Mohammed Saleh Al-Jawad

Due to the research gap in the literature regarding the Nahr Umr formation within the Abu-Amood oil field, this study aims to develop a dynamic compositional reservoir model for this formation. Located in the Dhi Qar province of Iraq, the Nahr Umr Formation, primarily composed of sandstone, lies 3025 meters below sea level and holds significant hydrocarbon potential. This research focuses on building a compositional model to predict reservoir behavior under various production strategies, ultimately maximizing oil recovery. The model was constructed using geological report data and implemented with Petrel and CMG software. Utilizing pressure-volume-temperature (PVT) data and the Peng-Robinson equation of state (EOS), an accurate fluid properties model was developed. Through reservoir and well area calculations, this study demonstrates that the Nahr Umr reservoir can be effectively drained using only six producers. This comprehensive model is crucial for designing efficient production strategies and enhancing oil recovery in the Nahr Umr Formation.

The study area is located within the Mesopotamian Basin, the habitat of several oil fields that extract hydrocarbons from NW-SE-trending anticline formations parallel to the Zagros folding belt. The study aimed to determine if discrepancies in bubble pressure values were caused by faults or reefs using historical matching of gas production. The study revealed a distinct Mishrif reservoir seismic graph and seismic reflectors exhibiting many severe discontinuities, indicating the presence of a reef or fault. Field production data matched the fault case more consistently than the reef case during reservoir simulation, as per the well results (C37P, C43P, C46P). The history-matching simulation confirmed the two faults and rejected the reef. After seismic interpretation and historical matching, the Mishrif reservoir has two faults that were the cause of the bubble point pressure discrepancy during production operations. This method is suitable for discovering geological structures and deformations that cause bubble pressure differences for use by other researchers.

This work evaluates the economic feasibility of various production scenarios for the Zubair reservoir in the Kifl oil field using cash flow and net present value (NPV) calculations. The Kifl field is an exploratory field that has not yet been developed or assessed economically. The first well was drilled in 1960, and three other wells were later drilled to assess the oil accumulation, so in this research, Different production scenarios were evaluated economically. These scenarios were proposed based on the reservoir model of the Zubair formation in the field. The research methodology used QUE$TOR software to estimate capital expenditures (CapEx) and operating expenditures (OpEx) based on field-level data, production profiles for each scenario, and produced fluid properties. CapEx estimates were mainly based on the number of drilled wells and plateau duration, while OpEx estimates were based on the number of barrels produced from each scenario. Cash flow values were estimated each year in the production interval after subtracting water handling costs, CapEx, and OpEx from the gross revenue of oil produced. The NPV for each scenario was then calculated using a 5% interest rate, and the most economical option was determined by comparing the NPV values of each scenario. Case-3 scenario was identified as the most economical option, with a high NPV value of approximately 612 MM$, an oil production rate of 10 MSTB/d, a Plateau duration of 3 years, a total number of wells of 11, and an Initial water cut of 13 %.  

Unconfined Compressive Strength is considered the most important parameter of rock strength properties affecting the rock failure criteria. Various research have developed rock strength for specific lithology to estimate high-accuracy value without a core. Previous analyses did not account for the formation's numerous lithologies and interbedded layers. The main aim of the present study is to select the suitable correlation to predict the UCS for hole depth of formation without separating the lithology. Furthermore, the second aim is to detect an adequate input parameter among set wireline to determine the UCS by using data of three wells along ten formations (Tanuma, Khasib, Mishrif, Rumaila, Ahmady, Maudud, Nahr Umr, Shuaiba and Zubair). After calibration with core test, the results revealed that Young’s Modulus correlations are the best to predict UCS with RMSE (53.23 psi). Furthermore, the result showed that using the static Young Modulus as an input parameter in predicting UCS gives a closer result to the laboratory test than using a sonic log. This study found that many previous equations were developed for only one type of rock and tended to generalize poorly to the broader database. This study also provided more accurate rock strength estimation, leading to better prognosis in operational strategies and hydraulic fracturing location planning in oil well development when geomechanical analysis needs to be addressed where no core is available. Finally, the expected continuous rock mechanical profile indicates the formation's strength and stability around the wellbore.

Permeability estimation in the Upper Shale Member (USM) is rather complex due to the heterogeneity. Conventional logs do not provide permeability directly, but the core data does. Core operations and analysis processing are all costly. An alternative method is used by dividing the reservoir rock into groups (rock types) with similar characteristics in porosity and permeability for cored wells. These identified groups for the core wells can be applied to the uncored wells to predict their groups and permeabilities. Four wells were used in this study: one cored well and three uncored wells. Two classification methods of rock types were used; the first is the machine learning (ML) method (supervised and unsupervised), a new technics invention, and the second method is the hydraulic flow unit (HFU). Four groups were selected by using ML and HFU methods. A regression equation for each rock type was obtained to predict permeability. Then, establishing a relationship between the results of the core data groups and the logs' responses for the core well is referred to as "correlation," which is training the logs that provide an estimating model. A validation check was completed by comparing the rock type and permeability estimation from the core with the rock type and permeability estimation from the logs, which were highly matched. Finally, The rock type and permeability can be estimated for the uncored wells using only the logs that were learned from the generated training data set for the cored wells. Due to the use of more than one model to predict permeability, it was clear that one was more compatible than the other. The flow zone indicator, a classical method, still has a better rock type and permeability estimation than the new techniques, which use data mining and mathematical algorithms on a clustering basis.

Summary: This study considered the complexity of Mishrif geology and its effect on fluid movement within and across Mishrif reservoir intervals. For this purpose, we analyzed the following items: the multiple interval communication with high permeability contrast, the geological setting of the upper Mishrif (mA) interval, the channel structure in the Lower Mishrif-Part 1 (mB1) interval, the thin layers in the upper part of Lower Mishrif-Part 2 (mB2U) of very high permeability, and the microporous interval of the lower part of Lower Mishrif-Part 2 (mB2L); none of them were well defined before this work. The bottom interval of Mishrif or Rumaila (mC) is predominantly microporous, and the best reservoir is at the top of intermediate quality. Two high-porosity layers are systematically found in the mC unit, which is casually referred to as “rabbit ears.” The mB2L contains grainstones in the far north of the West Qurna/1 oil field (WQ1). In the south of mB2L, some of the toe sets from the clinoforms in a distal depositional setting have developed into rather important vertical pressure baffles and barriers to vertical flow. The mB2U generally consists of grainstones with thin streaks of mudstone high flow layers (HFLs), and the rocks underneath are described generally as grainstone shoals. About 80% of stock tank oil originally in place (STOOIP) in mB2U exists in grainstones. There are no known microporous reservoirs in mB2U. The pressure difference across the boundaries between mB1 and mA can be positive or negative. At the base, mB1 channels are always in pressure communication with the mB2U below. The best flow from the mA comes from HFLs, which are found around faults. Reservoir quality within mA is generally best in the first section of the upper Mishrif (mAa), and the majority of STOOIP in mA exists in microporous rocks, while some 30% of STOOIP is contained in grainstones.

The injection of Low Salinity Water (LSWI) as an Enhanced Oil Recovery (EOR) method has recently attracted a lot of attention. Extensive research has been conducted to investigate and identify the positive effects of LSWI on oil recovery. In order to demonstrate the impact of introducing low salinity water into a reservoir, simulations on the ECLIPSE 100 simulator are being done in this work. To simulate an actual reservoir, an easy static model was made. In order to replicate the effects of injecting low salinity water and normal salinity, or seawater, the reservoir is three-phase with oil, gas, and water. It has one injector and one producer. Five cases were suggested to investigate the effect of low salinity water injection with different concentrations and the period of injection. The low salinity injection period varied from twenty-five years in case one and reduced five years in each case until reached to five years in final case. Higher oil recovery factor obtained in case one with injection time twenty-five years and lower recovery factor for case five with injection time of low salinity water injection five years. Lower water concentration gives higher oil recovery for all cases where this study investigated the effect of low-salinity water flooding as slug injection. From the five cases presented, field oil recovery factor (FOE), field oil production rate (FOPR), field oil production total (FOPT), field pressure (FP), and field water cut (FWCT) were observed. Oil recovery is 56.6 percent in high salinity water flooding (HSWF), and 71.8 percent in low salinity water flooding (LSWF) for 0 percent salt concentration and 62.40 percent for 20 percent salt concentration as in case one.

In this study, the researchers attempted to build models that are more accurate by using lithofacies data along the field. Using a wider field area and extended data from both electrofacies and lithofacies could provide an accurate model of the reservoir description that will be useful for future field developments. The stochastic and deterministic models were presented to build the facies model as the basis for the construction, comparison, and selection of the more accurate model of the Mishrif Formation in the West Qurna oilfield/Iraq. The results obtained from this study indicated that the Sequential Gaussian indicator simulation algorithm offered a more accurate stochastic model between lithofacies data extracted from 20 wells and electrofacies from 75 wells, while the Truncated Gaussian simulation algorithm has a better match than others for the distribution percentage of lithofacies and electrofacies. The stochastic model shows that Mishrif Formation consists of approximately 35–40% as carbonates, 32–35% as shale, and 28–30% as coarse sand, Dolomite, and other materials according to the SISIM and GTSIM, while the deterministic by the kriging algorithm referred to as 62%, 24%, and 14%, respectively. The best variogram that gives the best results indicated that the direction of facies distribution is mainly South-North with an inclination to North-West.

In many oil-recovery systems, relative permeabilities (kr) are essential flow factors that affect fluid dispersion and output from petroleum resources. Traditionally, taking rock samples from the reservoir and performing suitable laboratory studies is required to get these crucial reservoir properties. Despite the fact that kr is a function of fluid saturation, it is now well established that pore shape and distribution, absolute permeability, wettability, interfacial tension (IFT), and saturation history all influence kr values. These rock/fluid characteristics vary greatly from one reservoir region to the next, and it would be impossible to make kr measurements in all of them. The unsteady-state approach was used to calculate the relative permeability of five carbonate for core plugs from the Mishrif formation of WQ1. The relative permeability calculated by using Johnson, Bossler and Naumann (JBN) Correlation, which is, consider one of the unsteady-state approach where it found that the core plugs are water wet. A normalizing approach has been used to remove the effect of irreducible water and residual saturations, which would vary according on the environment. Based on their own irreducible water and trapped saturations, the relative permeabilities can subsequently be de-normalized and assigned to distinct sections (rock types) of the reservoir. The goal of this research is to normalize the relative permeability that was determined through water flooding.