) which may open pre-existing natural fractures resulting in downhole losses and in consequence might lead to differential sticking. In the case study presented herein, the proactive implementation of GeoMechanics and its Wellbore Stability application as well as the integration of drilling fluids services, resulted in the smooth and successful drilling of the lateral section, and also in the delivery of an in gauge hole necessary for multi-stage fracturing (MSF) completion optimization.

Paradigm Shift in Drilling to Completion in Unconventional Reservoir, Eastern Onshore, India

Permo-Triassic formations in Mandapetta field from eastern onshore, India possesses historical drilling challenges in terms of wellbore instability, non-productive time and poor hole condition in deep higher stressed formations. Lack of acquiring reliable log data and problems in recovering good quality cores present difficulties in proper formation evaluation and zone selection for testing. Historical well test results in target K-Formation has been not encouraging despite good gas shows during drilling. Estimated formation pressure gradient ranges 1.45sg-1.52sg. Layered shale with coal and tight sandstone in same open hole section pose risks of mud losses and poor cement job. Present study highlights the workflow adopted to improve drilling and completion in open hole section of more than 1000 m with varying lithology being drilled successfully. Advanced 3D anisotropic acoustic measurements acquired are used to estimate anisotropic elastic properties (vertical and horizontal Young's modulus and Poisson's ratio) in the overlying shales. Horizontal tectonics has been determined across stress induced anisotropic layers. This approach provides better understanding of formations and stress distribution. Thomsen Gamma values range 0.1 to 0.4 in shale layers of overburden formations. In order to minimize uncertainty in 8.5inch section while drilling, advanced logs were acquired in 12.25inch hole section to estimate tectonics at well location while constraining ratio of horizontal to vertical Young's modulus and Poisson Ratio in shale layers based on Thomsen Gamma and clay volume. Analysis suggested typical VTI anisotropy of 15%-20% in shale layers. Inverted direct horizontal strain parameters at well location suggested the ratio of maximum to minimum horizontal stress to vary 1.15-1.23. Mud weight used while drilling 8.5inch section ranged 1.49sg1.52sg against the recommended mud weight of 1.50sg-1.52sg while pumping sealing agents to prevent mud losses in coal layers. Flow rate was maintained on lower values to minimize ECD values. Hole condition improved significantly with no issues in logging. Post-drill anisotropic rock mechanics model suggested good quality sandstone in target source formation with usual conventional reservoir in shallower formation. Zone was selected based on permeability, breakdown and completion quality for perforations. Analysis of high-quality sonic slowness helped to identify possible gas reservoir in laminated source rock. There was stress contrast of 2000psi-2500psi among reservoir layers and shale stress barriers. Implemented workflow and successful execution helped to drill the well 5 days earlier than plan with no major drilling incidents. Successful core recovery for Shale Gas evaluation was also possible due to better wellbore quality. Initial testing of K-Formation produced gas with significant improved flow rate by 150% without any stimulation for the 1st time in the history of the field.

An Efficient Bi-Objective Optimization Workflow Using the Distributed Quasi-Newton Method and Its Application to Well-Location Optimization

Summary Although it is possible to apply traditional optimization algorithms to determine the Pareto front of a multiobjective optimization problem, the computational cost is extremely high when the objective function evaluation requires solving a complex reservoir simulation problem and optimization cannot benefit from adjoint-based gradients. This paper proposes a novel workflow to solve bi-objective optimization problems using the distributed quasi-Newton (DQN) method, which is a well-parallelized and derivative-free optimization (DFO) method. Numerical tests confirm that the DQN method performs efficiently and robustly. The efficiency of the DQN optimizer stems from a distributed computing mechanism that effectively shares the available information discovered in prior iterations. Rather than performing multiple quasi-Newton optimization tasks in isolation, simulation results are shared among distinct DQN optimization tasks or threads. In this paper, the DQN method is applied to the optimization of a weighted average of two objectives, using different weighting factors for different optimization threads. In each iteration, the DQN optimizer generates an ensemble of search points (or simulation cases) in parallel, and a set of nondominated points is updated accordingly. Different DQN optimization threads, which use the same set of simulation results but different weighting factors in their objective functions, converge to different optima of the weighted average objective function. The nondominated points found in the last iteration form a set of Pareto-optimal solutions. Robustness as well as efficiency of the DQN optimizer originates from reliance on a large, shared set of intermediate search points. On the one hand, this set of searching points is (much) smaller than the combined sets needed if all optimizations with different weighting factors would be executed separately; on the other hand, the size of this set produces a high fault tolerance, which means even if some simulations fail at a given iteration, the DQN method’s distributed-parallelinformation-sharing protocol is designed and implemented such that the optimization process can still proceed to the next iteration. The proposed DQN optimization method is first validated on synthetic examples with analytical objective functions. Then, it is tested on well-location optimization (WLO) problems by maximizing the oil production and minimizing the water production. Furthermore, the proposed method is benchmarked against a bi-objective implementation of the mesh adaptive direct search (MADS) method, and the numerical results reinforce the auspicious computational attributes of DQN observed for the test problems. To the best of our knowledge, this is the first time that a well-parallelized and derivative-free DQN optimization method has been developed and tested on bi-objective optimization problems. The methodology proposed can help improve efficiency and robustness in solving complicated bi-objective optimization problems by taking advantage of model-based search algorithms with an effective information-sharing mechanism. NOTE: This paper is published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

Semi-analytical model of multiple fractured horizontal well across a discontinuity in a linear composite reservoir

Many geologic settings can be treated as linear composite (LC) reservoirs, where linear discontinuities divide the formation into multiple zones with different properties. Although there have been many studies on pressure behavior of production wells in an LC reservoir, most of the studies focus on vertical wells. The modeling of multiple fractured horizontal (MFH) wells in an LC reservoir remains limited. The goal of the present work is to propose a general semi-analytical model of an MFH well situated anywhere in a two-zone LC reservoir. This model can take into account the situation where the horizontal well intersects with the discontinuity and hydraulic fractures are distributed in both the two zones. According to the point-source function method, the semi-analytical solution for an MFH well in LC reservoirs is derived by using superposition principle, fracture discrete scheme and numerical inversion algorithm of Laplace transformation. Type curves of MFH wells far away from a discontinuity and across a discontinuity in an LC reservoir are drawn and analysed, respectively. Furthermore, the effects of some parameters on pressure behavior and rate response of an MFH well across a discontinuity are studied. This research finds that the pressure behavior and rate response of an MFH well across a discontinuity are significantly affected by the well location, properties of hydraulic fractures and formation properties.

Fast Optimization of Injector Selection for Waterflood, CO2-EOR and Storage Using an Innovative Machine Learning Framework

Optimal injector selection is a key oilfield development endeavor that can be computationally costly. Methods proposed in the literature to reduce the number of function evaluations are often designed for pattern level analysis and do not scale easily to full field analysis. These methods are rarely applied to both water and miscible gas floods with carbon storage objectives; reservoir management decision making under geological uncertainty is also relatively underexplored. In this work, several innovations are proposed to efficiently determine the optimal injector location under geological uncertainty. A geomodel ensemble is prepared in order to capture the range of geological uncertainty. In these models, the reservoir is divided into multiple well regions that are delineated through spatial clustering. Streamline simulation results are used to train a meta-learner proxy. A posterior sampling algorithm evaluates injector locations across multiple geological realizations. The proposed methodology was applied to a producing field in Asia. The proxy predicted optimal injector locations for water and CO2 EOR and storage floods within several seconds (94–98% R2 scores). Blind tests with geomodels not used in training yielded accuracies greater than 90% (R2 scores). Posterior sampling selected optimal injection locations within minutes compared to hours using numerical simulation. This methodology enabled the rapid evaluation of injector well location for a variety of flood projects. This will aid reservoir managers to rapidly make field development decisions for field scale injection and storage projects under geological uncertainty.

Optimization approach for Arctic field development design using subsea production systems

Russian experience in the design of trunk pipelines and Arctic studies have been used to develop an efficient model and method for Arctic field development design using the subsea production system (SPS). Compared to 2D models used in the past, the new design technique offers an opportunity to make 3D models and can be used for optimization of offshore field development projects. The proposed optimization model is based on the Bellman - Ford algorithm developed for 3D networks. This approach has been used for the first time to capture key features and specific subsea production system design processes. The algorithm and block diagrams developed for the proposed SPS design method is universal. This method can be used to address tasks of a more general nature. Optimization of the particular case between a single start point (well location) and single end point (SPS facility) is implemented as a separate software package, but the scope of applications is not limited by such cases and may be extended even further. It can also be very efficient for Arctic subsea field development.