Abstract This study aims at evaluating the mechanical integrity and the changes in the pore system of illitic sandstone formations being treated with HF acid generated in-situ and thermochemical fluids ( TCF ). Firstly, the acid-generating fluids were injected separately inside the core within a typical core flooding experiment. After that the core permeability, NMR porosity and rock static and dynamic parameters such as rock strength ( UCS ), dynamic Young's modulus, Poisson's ratio, shear modulus and bulk compressibility were evaluated before and after the treatment. Sand production prediction model was used to examine the potential of sand production problems for the cores treated with the in-situ generated acid ( ISGA ) and the TCF . Treating Scioto cores with the ISGA only led to permeability enhancement with final to initial permeability ratio K f i n a l K i n i t i a l of 1.2. In addition, treating the same cores with acid generating fluids and thermochemicals resulted in higher permeability enhancement with K f i n a l K i n i t i a l of 485. This higher enhancement in the formation permeability was attributed to the creation of some microfractures as was proved using the NMR scans. There was a reduction of the cores' static and dynamic parameters after being treated with the ISGA. This reduction was even higher after treating the core with thermochemical fluids. However, the sand production prediction model stated that the reduction in rock mechanical parameters would not lead to sand production problems.

Abstract Data uncertainty adds extra complexity in reservoir simulation of large and heterogeneous fields. Such complexity leads to higher consumption of time and effort in optimization study, e.g. in determining optimum well placement. Developing a systematic methodology to deal with any data uncertainty is critical to improve the reliability of such optimization study. In this study, we present a new methodology to optimize well placement configuration under uncertainty in waterflood design. As the waterflood performance is highly influenced by its timing, multiple scenarios were defined with varying injection starting time. Through this new methodology, we have successfully optimized all scenarios and determined the optimum time to start the waterflood.

Waterflood conformance is a significant problem in the West Sak field of Alaska. Re-assembling Pre-Formed Particle Gels (RPPG) have been used to treat Void Space Conduits (VSC) and repair the "short-circuited" waterflood. These VSC’s are typically formed by sand producing wormholes. Several dozen VSC solutions have been implemented since 2006, including molten wax, cement blends, and pre-formed particle gels. To date, all the solutions have been faced with various limitations due to the low reservoir temperature and poor sand consolidation. A good percentage of the pre-formed particle gel (PPG) solutions have been successful in sealing off VSCs, but often show limited longevity that can range from as little as several weeks to several years. An effort was undertaken to develop a product that would provide enhanced stability in the VSC and extend solution life beyond the current range. A cost-effective conformance solution was developed with increased mechanical strength through a re-crosslinking process known as RPPG. The goal of the RPPG solution is to provide a longer-term repair to a VSC and restore the primary water flood characteristics. This paper will present the results of the seventeen RPPG solutions that have been pumped between 2017 and 2019 in the West Sak field on the North Slope of Alaska. RPPG treatments have shown a 23% improvement over traditional PPG treatments at the economic payout during the field trial. There have been some significant learnings through this time period. The focus of this paper will be to review the VSC problem understanding and the reason for the RPPG product development, detail the evolution of RPPG job design over time and provide results and operational knowledge from the field trial efforts. The latest advancements in conformance engineering will be of interest to Reservoir and Production Engineers who are focused on supporting and optimizing conventional waterflooded assets, particularly when faced with conformance issues in an unconsolidated reservoir. In addition, the field results will be useful to those seeking to implement RPPG solutions in their area of operation.

What is the next best drilling decision to make in a Field Development Plan (FDP)? This is the key question we address in this work. A FDP consists of a sequence of decisions. Each action we take affects the reservoir and conditions any future decision. The novelty of our proposed approach is the consideration of the sequential nature of the decisions through the framework of Dynamic Programming (DP) and Reinforcement Learning (RL). In this framework, each scheduled drilling decision depends on the observations acquired between drillings. This methodology allows moving the focus from a static Field Development Plan optimization to a more dynamic framework that we call Field Development Policy Optimization. In addition to the formulation of this new framework, we have applied this methodology to optimize the development of a real oil and gas field. We model the FDP optimization problem under subsurface uncertainty as a Partially Observable Markov Decision Process (POMDP) and solve it through a RL algorithm in order to find an optimal drilling policy. Our methodology works for a general reservoir with a given set of geological model representing. To speed up the learning process we utilize a trained Deep Recurrent Neural Network (RNN) to approximate the reservoir simulator flows, which are subsequently used to compute the economic performance of a drilling policy through its discounted cash flows. The RNN is trained and tested on a set of reservoir simulator runs over randomly sampled realizations of our reservoir model, well location, type and control sequences of a drilling plan. From all the possible decisions involved in a FDP, we focus here only on finding optimal adaptive well drilling schedules (locations of vertical wells and well types). The RL agent learns the best drilling schedule policy by generating simulated episodes of experience and iteratively improving the policy using a Q value function approximated by a neural network trained across episodes. The final solution consists of an adaptive Field Development Plan yielding the highest expected Net Present Value (NPV), computed within a given, available time budget. It specifies an adaptive drilling schedule of producer and injector, well locations and well controls, as a function of the information obtained at each drilling step. The methodology has been applied to an actual reservoir for infill well location decisions. In this case, our objective is finding the best well placement and well type for the next producer and injector wells, as well as the optimization of the control schedule for new and preexisting wells in the reservoir. Our results show the learning progress of our RL algorithm until finding the optimal drilling plan. The robustness of the solution is evaluated across the best-trained policies. Methodology and results have been validated using a brute force sampling approach. Both RL and brute force approaches were possible due to our fast-to-compute RNN approximation of the reservoir simulator. This work represents, to our knowledge, the first application of an end-to-end AI workflow for Field Development Policy Evaluation in real fields, based on Reinforcement Learning and Deep Learning. The proposed methodology puts together an optimal field evaluation in planning and a surveillance workflow for a reactive decision-making.

Analysis of fracturing water flowback data from hydraulic fractures in unconventional reservoirs plays an important role in determining key fracture properties and predicting future well performance. Current flowback data analysis tools mostly rely on empirical correlations and/or simplified models. This paper presents a semi-analytical, multiphase solution that is applicable to the analysis of not only water flowback but early gas production data of multi-fractured horizontal gas wells. The proposed solution couples a Green's function-based analytical solution for gas flow in matrix with a finite-difference numerical solution for two-phase (gas and water) flow in fracture domain. The validity of proposed semi-analytical solution is verified against simulation results generated using commercial numerical simulators. Inverse analysis techniques are developed based on the proposed semi-analytical solution and presented in the form of type curves. Synthetic case studies show that the proposed type-curve methods can provide explicit and accurate estimation of fracture and reservoir properties, including fracture conductivity, and fracture pore volume. For all case tested, the proposed model is shown to provide accurate and consistent prediction of reservoir performance and estimations of fracture properties while maintaining the simplicity of explicit, iteration-free calculations.

Yan’an gas field is a typical tight sandstone reservoir with multiple gas layers, large vertical span, variable size of effective sandbody, high heterogeneity, and complex surface ground environment. Due to the depositional background including both continental and marine face, the tight gas reservoir shows marginal characteristics with low porosity/permeability, thin sandbody thickness and low lateral continuity. If the field is recovered by the current mainstream technology "horizontal drilling+multi-stages hydraulic fracturing", the subsurface reserves will not be controlled fully, resulting in low economic benefit. With over a decade of practice, a cost-effective hybrid well pattern adapted to each layer and each region was formed which shows its efficiency in development of Yan’an gas field. In the hybrid well pattern, directional (vertical) well is adopted to fully control the reserves of the four main gas-bearing layers, while horizontal well is adopted to maximize the production for the reserve-enriched layers. Unlike the uniform well spacing for the marine tight gas reservoir, the well spacing in Yan’an gas field is non-uniform to adapt the strong heterogeneity in terms of storage and percolation, and the methodology to quantify the well spacing according to the local reservoir property was discussed in detail. To decrease the sand-lost drilling risk in such a low sand-continuity reservoir, the strategy named "sand recognition while drilling" is proposed for the well placement. The mathematical models for designing the directional (vertical) wells and horizontal wells are also presented in this work. In addition, the non-technical factors such as loess plateau topography and coal/water/oil resources overlay give large difficulties to open the well site and extended reach wells are required, and the criterion in selection different types of well group was given mathematically as well. The above factors in constraint the well pattern are summarized with property constraint, local constraint, macro constraint and environment constraint. The hybrid well pattern successfully unlocked and maximized the marginal gas field.

Tight rock reservoirs have gained popularity and become a subject of great interest due to their huge recovery potential. A substantial portion of the potential hydrocarbon could be removed from the reservoir by injecting solvent gases (hydrocarbon or CO 2 ) as an Enhanced Oil Recovery (EOR) application. Achieving a precise modeling of these processes and accurate description of hydrocarbon dynamics requires a clear understanding of phase-change behaviour in confined (capillary) medium. It was previously shown that early vaporization of liquids could occur in channels that were larger than 1000 nanometers. The surface wettability plays a critical role in influencing the vaporization and condensation nature in confined systems. This paper studies the influence of the medium wettability on phase-transition temperatures of liquid hydrocarbons in macro (> 1000 nm) and nano (< 500 nm) channels by using different types of rock samples. The boiling temperature of hydrocarbon solvents was measured in two extreme wetting conditions: (1) strongly water wet, and (2) strongly oil wet. Boiling temperatures of heptane and octane in sandstone, limestone, and tight sandstone were observed to be lower than their bulk boiling points by closely 13%, on average. Altering rock wettability characteristically changes the average hydrocarbon nucleation temperatures being as critical as the pore size. The experimental outcomes also showed that reducing the solvent adsorption on clays in Berea sandstone lowers the nucleation temperature of heptane and octane from their normal phase-change temperatures by 30%.

Systematic and comprehensive analysis of gas lift operations must include historical performance evaluation and heuristic diagnostics for identification of suboptimal performers. Reliable nodal analysis models are necessary to optimize the gas lift systems. Though some degree of automation is currently possible, this process still takes substantial effort and time. The work presented here is a streamlined, repeatable, and automated workflow to diagnose and optimize gas lift operation through analytics, reliable wellbore modeling and calibration that reduces the time from days to hours. The workflow is also very easily repeatable, which makes it very efficient to update when new data is available. The workflow combines the emerging data analytics efforts in the oil and gas industry along with traditional gas lift optimization guidelines to identify artificial lift-related key recovery obstacles and corresponding development plans. This novel combined approach proves the effectiveness and necessity of augmented artificial intelligence, and the case study of field analysis and execution further endorse the impact of such optimization workflow for gas lift assessment and operations. It builds from data-driven and engineering-based workflows that compute smart metrics and dashboards based on the historical well production and the gas lift systems performance. Several KPIs and metrics are utilized, some of which are industry standard while others are intuitively built exclusively for this workflow. In this paper, we present the results and challenges in applying this workflow to an offshore Middle East oilfield, with over 50 gas lift wells. As always with field data, several aspects of the data had to be addressed. Accuracy and relevancy of data was questionable. We developed several workflows to address the missing/incorrect data to ensure the robustness and credibility of the results from the workflow. Due to the practical implication of the analysis, stringent quality control and error margins were maintained throughout the analysis. No nodal analysis models were available, consequently models were built and calibrated as a part of this effort. Overall field and facility constraints were also incorporated to ensure results were achievable in the field. The optimized operational plan would yield an additional 15 to 20% gains over current production rate. Further benefits by upgrading the gas lift design are also quantified using the proposed workflow. The time required to construct, calibrate, and optimize around 50 wells was less than two weeks.

Recent industry analysis based on publicly available production data of most unconventional basins in the US have consistently highlighted the underperformance of child wells as compared to parent wells, although completion practices have continuously evolved. Industry publications have suggested that average productivity degradation of child wells can be up to 29% for some Delaware Basin operators. In some cases, the detrimental effects of parent-child relationships have also been observed on the parent wells after the stimulation of the child wells. In such an environment it is important to develop completion strategies to mitigate the negative effects of this parent-child relationship. In the Delaware Basin, the negative parent-child effect was successfully mitigated on two different zipper pads, with parent wells as close as 500 ft away from the zippered child wells. On the first pad, one parent well was completed and six months later two child wells were zippered with the closest child 1,000 ft away from the parent and pumped with far-field diversion. On the second pad, one parent well was completed and four months later three child wells were zippered with the closest child well 500 ft away from parent and far-field diversion pumped on the two closest child wells. The stimulation treatment design was carefully designed to include far-field diverters on the stages near parent wells. Job size and far-field diverter quantity were determined using an integrated hydraulic fracture simulation software with an advanced particle transport model. Contingency scenarios were also prepared to facilitate real-time changes required when or if abnormal behavior was observed during the execution. The zipper sequence was also planned to help establish a stress-shadow effect near the parent well to further mitigate detrimental parent-child interactions. To monitor execution in real time and evaluate interactions between wells, high-frequency pressure gauges were installed on all observation wells including parent and child wells. The completion design and far-field diversion treatment worked as planned for the first pad, with no significant well interference pressure signature observed on the monitoring well. For the second pad, the parent well saw pressure increases up to 700 psi during the treatment of a stage midway along the lateral of the closest child well which was completed with far-field diverter. Contingency plans were successfully executed, and no significant pressure increase was observed on the remainder of the lateral. Early production results indicate that the negative impacts of parent-child interactions were successfully mitigated on both pads, with the production of the parent wells quickly returned to their observed trends prior to child wells stimulation. Child wells production, when normalized both by lateral length and stimulation size, was on par with that of the parent well.

Flowback rate transient analysis (RTA) is a practical tool for characterizing hydraulic fracture (HF) properties. However, the accuracy of the interpreted results from flowback RTA is challenged by the complexity in two-phase flow in the hydraulic fracture and matrix system. Accordingly, we present a new semianalytical method to characterize HF attributes and dynamics using multi-phase flowback data for tight and ultratight (shale) oil wells. The proposed method includes a two-phase diagnostic plot, a fracture RTA approach for straight-line analysis, and a matrix model capable of characterizing water and oil flow. The RTA approach is based on fracture infinite acting linear flow (IALF) and boundary dominated flow (BDF) solutions, which treats HF as an open tank with a variable production rate at the well and the contribution of water and oil from matrix within the distance of investigation (DOI). The pressure-dependent fluid and geomechanical properties, such as permeability and porosity, are considered in the pseudotime defined in fracture and matrix to reduce the nonlinearity of the system. We tested the accuracy of the proposed method against numerical results obtained from commercial software and verified its applicability by analyzing the flowback and long-term production data from a field example in Eagle Ford shale. The validation results confirm that our method can closely calculate water and oil influx from matrix as well as the average pressure and saturation in the HF and matrix DOI. The accurate estimation of the initial fracture permeability and pore volume demonstrates the applicability of the proposed method in quantifying HF properties from two-phase flowback data exhibiting fracture IALF and BDF regimes. The analysis results show that the estimated initial fracture pore volume shows more accuracy than initial fracture permeability due to the different calculation sources in the straight-line analysis. In short, the proposed method is, to our best knowledge, the first RTA approach incorporating the two-phase water and oil influx from matrix into the inverse analysis of fracture properties and dynamics using straight-line analysis, instead of history matching

The authors have developed an algorithm that combines simple, proven, analytical work tools. The algorithm allows one to quickly update the potential, taking into account geological and technical factors, assess risks, estimate current recoverable reserves, incremental oil production, and economics for each well. The purpose of the work is to reduce routine labor costs when searching for and selecting wells, to rank the entire list of relevant candidates, to increase the share of successful operations, as well as to increase incremental oil production by accelerating well optimization with minimal risk. A pilot project helped to improve the success rate of optimization jobs from 75 to 89%, and increase specific incremental oil production by 57% in comparison with the previous year. The tool can also be used for auditing decisions as a retrospective.

Along with the maturation of main oil exploration and production (E&P) areas in the world, the industry has started to seek for new investment opportunities that have been considered unattractive so far. Among such opportunities might be small oil fields, whose low resource base makes the development marginal in terms of value. Such discoveries remain risky due to lower benefit–cost ratios and geological uncertainty that cannot be reduced so that the downside risk is eliminated. Nevertheless, they still can be attractive investment opportunities if the decision making process captures available operational flexibilities and opportunities to gather additional information allowing to tune the development plan during the course of a project. This can allow to mitigate downside risks associated with the reservoir uncertainty. In order to capture additional flexibilities and account for a possibility to change the course of the project within the investment valuation, the decision maker demands more advanced tools than a static discounted cash flow (DCF) approach, traditionally used in the industry. In this study, we analyze an opportunity to phase the drilling strategy into two stages as a tool that allows to optimize the field development under technical, cost and market uncertainties. We demonstrate how the decision maker can use additional information generated during the initial stage of the project in order to improve the future development plan by optimizing the decision to drill optional production wells. Using an algorithm based on the least-squares Monte Carlo (LSM) approach, we consider the waiting option to expand the production. We identify additional value created by the staged development strategy for a small offshore oil field, applying the methodology developed in Fedorov et al. (2020) to the synthetic benchmark model Olympus. We also extend the modelling approach of Fedorov et al. (2020) by accounting for cost uncertainty within the LSM algorithm. The underlying reservoir model allows us to realistically account for the effect of the decision to postpone drilling of the optional wells, on the field production. Our findings in this paper confirm the results of Fedorov et al. (2020) , where an analytical modelling approach was used to account for reservoir uncertainty and forecast oil field production. Based on the Olympus case, we show that in case of staged development, the decision maker is able to increase the investment value due to the flexibility to avoid drilling additional wells in case of unfavourable oil price and/or production scenarios. Our conclusion, therefore, is that the staged development has a potential to be applied when the technical uncertainty represents high risk for the project economy.

Ledong HPHT gas field in Yinggehai basin of South China Sea. The pore pressure ramp above the over-pressured target sand with narrow safe mud weight window lead to high drilling risk. To improve drilling safety, multiple steps and approaches were taken in real-time monitoring of pore pressure and target zone depth prediction. This paper will describe the different techniques applied in this field to optimize mud weight and accurate prediction of casing shoe position. Using real-time LWD and mud logging data, pore pressure was calculated and geomechanics model was updated while drilling. The geomechanics model was built and calibrated against drilling and surface data. Intermediate vertical seismic profile (VSP) data was used to correlate with surface seismic profile to predict top of over-pressured reservoir and pore pressure below the bit. Last year, the latest look-ahead technology based on LWD deep electromagnetic measurement was also used to improve the depth prediction accuracy and thin bed detection for high-pressured sands below seismic resolution. The real-time pore pressure monitoring keeps the mud weight and equivalent circulating density (ECD) within a safe margin to avoid kick and mud loss. The estimated pore pressure coefficient calculated in real-time was within 0.01 SG from actual measurement taken in permeable sandstone intervals. VSP data provides formation top prediction between 50 to 200m ahead of the bit in this area. Predicted depth accuracy varies but generally stay below 10m for the depth range mentioned above. The integration of LWD and intermediate VSP logging has helped to reduce the high depth uncertainty in the predrill model. However, the above technique could not resolve for thin beds or low acoustic impedance contrast formation. This advocate to the use of real-time high-resolution inversion bed boundary detection ranging from 3 to 20m below the bit. Clear prediction of resistivity profile ahead of bit enable proactive decision making while drilling. This additional information helped to improve depth prediction accuracy for casing shoe position and ensure well integrity.

Deep directional resistivity logging-while-drilling technology and its measurement processing are instrumental in strategic well placement, landing, geosteering, and reservoir understanding. However, since the measurements are truly at the reservoir scale, reservoir complexities limit the applicability of 1D and sometimes even 2D interpretation models and inversions. A new full-3D imaging inversion is introduced to accurately map reservoir sections where 1D and 2D models are not adequate to represent the reservoir. The imaging inversion uses a 3D EM simulator and accounts for both resistivity and resistivity anisotropy and is fully consistent with the 1D and 2D inversion results in sections of lower complexity. The inversion is validated with a realistic 3D reservoir model and applied to field datasets from the North Sea, revealing complex 3D detail consistent with all the data.

Segmentation of X-ray images of rocks is an important step in digital rock technology. Current segmentation methods often suffer from operator bias and can be time-consuming. To overcome these limitations, an automated image segmentation model is created for pore scale images through the use of convolutional neural networks. A dataset of micro-Computed Tomography (CT) images of sandstones is considered. To create the ground truth data for training, Scanning Electron Microscopy 2D and micro-CT 3D images are used to obtain accurate segmentation masks. Three models are trained with the available data: One with limited images, one with the full dataset, and one with augmented data. The data augmentation is achieved by increasing sample size through image partitioning. The data set for each model is divided into training, validation, and testing with a 60/30/10 split, respectively. The U-Net architecture, designed to work with limited training data, is used to develop the models. Further validation of the models is performed on a different dataset unseen by the models. The Minkowski functionals and permeability of the volume generated by the segmented images are computed and compared with the ground truth segmentation. Against the unseen dataset, the models scored a dice coefficient of 0.9479, 0.9518, and 0.9599, respectively. We discuss the potential for improvement by data augmentation and fine-tuning. One limitation of the models is with the deficiency of variety in training data as both SEM and micro-CT is required to obtain the segmentation masks. We also discuss the performance of the model on unseen samples and show the potential improving efficiency in the digital rock technology. The models provide a quick and accurate segmentation for images of sandstones without the influence of operator bias, and the method shows promise for further development and improvement.

Oil companies are facing several challenges from external and internal environment like volatility of crude oil price, capex reduction, subsurface uncertainty, delay in the surface facilities etc. that impacts the long-term strategic plans. Kuwait Oil Company (KOC)'s annual Asset Action Plan (AAP) operationalizes the long-term strategy of the company assurance of annual AAP is key for having a credible forecast and meeting long term goals. A robust Corporate Assurance process has been developed that consists of workflows that will guarantee company's ability to develop and execute credible field development plans. A review on proper planning, execution and alignment of different business processes is required during assurance of Asset Action Plan. This assurance process provides confidence to the management in approving the AAP. Opportunity based Asset Action Plan (AAP) process is the backbone of KOC's annual business planning and portfolio management system. This allows alternative portfolio choices and management can make the right decision. The opportunity will provide consistency and clarity of the Portfolio constituents and common rational basis for investment decision making and allow for alignment between the KOC major business processes. AAP process is carried out annually and is initiated through high level business guidelines management. During finalization of AAP, an assurance review by corporate technical team for large national companies having multiple assets is considered as best practices that support management in approving the AAP and to steer the company in right direction. AAP Assurance process involves reviewing the forecast with the historical performance of the reservoir, planned activities, expected outcomes from the activities, feasibility and associated cost. A systematic three stage process has been adopted to conduct AAP Assurance for reservoirs which are at different phases of development. This assurance workflow and methodology helps to align changing oil and gas scenarios, different business process, health of the reservoirs and meeting future production targets. This process ensures that proposed AAP plan will be able to deliver production target commitments and identify any gaps and propose mitigation plans. This also confirms that ongoing development plans are in alignment with facility planning. It also validates conformance of the adopted reservoir development and management workflows with global best practices (analogue reservoirs) to maximize recovery and maintaining health of reservoir.

Caprocks play a crucial role in geological storage of CO 2 by preventing the escape of CO 2 and thus trapping CO 2 into underlying porous reservoirs. An evaluation of interaction-induced alteration of hydromechanical properties of caprocks are essential to better assess the leaking risk and injection-induced rock instability, and thus ensuring a long-term viability of geological CO 2 storage. We study the changes in nanopores, elastic velocities and mechanical responses of a carbonate caprock due to rock-water/brine-CO 2 interaction (CO 2 pressure ~ 12 MPa; 50 ℃). Before the interaction, the total and accessible porosities are 1.6% and 0.6%, respectively, as characterized by the Small Angle Neutron Scattering (SANS) technique. SANS results show that the total porosity of the carbonate caprock increases apparently due to rock-brine-CO 2 interaction and the increasing rate rises as brine concentration increases (2.2% for 0M NaCl, 2.6% for 1M NaCl, and 2.7% for 4M NaCl). The increase total porosity is due to the dissolution of calcite which tends to enlarge accessible pores (by 0.8%-1.2%) while slightly decrease the inaccessible pores (by 0.1%-0.2%). Under CO 2 -acidified water environment, P- and S-wave velocities (5536.7 m/s and 2699.7 m/s) of a core sample containing natural fractures decreases by 8.5% and 8.1% respectively, while both P- and S-wave velocities (6074.1 m/s and 3858.8 m/s) for a intact sample show only ~0.5% decreases. The interaction also causes more than 50% degradation of the uniaxial compressive strength for the core sample with natural fractures. We also conduct simulations of the single-phase creeping flow and two-phase water-CO 2 flow in micron-scale natural fractures, as extracted from X-ray Micro-CT images of the core sample. The simulated absolute permeability (2.0×10 -12 m 2 ) is much higher than the matrix permeability (6.7×10 -20 m 2 before the interaction; 1.3×10 -19 m 2 after the interaction), as calculated based on the Kozeny–Carman Equation. This indicates that natural fractures provide preferential flow paths for CO 2 while flow through caprock matrix can be reasonably neglected. Simulation results also indicate that CO 2 preferentially migrates in the natural fractures where there are more inter-connected and permeable channels. The study recommends that more attention should be addressed on interaction-induced alteration of fracture/faults permeability/stability, and its effect on the sealing integrity of carbonate caprocks.

The three-dimensional connectivity of the fluid phases in porous media plays a crucial role in governing the fluid transport, displacement, and recovery. Accurate three-dimensional quantification of the fluid phase connectivity following each fluid injection stage will lead to better understanding of the efficacy and efficiency of the fluid injection strategies. Two metrics for measuring the connectivity in 3D show robust performance; one uses fast marching method to quantify average time required for a monotonically advancing wave to travel between any two pixels and the other uses two-point probability function to approximate the average distance between any two connected pixels belonging to the same fluid phase. The two connectivity metrics are applied on the three-dimensional (3D) CT scans of one water-wet Ketton whole-core sample subjected to five stages of multiphase fluid injection to quantify the evolution of the three-dimensional connectivity of the three fluid phases (oil, water, and gas). The water-wet Ketton carbonate sample (4.9 mm in diameter and 19.5 mm in length) is subjected to five sequential stages of fluid injection: 100%-brine-saturated sample, oil injection, water-flooding #1, gas injection, and water-flooding #2. CT-scan of the core sample was acquired after each injection stage. The metric response for oil phase connectivity drops after each injection process, denoting a reduction in oil connectivity after each fluid injection. The spatiotemporal variations in the connectivity of a fluid phase help understand the fluid displacement across pores of varying sizes depending on the wettability.

This paper will discuss the Managed Pressure Directional Drilling fit-for-purpose solution deployed to meet the drilling challenges faced in 5 consecutive wells drilled in South Texas, USA. This innovative solution integrates a state-of-art Rotary Steerable System (RSS) with Managed Pressure Drilling (MPD) technology. Drilling hazards such as well control events, simultaneous kick-loss, and stuck pipe were mitigated, and an improved drilling performance with a reduction of NPT as compared to other directional drilling systems. The solution requires the integration of two highly technical disciplines, MPD and Directional Drilling. Hence, a Joint Operating & Reporting Procedure (JORP) and a defined communication protocol are crucial for effective execution. The solution is based on a rigorous Drilling Engineering process, including detailed offset wells analysis to deliver a comprehensive risk assessment & mitigation plan in collaboration with the Operator to tackle drilling hazards without compromising the directional drilling requirements. This paper will summarize the 5 wells operations, the drilling optimization results, and the lessons learned from an integrated services point of view in terms of deliverables that made the difference on this project and allowed the Operator to achieve their objectives. In particular, the effective communication protocol between the directional drilling services, MPD services, and rig contractors to ensure safe operational alignment.

Reliable hydrocarbon production forecasting is necessary for optimizing field development and management and for assisting asset teams in making sound business decisions. In conventional reservoir modeling workflows, the data used to build a subsurface flow model comes with inherent uncertainties that are typically represented by, and propagated through, a set of prior model realizations. History matching, which is used to generate a set of calibrated reservoir models, typically involves repeated runs of computationally intensive forward simulations. For many existing automated history matching workflows, production data scarcity and its non-linearity with respect to reservoir input model parameters complicate the formulation and solution of model calibration problems. Additional complexities of the model calibration workflows include ensuring geologic realism (plausibility) and assessing the uncertainty of the solution and the resulting forecasts. Short-term predictions that can be used to inform daily operational decisions may be more conveniently derived from data-driven models without relying on complex integrated reservoir models and the related complex workflows. In this paper, we develop spatial autoregressive models to obtain short-term data-driven prediction models. To this end, the injectors and producers in a reservoir are related through a set of weights that can be interpreted as their lumped connectivity information, which may be initialized with prior geologic knowledge, whenever available. Each element in the matrix dictates the existence and strength of connectivity between any injector-producer pair. The well connectivity depends on the spatial geologic features within the reservoir, such as heterogeneity and compartmentalization induced by stratigraphic trap, faulting and diagenesis. As more dynamic data becomes available, the connectivity matrices are continuously updated to derive new forecast in a manner similar to the history matching process. When data is not sufficient to resolve the uncertainty in connectivity information, alternative scenarios may remain plausible. However, when additional information about inter-well connectivity is available, it can be imposed to reduce the uncertainty in inter-well connectivity. The model presents a practical tool that can be applied to evaluate and rule out certain inter-well connectivity scenarios and address what-if questions. We present the model and discuss its properties with both synthetic and field-scale examples.