Clathrate hydrates are non-stoichiometric compounds of water and gas molecules coexisting at relatively low temperatures and high pressures. The gas molecules are trapped in cage-like structures of the water molecules by hydrogen bonds. There are several hydrate deposits in permafrost and oceanic sediments with an enormous amount of energy. The energy content of methane in hydrate reservoirs is considered to be up to 50 times that of conventional petroleum resources, with about 2,500 to 20,000 trillion m 3 of methane gas. More than 220 hydrate deposits in permafrost and oceanic sediments have been identified to date. The exploration and production of these deposits to recover the trapped methane gas could overcome the world energy challenges and create a sustainable energy future. Furthermore, global warming is a major issue facing the world at large and it is caused by greenhouse gas emissions such as carbon dioxide. As a result, researchers and organizations have proposed various methods of reducing the emission of carbon dioxide gas. One of the proposed methods is the geological storage of carbon dioxide in depleted oil and gas reservoirs, oceanic sediments, deep saline aquifers, and depleted hydrate deposits. Studies have shown that there is the possibility of methane gas production and carbon dioxide storage in hydrate reservoirs using the injection of carbon dioxide and nitrogen gas mixture. However, the conventional hydrocarbon production methods cannot be used for the hydrate reservoirs due to the nature of these reservoirs. In addition, thermal stimulation and depressurization are not effective methods for methane gas production and carbon sequestration in hydrate-bearing sediments. Therefore, the gas replacement method for methane production and carbon dioxide storage in clathrate hydrate is investigated in this paper. The research studies (experiments, modeling/simulation, and field tests) on CO 2 /N 2 gas mixture injection for the optimization of methane gas recovery in hydrate reservoirs are reviewed. It was discovered that the injection of the gas mixture enhanced the recovery process by replacing methane gas in the small and large cages of the hydrate. Also, the presence of N 2 molecules significantly increased fluid injectivity and methane recovery rate. In addition, a significant amount of free water was not released and the hydrate phase was stable during the replacement process. It is an effective method for permanent storage of carbon dioxide in the hydrate layer. However, further research studies on the effects of gas composition, particle size, and gas transport on the replacement process and swapping rate are required.

Accurate prediction of gas compressibility factor is essential for the evaluation of gas reserves, custody transfer and design of surface equipment. Gas compressibility factor (Z) also known as gas deviation factor can be evaluated by experimental measurement, equation of state and empirical correlation. However, these methods have been known to be expensive, complex and of limited accuracy owing to the varying operating conditions and the presence of non-hydrocarbon components in the gas stream. Recently, newer correlations with extensive application over wider range of operating conditions and crude mixtures have been developed. Also, artificial intelligence is now being deployed in the evaluation of gas compressibility factor. There is therefore a need for a holistic understanding of gas compressibility factor vis-a-vis the cause-effect relations of deviation. This paper presents a critical review of current understanding and recent efforts in the estimation of gas deviation factor.

Proper understanding of reservoir producing mechanism forms a backbone for optimal fluid recovery in any reservoir. Such an understanding is usually fostered by a detailed petrophysical evaluation, structural interpretation, geological description and modelling as well as production performance assessment prior to history matching and reservoir simulation. In this study, gravity drainage mechanism was identified as the primary force for production in reservoir X located in Niger Delta province and this required proper model calibration using variation of vertical anisotropic ratio based on identified facies as against a single value method which does not capture heterogeneity properly. Using structural maps generated from interpretation of seismic data, and other petrophysical parameters from available well logs and core data such as porosity, permeability and facies description based on environment of deposition, a geological model capturing the structural dips, facies distribution and well locations was built. Dynamic modeling was conducted on the base case model and also on the low and high case conceptual models to capture different structural dips of the reservoir. The result from history matching of the base case model reveals that variation of vertical anisotropic ratio (i.e. kv/kh) based on identified facies across the system is more effective in capturing heterogeneity than using a deterministic value that is more popular. In addition, gas segregated fastest in the high case model with the steepest dip compared to the base and low case models. An improved dynamic model saturation match was achieved in line with the geological description and the observed reservoir performance. Quick wins scenarios were identified and this led to an additional reserve yield of over 1MMSTB. Therefore, structural control, facies type, reservoir thickness and nature of oil volatility are key forces driving the gravity drainage mechanism.

Understanding the complexity in the distribution of hydrocarbon in a simple structure with flow baffles and connectivity issues is critical in targeting and developing the remaining pay in a mature asset. Subtle facies changes (heterogeneity) can have drastic impact on reservoir fluids movement, and this can be crucial to identifying sweet spots in mature fields. This study evaluated selected reservoirs in Ovhor Field, Niger Delta, Nigeria with the objective of optimising production from the field by targeting undeveloped oil reserves or bypassed pay and gaining an improved understanding of the selected reservoirs to increase the company's reserves limits. The task at the Ovhor field, is complicated by poor stratigraphic seismic resolution over the field. 3-D geological (Sedimentology and stratigraphy) interpretation, Quantitative interpretation results and proper understanding of production data have been used in recognizing flow baffles and undeveloped compartments in the field. The full field 3-D model was constructed in such a way as to capture heterogeneities and the various compartments in the field. This was crucial to aid the simulation of fluid flow in the field for proper history matching, future production, prediction and design of well trajectories to adequately target undeveloped oil in the field. Reservoir property models (Porosity, Permeability and Net-To-Gross) were constructed by biasing log interpreted properties to a defined environment of deposition model whose interpretation captured the heterogeneities expected in the studied reservoirs. At least, two scenarios were modelled for the studied reservoirs to capture the range of uncertainties. This integrated approach led to the identification of bypassed oil in some areas of the selected reservoirs and an improved understanding of the studied reservoirs. Dynamic simulation and production forecast on the 4 reservoirs gave an undeveloped reserve of about 3.82 MMstb from two (2) identified oil restoration activities. These activities included side-tracking and re-perforation of existing wells. New wells have been drilled to test the results of our studies and the results confirmed our findings.

Production ramp-up can be a tricky exercise, with a repertoire of challenges and surprises, which can threaten the proposed percentage of success. These challenges largely stem from paucity of data, change in reservoir dynamics, impact of unauthorized third-party activities and the compliment of skill sets/experience in the team. In addition, reservoir models are in some cases, ineffective tools for reservoir management and prediction. This is especially true in assets that are heavily bunkered by crude oil thieves and in geologically complex reservoirs with very few wells. Therefore, models must be updated with recent data and new understanding, to remain useful and accurate. Of great importance to a successful production ramp-up effort is assembling an integrated team of experienced sub-surface professionals and field executors. Unfortunately, in some teams and companies, the position and opinions of Geologists are often overlooked, resulting in mixed outcomes. Geologists do not only think in "millions of years" but can add value and contribute significantly to the overall project objective. This paper explores and explains the role of a geologist, as part of an integrated asset management team, in the successful ramp-up of production from 45KOPD to about 80KOPD, without drilling a single well or executing any rig activity. It also showcases some work methodologies adopted in the evaluation of candidate wells and the power inherent in adopting an integrated subsurface approach.

Well delivery is an expensive scope in the exploration and field development process. Among other drivers, an ideal well must be delivered safely while achieving top-quartile performance on cost, schedule and business objectives. However, delivering an ideal well amid subsurface uncertainties and tightening budgets is usually challenging. As part of the drive for continued value creation, this paper presents an empirical process-improvement initiative for de-risking and optimizing the landing of the drain-hole sections of highly deviated wells amid subsurface uncertainties and at minimal costs. A review of the conventional procedure for executing subsurface scope of the delivery of development wells has been accomplished. The review takes advantage of a combination of recent experiences in delivering four horizontal development wells in an offshore field in the Niger Delta, in addition to a catalogue of available knowledge and best practices from other fields and operators. This review culminates in an improved well delivery optimization process and practice. In addition to promoting operational HSE excellence, it increases the chance of delivering an ideal well, including the mitigation of a subsurface-related non-productive time (NPT) and other related costs. As a complement to the practice, a simple workflow is provided to aid robust decision-making and facilitate applications in practice. For completeness, relevant examples are included to demonstrate the applicability of this new process.

In this study, a simpler numerical model for calculating inter-well distance was developed. This model was developed as an alternative to the Ei-function used for computing pressure drops. The mainobjective of developing this model is tomake resolution of pilfering issues easyto resolve. With the developed model, calculations relating to pressure drops and more specifically, inter-well distance, can be done with greater ease and accuracy. In developing this model, the integral equation of the Eifunction in the pressure drop equation was solved numerically. The numerical solution reduced thepressure drop equation to a polynomial equation which is much easier to solve. The developed model was used to solve real problems. Results generated from it were compared with those obtained using previous approaches. Important informationsuch as well configuration, region of the reservoir, and transient history wherethe work is valid are stated. The development of the correlations and tables forthe range of validity and values of the Ei-function is a major quantum leap in well testing and analysis. It will be quite cumbersome to resolve integrals with unknowns, hence, methods of trials and errors have been resorted to over the years. However, this new approach resolved the pressure drop equation into a systemof polynomials which is much easier to solve. Consequently, the distance betweenpossibly interfering wells (which is an important variable during interference test) can now be gotten with ease. The developed model is valid within the range of validity of the Ei-function. Without doubt, this work will help redefine the pressure drop equation into a polynomial equation which can easily be resolved using any of the known approaches to solving problems involving polynomials. More so, getting the correct distance betweenthe two wells in question is pivotal to the test. With the model developed in this work, getting inter-well distance is now easier and more accurate.

The development plan of the new oil field discovered in a remote offshore environment, Niger Delta, Nigeria was evaluated. As the oil in place is uncertain, a probabilistic approach was used to estimate the STOOIP using the low, mid, and high cases. The STOOIP for these cases were 95 MMSTB, 145 MMSTB and 300 MMSTB which are the potential amount of oil in the reservoir. Rock and fluid properties were determined using PVT sample and then matched to the Standing correlations with an RMS of 4.93%. The performance of the different well models were analyzed, and sensitivities were run to provide detailed information to reduce the uncertainties of the parameters. Furthermore, production forecast was done for the field for the different STOOIP using the predicted number of producer and injector wells. The timing of the wells was accurately allocated to provide information for the drillers to work on the wells. From the production forecast, the different STOOIP cases had a water cut ranging from 68-73% at the end of the 15-year field life. The recoverable oil estimate was accounted for 33.25 MMSTB for 95 MMSTB (low), 55.1 MMSTB for 145 MMSTB (mid) and 135 MMSTB for 300 MMSTB (high) at 35%, 38% and 45% recovery factor. Based on the proposed development plan, the base model is recommended for further implementation as the recovery factor is 38% with an estimate of 55.1 MMSTB. The platform will have 6 producers and 2 injectors. The quantity of oil produced is estimated at 15000 stbo/day which will require a separator that has the capacity of hold a liquid rate of about 20000 stb/day. The developmental wells are subsequently increased to achieve a water cut of 90-95% with more recoverable oil within the 15-year field life. This developmental plan is also cost effective as drilling more wells means more capital expenditure.

The application of pseudo-steady-state and pressure transient response techniques to assist in hydrocarbon volume estimate is presented for a reservoir isolated from its main by a non-sealing fault. The techniques discussed in this paper utilized the pseudo steady state principle to determine the fault boundary behavior dominated flow regime of an oil well which has produced for over eight years in a marginal field of the Niger Delta environment. The material balance technique which utilized accountability of fluid withdrawn/injected and energy conservation principles within the pseudo steady state boundary dominated flow was used alongside with the pressure transient analysis to validate this oil in place number. Seismic attributes was also used to predict the geometry and distribution of the sand based on the conventional seismic interpretation. The seismic attribute analyses clearly show the geometry and spatial distribution of the reservoir sand bodies. Hence, understanding a pseudo steady state dominated regional flow time in a faulted reservoir plays a key role in the management and development of reserves in a marginal field operation.

Faster and more accurate decisions are what the Oil and Gas industry needs with the world's fast-evolving energy needs and economy. The area of Artificial intelligence and Data-driven modelling is relatively new and has not found popular application in the industry. AI is an emerging technology that can be used to predict event outcomes and automate anomaly-detection processes. The various applications of AI in different industries were researched into. This paper highlighted important processes that can be improved with the application of Artificial Intelligence through data-driven modelling. It also highlights areas in the various industries where AI intelligence is already being applied and ways it can be improved. AI and data-driven modelling has the potential to improve exploration accuracy, reduce production down-time, reduce cost of maintenance, and reduce health and safety risks. This body of information can serve as a guideline for adopting AI in the oil and gas industry. A trend of industry-tailored intelligence solutions would be more effective in the evolving energy industry.

Considering the imminent end of the ‘easy oil’ era, the increasing demand for energy and the global push towards the energy transition, oil and gas companies are more than ever interested in sustainable ways to develop marginal and complex hydrocarbon fields economically, through the application of technology and maximization of data analysis. In small partially appraised fields where the cost of drilling an appraisal well could derail the project economics, it becomes necessary to sweat the limited data available for reservoir modelling. The uncertainty analysis must be robust enough to ensure that the adopted field development strategy would yield a positive net present value despite the wide uncertainties associated with the field. The conventional workflow for subsurface uncertainty modelling involves defining the uncertainty ranges of static and dynamic reservoir parameters based on a single reservoir model concept. This paper focuses on a marginal field case study where the multi scenario modelling approach was adopted. This approach considered alternate reservoir geologic concepts based on different interpretations of the reservoir architecture, taking full cognizance of the available data, reservoir uncertainties and regional geology knowledge. Field Alpha is located onshore of Niger Delta in Nigeria. The geologic setting consists mainly of multi-storey, complex channel-belt systems, incising through Shoreface deposits. The reservoir of interest is an elongated structure with only two well penetrations located at the opposite distal part of the structure. The key reservoir uncertainties are reservoir structure, architecture, connectivity, and property distribution. Two possible distinct architecture were interpreted based on regional correlation and seismic. This paper focuses on how the interpretations and other information informed a robust development strategy that yielded significant (30 %) reduction in development cost and positive net present value.

This paper presents the research work on using a machine learning algorithm to predict the viscosity of Niger Delta oil reservoirs using formation volume factor and fluid density at bubble point pressure as correlating parameters. Oil Viscosity stands out when considering the amount of oil recoverable from the reservoir hence it is an important input into the recovery factor computation, material balance analysis, reservoir simulation/history match, EOR evaluations and many other applications. Laboratory techniques of obtaining oil viscosity are quite expensive and time consuming, hence the need for various mathematical correlations developed for its estimation. Majority of the correlations make use of empirical and experimental relationships developed from analyzing oil samples to obtain a trend to predict viscosity mostly for a basin. None of these has been developed for oil viscosity for Niger Delta fluids. Viscosity has been globally defined as the resistance to shear stresses within the fluid or the resistance of the fluid molecules to deformation. For a typical reservoir fluid system, where the liquid and gas exist in dynamic equilibrium, reservoir fluid composition along with temperature and pressure has been established to determine reservoir fluid viscosity 1 . Hence for an isothermal system and at a defined pressure in the reservoir the viscosity will be dependent on largely the composition. The reservoir fluid composition is also represented by the reservoir fluid density and the formation volume factor; therefore it is possible to deduce the viscosity of reservoir fluids from the oil density and formation volume factor even though a direct relationship has not been established between these parameters. Therefore, a correlation that can establish a relationship between the specific gravity (density) and FVF with viscosity will have significant value in the oil and industry. The data used for this analysis includes viscosity, formation volume factor, oil density at 2800 sample bubble point pressure. The data was obtained by analyzing over 3500 PVT Analysis reports, extracting the data points using a python work program, cleaning up the data and removing erroneous data, performing preliminary analysis to establish baseline relationships between the data. Supervised learning using a classification tree model was used as the machine learning approach. Seven different machine learning algorithms were reviewed, and the Random Forest Regressor was selected as the most suitable algorithm for the prediction. The model prediction results were quiet encouraging as the model was able to predict viscosity within 10% deviation from the experimental viscosity for over 80% of the cases resulting in about 90% prediction accuracy. The analysis of the results further revealed that the model could better predict viscosity of Medium to Light oil with an R 2 value of between 0.90-0.96 without adjusting some obvious erroneous data points. Future of this research work will involve further in-depth analysis which will merge the preliminary QC plots with the results to evaluate the effect of the outlier sample points on the final predictability of the model. Also explore other machine learning models to further improve predictability and be able to predict viscosity across other pressure values other than the bubble point pressure to capture viscosity along the producing life of the reservoir.

The dynamics of tracer particles in a viscous Newtonian fluid is studied analytically and numerically through channels of varying thickness for fluids undergoing creeping flow. Exact analytical solutions of mass conservation equations of tracer particles including consideration for pressure forces are obtained. Results of the analysis indicates that Stokes velocity is an indispensable parameter and is dependent on parameters such as channel thickness (height), viscosity of the fluid, pressure gradient driven the fluid and Reynolds number corresponding to the channel thickness. The accuracy of the solution obtained is verified by comparing its velocity profiles with those obtained from finite-element-based numerical simulation studies.

Puissant field planning is increasingly becoming a sophisticated quandary with less emphasis on parametric synergy with reservoir spasmodic acuity. This conundrum leads to inaccurate harbinger of the required number of wells to be drilled for future field development programs from existing production and reservoir data particularly at pressures above the bubble point which is a major sobriety as orchestrated in most recent simulators. The aim of this erudition is to compendiously carry out astute predictive heterodox principles of wellbore aggregates from critical recovery factor parameters for savvy field planning. The main objectives are to glean and develop new propinquities for differential pressures ( ΔP ), rock compressibilities ( C o ) and oil formation volume factors ( B o ) for predicting the number of wells to be drilled and recovery factors (RF) by equating the simulated results and the theoretical model (Ezekwe, 2010). To elucidate, metaphorize and ruminate new models. Reservoir and economic data was carefully simulated using FAST-FEKETE Evolution software for initial 40 future oil wells. Average results were mathematically correlated with recovery factor model to produce new correlations to quickly re-jig field planning efficiency. Results of matched and validated compressibility factors, differential reservoir pressures and oil formation volume factors were correlated with field data from Ezekwe (2011) model. Results of compressibility factor showed increasing similar 3 rd order polynomial converging correlation for both models but gave slight divergence with increasing number of wells and RF. Results of differential pressures gave linearly increasing correlation with number of wells and RF while the new model had a cross-over point at 6435.64 psi for 2 wells but slightly increased divergently with number of wells and RF. Results of oil FVF gave a good similar regression (R 2 ) of 0.999 while both models showed decreasing 3 rd order polynomial correlation comparison with number of wells but with slight divergent disparity with increased RF. To further validate the potency of this study, detailed comprehensive paired sample test gave standard deviation, standard error of mean and degree of freedom of 0.00356, 0.0012 and 8 for compressibility factors; 324.7, 102.68 and 9 for differential pressure while the oil formation volume factor gave 0.0067, 0.0021 and 9. The predictions obtained by the new model showed appreciable degree of consistency and accuracy with number of wells and RF. This is perhaps largely hinged on the capacity to cogently infuse field data with theoretical and simulated models effectively. This study has clearly shown that no special technique or rigorous computational procedures is required to plan future number of wells to be drilled in a field or perhaps estimate the required RF. Sequel to this, further research is encouraged to inculcate more correlations based on comprehensive field validation studies to improve the efficacy of this model.

This paper presents the innovative use of interference tests in the assessment of reservoir connectivity and the field oil production rate during the development phase and prior to the first oil of the EGINA field, which is located in a water depth of 1600 m, deep offshore Niger Delta. The interference test campaign involved 26 pre-first oil wells (13 oil producers and 13 water injectors) to assess and subsequently mitigate reservoir connectivity uncertainties arising from the numerous faults and between the different channels within the complexes. The results proved valuable in confirming or otherwise reservoir connectivity, field oil production rate and the number of wells required at first oil to achieve the production plateau. The tests were designed using the analytical method (PIE software) and the reservoir simulation models enabling to establish the cumulative water injection required, the injection duration and the time a response is expected at the observers. These all had impacts on the planning, OIMR vessel requirements and selection of permanent downhole gauges for the wells. In addition, the tests were performed with the water injectors as pulsers and the oil producers as observers allowing to avoid and the associated environmental impact. Ten interference tests were realized compared to four planned in the FDP partly due to the use of the more cost effective OIMR vessel in addition to the rig.

Reservoir-H sequence, comprising of three reservoirs (H1, H2 & H3) is one of the most complex reservoirs in Niger-delta. With a combined well-count in excess of sixty producers and injectors and a production history of more than fifty-five years, the reservoir has had a history of challenging simulation studies with average water-cut matches resulting in new wells having high water breakthrough from onset. In the latest effort, an assisted history match using genetic algorithm was employed. This approach is a two-step approach including an identification of all relevant history match parameters for the three reservoirs, followed by a fine-tuning of pressure and saturation history match using genetic algorithm. This approach enabled the identification of aquifer assumptions (architecture and transmissibility) as a critical factor in successfully matching the wells in these reservoirs. In addition to pressure and saturation matches, infill opportunities were further validated by tracking current reservoir fluid contacts with the model. The current model has significantly improved the overall water-cut match in more than ten wells that historically had water-breakthrough challenges while using principally global history-match parameters. The elimination of many local changes in the current model is expected to improve both the reliability and the shelf of the model. Also, the variance between estimated contacts compared to actual gas-oil and oil-water contacts around infill locations is less than five feet indicating good predictability of the model. In order to save development cost, multiple opportunities identified in these reservoirs are to be targeted with dual strings. Additional savings were realized by reducing the overall simulations studies timeline by four months.

At inception of a production rate regime, a horizontal well is expected to sweep oil within its drainage radius until the flow transients are interrupted by an external boundary or an impermeable heterogeneity. If the interruption is an impermeable heterogeneity or sealing fault, then the architecture of the heterogeneity must be deciphered in order to be able to design and implement an effective work-over or well re-entry to boost oil production from the reservoir. In this paper, therefore, the behavior of a horizontal well located within a pair of sealing faults inclined at 90 degrees is investigated using flow pressures and their derivatives. It is assumed that the well flow pressure is undergoing infinite activity, and each fault acts as a plane mirror. The total pressure drop in the object well is calculated by superposition principle. Damage and mechanical skin and wellbore storage are not considered. The main objective of our investigation is to establish identifiable signatures on pressure-time plots that represent infinite flow in the presence of adjacent no flow faults inclined at 90degrees. Results obtained show that the flowing wellbore pressure is influenced strongly by object well design, object well distance from each fault, and distance of each image from the object well. Irrespective of object well distance from the fault, there are three (3) images formed. Central object well location yields a square polygon, with two image wells nearer to the object well at equidistance from the object well, and the farthest image well to be 2 d 2 . From the object well For off-centered object well location within the faults, a rectangular polygon is formed, with each image at a different distance from one well to another. Dimensionless pressure and dimensionless pressure derivative gradients during infinite-acting flow are (4.6052/LD) and 2/LD, respectively for all well locations within the faults.

Among the most frequently utilized existing classical water influx models, numerous computational conundrums abound due to inherent and unquantifiable uncertainties of aquifer propensity. All the plexus require historical reservoir performance data to evaluate their inherent patterns chronologically with reservoir pressures. Van-Everdingen and Hurst model is challenged with various forms of pressure approximations and iteration errors. Omeke et al (2011) tried to illumine on the technical deficiencies of the Van-Everdingen-Hurst and Fetkovich approximations but their new correlation fell short of operational technicalities with poor regressions with noticeable divergence from the classical models for higher cumulative water influx (W e ) and time ≥ 3000 days. In this paper, a retrospectively sagacious and pellucid technique was developed and used to estimate aquifer influx with appreciable convergence at higher cumulative water influx and time for finite aquifer systems for pseudo-steady state flow regimes. Basic assumptions were used for the characterization of the aquifer flows giving rise to ordinary differential equations and solutions which was obtained for estimation of cumulative water influx with time without superposition, pressure approximation or iterations. To validate the developed model, comparison was done with Omeke et al., Carter and Tracy, Fetkovich and Van-Everdingen models. Omeke et al and the Van-Everdingen models were used as standard for comparison due to their exact solutions of radial diffusivity equation and to lay credence to the credibility of this research. In the two case examples appraised, results showed an improvement of the new model with better convergence nexus to Van-Everdingen and Fetkovich models while the Omeke et al model diverged. Detailed statistical re-evaluation confirmed the technical scenario where for case 1, the standard error, standard deviation and skewness gave 0.130, 2.60 and 0.313 for the new model which closely approximates the Van Everdingen as against the 0.131, 2.63 and 0.499 for the Omeke et al while the base case (Van-Everdingen) result gave 0.128, 2.57 and 0.353. Results of case 2 also gave similar trend of standard errors, standard deviation and skewness of 0.961, 30.4 and -0.191 for the new model as against 1.142, 36.1 and 0.139 for the Omeke et al with the base case Van-Everdingen result giving 0.931, 29.4 and -0.271 indicating that the new model performed better than the Omeke et al but closely approximates the the Van-Everdingen model. The predictions obtained by the new model showed appreciable degree of accuracy at higher cumulative water influx (W e ) and time. The degree of accuracy of performance of the models is largely hinged on the efficacy to handle pressure alterations of the history data. The number of these alterations puts considerable computational strain on the models giving rise to divergence of results at such periods. No pressure averaging or superpositioning and rigorous computational iterations. Sequel to this study, further research should continue on the theoretical basis of the constant (∄) to improve the efficacy of this model.

Inflow control devices (ICD) have been used to balance flux around completions and also delay break-through of unwanted water into completions. Inflow-control devices (ICDs) were provided to curtail water production from heterogenous reservoirs with strong aquifer systems and/or supported with water injection. The model for the ICD consists of pressure-drop equations from the reservoir, through the screen, the flow conduit, the ICD nozzle, and into the production tubing, along with pressure drop through the lower-completion system. This additional pressure drop does not contribute to additional fluid inflow into the wellbore and this is seen to be an impairment to the productivity of horizontal wells. Pressure losses from horizontal wellbores which do not contribute to increased production is seen as skin and consequently, a new equation was derived to estimate skin due to ICD in horizontal completions. In this paper, a horizontal well equipped with inflow control device which was drilled in one of the off-shore fields of the Niger Delta region was used as a case study in evaluating the performance of an ICD completion. The new equation was used to estimate the skin due to ICD of this completion and the result obtained is compared with the skin obtained using pressure transient analysis and the results obtained from both approach are similar. This paper shares how the new equation was used to estimate skin due to ICD and the result comparison with that of a pressure transient analysis.

By investigating how capillary, viscous and gravity forces impact the matrix–fracture transfer mechanisms in fractured oil – gas systems, this paper aims to deepen our understanding on the modeling of fractured reservoirs with the intention of creating more predictive and more realistic dual porosity – dual permeability reservoir simulation models. The hypothesis of this study is that first order differential equations may be able to capture and approximate the total transfer realistically, however it has to be known to which extent the individual forces influence the total transfer. Complex, higher order, non-linear differential equations are able to capture the combined influence of the forces, however their formulation and implementation are extremely challenging. To investigate whether the total matrix – fracture transfers can be expressed as the sum of the individual forces multiplied with appropriate coefficients, two dimensional models were created using ECLIPSE 100 Black Oil simulator and Eclipse 300 for the study compositional effect. The results showed that the models can realistically capture the effect of capillary-, gravity- and viscous forces individually. These scenarios can serve as a solid basis for further quantitative analysis regarding the extent, to which the individual forces influence matrix – fracture transfers. In this study we determined the Minimum Miscibility Pressure (MMP) of CO 2 using slim-tube test, we also carried out sensitivity studies of important operational CO 2 -EOR parameters to determine how they will affect oil recovery factor and displacement efficiency in a single matrix block surrounded by fractures. To ensure miscibility CO 2 was injected at a pressure above minimum miscibility pressure. A significant improvement in CO 2 EOR processes and CO 2 storage can be achieved by applying the findings of this research in dual porosity – dual permeability models in fracture-matrix systems. These findings not only result in a technical advancement, but could be of high significance from an economical point of view as well. A slim tube model was set up and used to determine the Minimum Miscibility Pressure (MMP). Seven components (CO 2 ; N 2 -CH 4 ; C 2 -NC 4 ; IC 5 -C 7 ; C 8 -C 12 ; C 13 -C 19 ; C 20 -C 30 ) were presented in the model to compare the rate of dissolution and displacement efficiency. Understanding the process and applying this knowledge to enhance recovery of hydrocarbons, CO 2 Capture and Storage can have a significant economic and environmental effect.